Buggy python submission (verdict: Time Limit Exceeded):
import sys
input = sys.stdin.buffer.readline
from numba import njit
import numpy as np
INF = 10**18

@njit('(i8, i8[:,:])')
def solve(N, XYP):
    X = XYP[:, 0]
    Y = XYP[:, 1]

    def calcCost(sign, A):
        ret = np.ones((1 << N, N), dtype=np.int64) * INF
        for state in range(1 << N):
            useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
            for i in range(N):
                xyp = XYP[i]
                cost = abs(xyp[sign])
                for z in useA:
                    cost = min(cost, abs(xyp[sign] - z))
                ret[state][i] = cost * xyp[2]
        return ret

    costX = calcCost(0, X)
    costY = calcCost(1, Y)
    ans = np.ones(N + 1, dtype=np.int64) * INF

    for state in range(pow(3, N)):
        stateX = 0
        stateY = 0
        M = N
        for _ in range(N):
            state, r = divmod(state, 3)
            stateX <<= 1
            stateY <<= 1
            if r == 0: M -= 1
            if r == 1: stateX |= 1
            if r == 2: stateY |= 1

        cx, cy = costX[stateX], costY[stateY]
        cost = np.minimum(cx, cy).sum()
        ans[M] = min(ans[M], cost)

    for a in ans:
        print(a)

def sol():
    N = int(input())
    XYP = np.asarray([list(map(int, input().split())) for _ in range(N)])
    solve(N, XYP)

sol()


Fixed submission:
import sys
input = sys.stdin.buffer.readline
from numba import njit
import numpy as np
INF = 10**18

@njit('(i8, i8[:,:])')
def solve(N, XYP):
    X = XYP[:, 0]
    Y = XYP[:, 1]

    def calcCost(sign, A):
        ret = np.ones((1 << N, N), dtype=np.int64) * INF
        for state in range(1 << N):
            useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
            for i in range(N):
                xyp = XYP[i]
                cost = abs(xyp[sign])
                for z in useA:
                    cost = min(cost, abs(xyp[sign] - z))
                ret[state][i] = cost * xyp[2]
        return ret

    costX = calcCost(0, X)
    costY = calcCost(1, Y)
    ans = np.ones(N + 1, dtype=np.int64) * INF

    for state in range(pow(3, N)):
        stateX = 0
        stateY = 0
        M = N
        for _ in range(N):
            state, r = divmod(state, 3)
            stateX <<= 1
            stateY <<= 1
            if r == 0: M -= 1
            if r == 1: stateX |= 1
            if r == 2: stateY |= 1

        cx, cy = costX[stateX], costY[stateY]
        cost = 0
        for i in range(N):
            cost += min(cx[i], cy[i])
        ans[M] = min(ans[M], cost)

    for a in ans:
        print(a)

def sol():
    N = int(input())
    XYP = np.asarray([list(map(int, input().split())) for _ in range(N)])
    solve(N, XYP)

sol()

Buggy python submission (verdict: Time Limit Exceeded):
import sys
input = sys.stdin.buffer.readline
from numba import njit
import numpy as np
INF = 10**18

@njit('(i8, i8[:,:], i8[:,:])')
def solve(N, costX, costY):
    ans = np.ones(N + 1, dtype=np.int64) * INF

    for state in range(pow(3, N)):
        stateX = 0
        stateY = 0
        M = N
        for _ in range(N):
            state, r = divmod(state, 3)
            stateX <<= 1
            stateY <<= 1
            if r == 0: M -= 1
            if r == 1: stateX |= 1
            if r == 2: stateY |= 1

        cx, cy = costX[stateX], costY[stateY]
        cost = np.minimum(cx, cy).sum()
        ans[M] = min(ans[M], cost)

    for a in ans:
        print(a)

def sol():
    N = int(input())
    XYP = [tuple(map(int, input().split())) for _ in range(N)]
    XYP.sort(key=lambda xyp: xyp[2], reverse=False)

    X = [x for x, _, _ in XYP]
    Y = [y for _, y, _ in XYP]

    def calcCost(AP, A):
        ret = np.ones((1 << N, N), dtype=np.int64) * INF
        for state in range(1 << N):
            useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
            for i, (a, p) in enumerate(AP):
                cost = abs(a)
                for z in useA:
                    cost = min(cost, abs(a - z))
                ret[state][i] = cost * p
        return ret

    costX = calcCost([(x, p) for x, _, p in XYP], X)
    costY = calcCost([(y, p) for _, y, p in XYP], Y)

    solve(N, costX, costY)

sol()


Fixed submission:
import sys
input = sys.stdin.buffer.readline
from numba import njit
import numpy as np
INF = 10**18

@njit('(i8, i8[:,:])')
def solve(N, XYP):
    X = XYP[:, 0]
    Y = XYP[:, 1]

    def calcCost(sign, A):
        ret = np.ones((1 << N, N), dtype=np.int64) * INF
        for state in range(1 << N):
            useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
            for i in range(N):
                xyp = XYP[i]
                cost = abs(xyp[sign])
                for z in useA:
                    cost = min(cost, abs(xyp[sign] - z))
                ret[state][i] = cost * xyp[2]
        return ret

    costX = calcCost(0, X)
    costY = calcCost(1, Y)
    ans = np.ones(N + 1, dtype=np.int64) * INF

    for state in range(pow(3, N)):
        stateX = 0
        stateY = 0
        M = N
        for _ in range(N):
            state, r = divmod(state, 3)
            stateX <<= 1
            stateY <<= 1
            if r == 0: M -= 1
            if r == 1: stateX |= 1
            if r == 2: stateY |= 1

        cx, cy = costX[stateX], costY[stateY]
        cost = 0
        for i in range(N):
            cost += min(cx[i], cy[i])
        ans[M] = min(ans[M], cost)

    for a in ans:
        print(a)

def sol():
    N = int(input())
    XYP = np.asarray([list(map(int, input().split())) for _ in range(N)])
    solve(N, XYP)

sol()

Buggy python submission (verdict: Time Limit Exceeded):
import sys
input = sys.stdin.buffer.readline
from numba import njit
import numpy as np
INF = 10**18

@njit
def solve(N, costX, costY):
    ans = np.ones(N + 1, dtype=np.int64) * INF

    for state in range(pow(3, N)):
        stateX = 0
        stateY = 0
        M = N
        for _ in range(N):
            state, r = divmod(state, 3)
            stateX <<= 1
            stateY <<= 1
            if r == 0: M -= 1
            if r == 1: stateX |= 1
            if r == 2: stateY |= 1

        cx, cy = costX[stateX], costY[stateY]
        cost = np.minimum(cx, cy).sum()
        ans[M] = min(ans[M], cost)

    return ans

def sol():
    N = int(input())
    XYP = [tuple(map(int, input().split())) for _ in range(N)]
    XYP.sort(key=lambda xyp: xyp[2], reverse=False)

    X = [x for x, _, _ in XYP]
    Y = [y for _, y, _ in XYP]

    def calcCost(AP, A):
        ret = np.ones((1 << N, N), dtype=np.int64) * INF
        for state in range(1 << N):
            useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
            for i, (a, p) in enumerate(AP):
                cost = abs(a)
                for z in useA:
                    cost = min(cost, abs(a - z))
                ret[state][i] = cost * p
        return ret

    costX = calcCost([(x, p) for x, _, p in XYP], X)
    costY = calcCost([(y, p) for _, y, p in XYP], Y)

    ans = solve(N, costX, costY)
    print(*ans, sep='\n')

sol()


Fixed submission:
import sys
input = sys.stdin.buffer.readline
from numba import njit
import numpy as np
INF = 10**18

@njit('(i8, i8[:,:])')
def solve(N, XYP):
    X = XYP[:, 0]
    Y = XYP[:, 1]

    def calcCost(sign, A):
        ret = np.ones((1 << N, N), dtype=np.int64) * INF
        for state in range(1 << N):
            useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
            for i in range(N):
                xyp = XYP[i]
                cost = abs(xyp[sign])
                for z in useA:
                    cost = min(cost, abs(xyp[sign] - z))
                ret[state][i] = cost * xyp[2]
        return ret

    costX = calcCost(0, X)
    costY = calcCost(1, Y)
    ans = np.ones(N + 1, dtype=np.int64) * INF

    for state in range(pow(3, N)):
        stateX = 0
        stateY = 0
        M = N
        for _ in range(N):
            state, r = divmod(state, 3)
            stateX <<= 1
            stateY <<= 1
            if r == 0: M -= 1
            if r == 1: stateX |= 1
            if r == 2: stateY |= 1

        cx, cy = costX[stateX], costY[stateY]
        cost = 0
        for i in range(N):
            cost += min(cx[i], cy[i])
        ans[M] = min(ans[M], cost)

    for a in ans:
        print(a)

def sol():
    N = int(input())
    XYP = np.asarray([list(map(int, input().split())) for _ in range(N)])
    solve(N, XYP)

sol()

Buggy python submission (verdict: Time Limit Exceeded):
import sys
input = sys.stdin.buffer.readline
from numba import njit
import numpy as np
INF = 10**18

@njit(cache=True)
def solve(N, costX, costY):
    ans = [INF] * (N + 1)

    for state in range(pow(3, N)):
        stateX = 0
        stateY = 0
        M = N
        for _ in range(N):
            state, r = divmod(state, 3)
            stateX <<= 1
            stateY <<= 1
            if r == 0: M -= 1
            if r == 1: stateX |= 1
            if r == 2: stateY |= 1

        cx, cy = costX[stateX], costY[stateY]
        cost = np.minimum(cx, cy).sum()
        ans[M] = min(ans[M], cost)

    return ans

def sol():
    N = int(input())
    XYP = [tuple(map(int, input().split())) for _ in range(N)]
    XYP.sort(key=lambda xyp: xyp[2], reverse=False)

    X = [x for x, _, _ in XYP]
    Y = [y for _, y, _ in XYP]

    def calcCost(AP, A):
        ret = np.ones((1 << N, N), dtype=np.int64) * INF
        for state in range(1 << N):
            useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
            for i, (a, p) in enumerate(AP):
                cost = abs(a)
                for z in useA:
                    cost = min(cost, abs(a - z))
                ret[state][i] = cost * p
        return ret

    costX = calcCost([(x, p) for x, _, p in XYP], X)
    costY = calcCost([(y, p) for _, y, p in XYP], Y)

    ans = solve(N, costX, costY)
    print(*ans, sep='\n')

sol()


Fixed submission:
import sys
input = sys.stdin.buffer.readline
from numba import njit
import numpy as np
INF = 10**18

@njit('(i8, i8[:,:])')
def solve(N, XYP):
    X = XYP[:, 0]
    Y = XYP[:, 1]

    def calcCost(sign, A):
        ret = np.ones((1 << N, N), dtype=np.int64) * INF
        for state in range(1 << N):
            useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
            for i in range(N):
                xyp = XYP[i]
                cost = abs(xyp[sign])
                for z in useA:
                    cost = min(cost, abs(xyp[sign] - z))
                ret[state][i] = cost * xyp[2]
        return ret

    costX = calcCost(0, X)
    costY = calcCost(1, Y)
    ans = np.ones(N + 1, dtype=np.int64) * INF

    for state in range(pow(3, N)):
        stateX = 0
        stateY = 0
        M = N
        for _ in range(N):
            state, r = divmod(state, 3)
            stateX <<= 1
            stateY <<= 1
            if r == 0: M -= 1
            if r == 1: stateX |= 1
            if r == 2: stateY |= 1

        cx, cy = costX[stateX], costY[stateY]
        cost = 0
        for i in range(N):
            cost += min(cx[i], cy[i])
        ans[M] = min(ans[M], cost)

    for a in ans:
        print(a)

def sol():
    N = int(input())
    XYP = np.asarray([list(map(int, input().split())) for _ in range(N)])
    solve(N, XYP)

sol()

Buggy python submission (verdict: Time Limit Exceeded):
import sys
input = sys.stdin.buffer.readline
from numba import njit
import numpy as np
INF = 10**18

# @njit
def solve(N, costX, costY):
    ans = [INF] * (N + 1)

    for state in range(pow(3, N)):
        stateX = 0
        stateY = 0
        M = N
        for _ in range(N):
            state, r = divmod(state, 3)
            stateX <<= 1
            stateY <<= 1
            if r == 0: M -= 1
            if r == 1: stateX |= 1
            if r == 2: stateY |= 1

        cx, cy = costX[stateX], costY[stateY]
        cost = np.minimum(cx, cy).sum()
        ans[M] = min(ans[M], cost)

    return ans

def sol():
    N = int(input())
    XYP = [tuple(map(int, input().split())) for _ in range(N)]
    XYP.sort(key=lambda xyp: xyp[2], reverse=False)

    X = [x for x, _, _ in XYP]
    Y = [y for _, y, _ in XYP]

    def calcCost(AP, A):
        ret = np.ones((1 << N, N), dtype=np.int64) * INF
        for state in range(1 << N):
            useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
            for i, (a, p) in enumerate(AP):
                cost = abs(a)
                for z in useA:
                    cost = min(cost, abs(a - z))
                ret[state][i] = cost * p
        return ret

    costX = calcCost([(x, p) for x, _, p in XYP], X)
    costY = calcCost([(y, p) for _, y, p in XYP], Y)

    ans = solve(N, costX, costY)
    print(*ans, sep='\n')

sol()


Fixed submission:
import sys
input = sys.stdin.buffer.readline
from numba import njit
import numpy as np
INF = 10**18

@njit('(i8, i8[:,:])')
def solve(N, XYP):
    X = XYP[:, 0]
    Y = XYP[:, 1]

    def calcCost(sign, A):
        ret = np.ones((1 << N, N), dtype=np.int64) * INF
        for state in range(1 << N):
            useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
            for i in range(N):
                xyp = XYP[i]
                cost = abs(xyp[sign])
                for z in useA:
                    cost = min(cost, abs(xyp[sign] - z))
                ret[state][i] = cost * xyp[2]
        return ret

    costX = calcCost(0, X)
    costY = calcCost(1, Y)
    ans = np.ones(N + 1, dtype=np.int64) * INF

    for state in range(pow(3, N)):
        stateX = 0
        stateY = 0
        M = N
        for _ in range(N):
            state, r = divmod(state, 3)
            stateX <<= 1
            stateY <<= 1
            if r == 0: M -= 1
            if r == 1: stateX |= 1
            if r == 2: stateY |= 1

        cx, cy = costX[stateX], costY[stateY]
        cost = 0
        for i in range(N):
            cost += min(cx[i], cy[i])
        ans[M] = min(ans[M], cost)

    for a in ans:
        print(a)

def sol():
    N = int(input())
    XYP = np.asarray([list(map(int, input().split())) for _ in range(N)])
    solve(N, XYP)

sol()

Buggy python submission (verdict: Time Limit Exceeded):
import sys
input = sys.stdin.buffer.readline
from numba import njit
import numpy as np
INF = 10**18

@njit
def solve(N, costX, costY):
    ans = [INF] * (N + 1)

    for state in range(pow(3, N)):
        stateX = 0
        stateY = 0
        M = N
        for _ in range(N):
            state, r = divmod(state, 3)
            stateX <<= 1
            stateY <<= 1
            if r == 0: M -= 1
            if r == 1: stateX |= 1
            if r == 2: stateY |= 1

        cx, cy = costX[stateX], costY[stateY]
        cost = np.minimum(cx, cy).sum()
        ans[M] = min(ans[M], cost)

    return ans

def sol():
    N = int(input())
    XYP = [tuple(map(int, input().split())) for _ in range(N)]
    XYP.sort(key=lambda xyp: xyp[2], reverse=False)

    X = [x for x, _, _ in XYP]
    Y = [y for _, y, _ in XYP]

    def calcCost(AP, A):
        ret = np.ones((1 << N, N), dtype=np.int64) * INF
        for state in range(1 << N):
            useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
            for i, (a, p) in enumerate(AP):
                cost = abs(a)
                for z in useA:
                    cost = min(cost, abs(a - z))
                ret[state][i] = cost * p
        return ret

    costX = calcCost([(x, p) for x, _, p in XYP], X)
    costY = calcCost([(y, p) for _, y, p in XYP], Y)

    ans = solve(N, costX, costY)
    print(*ans, sep='\n')

sol()


Fixed submission:
import sys
input = sys.stdin.buffer.readline
from numba import njit
import numpy as np
INF = 10**18

@njit('(i8, i8[:,:])')
def solve(N, XYP):
    X = XYP[:, 0]
    Y = XYP[:, 1]

    def calcCost(sign, A):
        ret = np.ones((1 << N, N), dtype=np.int64) * INF
        for state in range(1 << N):
            useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
            for i in range(N):
                xyp = XYP[i]
                cost = abs(xyp[sign])
                for z in useA:
                    cost = min(cost, abs(xyp[sign] - z))
                ret[state][i] = cost * xyp[2]
        return ret

    costX = calcCost(0, X)
    costY = calcCost(1, Y)
    ans = np.ones(N + 1, dtype=np.int64) * INF

    for state in range(pow(3, N)):
        stateX = 0
        stateY = 0
        M = N
        for _ in range(N):
            state, r = divmod(state, 3)
            stateX <<= 1
            stateY <<= 1
            if r == 0: M -= 1
            if r == 1: stateX |= 1
            if r == 2: stateY |= 1

        cx, cy = costX[stateX], costY[stateY]
        cost = 0
        for i in range(N):
            cost += min(cx[i], cy[i])
        ans[M] = min(ans[M], cost)

    for a in ans:
        print(a)

def sol():
    N = int(input())
    XYP = np.asarray([list(map(int, input().split())) for _ in range(N)])
    solve(N, XYP)

sol()

Buggy python submission (verdict: Time Limit Exceeded):
import sys
input = sys.stdin.buffer.readline
from numba import njit
import numpy as np
INF = 10**18

@njit
def solve(N, costX, costY):
    ans = [INF] * (N + 1)

    for state in range(pow(3, N)):
        stateX = 0
        stateY = 0
        M = N
        for _ in range(N):
            state, r = divmod(state, 3)
            stateX <<= 1
            stateY <<= 1
            if r == 0: M -= 1
            if r == 1: stateX |= 1
            if r == 2: stateY |= 1

        cx, cy = costX[stateX], costY[stateY]
        cost = 0
        for i in range(N):
            cost += min(cx[i], cy[i])
            if cost >= ans[M]:
                break
        else:
            ans[M] = cost

    return ans

def sol():
    N = int(input())
    XYP = [tuple(map(int, input().split())) for _ in range(N)]
    XYP.sort(key=lambda xyp: xyp[2], reverse=False)

    X = [x for x, _, _ in XYP]
    Y = [y for _, y, _ in XYP]

    def calcCost(AP, A):
        ret = np.ones((1 << N, N), dtype=np.int64) * INF
        for state in range(1 << N):
            useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
            for i, (a, p) in enumerate(AP):
                cost = abs(a)
                for z in useA:
                    cost = min(cost, abs(a - z))
                ret[state][i] = cost * p
        return ret

    costX = calcCost([(x, p) for x, _, p in XYP], X)
    costY = calcCost([(y, p) for _, y, p in XYP], Y)

    ans = solve(N, costX, costY)
    print(*ans, sep='\n')

sol()


Fixed submission:
import sys
input = sys.stdin.buffer.readline
from numba import njit
import numpy as np
INF = 10**18

@njit('(i8, i8[:,:])')
def solve(N, XYP):
    X = XYP[:, 0]
    Y = XYP[:, 1]

    def calcCost(sign, A):
        ret = np.ones((1 << N, N), dtype=np.int64) * INF
        for state in range(1 << N):
            useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
            for i in range(N):
                xyp = XYP[i]
                cost = abs(xyp[sign])
                for z in useA:
                    cost = min(cost, abs(xyp[sign] - z))
                ret[state][i] = cost * xyp[2]
        return ret

    costX = calcCost(0, X)
    costY = calcCost(1, Y)
    ans = np.ones(N + 1, dtype=np.int64) * INF

    for state in range(pow(3, N)):
        stateX = 0
        stateY = 0
        M = N
        for _ in range(N):
            state, r = divmod(state, 3)
            stateX <<= 1
            stateY <<= 1
            if r == 0: M -= 1
            if r == 1: stateX |= 1
            if r == 2: stateY |= 1

        cx, cy = costX[stateX], costY[stateY]
        cost = 0
        for i in range(N):
            cost += min(cx[i], cy[i])
        ans[M] = min(ans[M], cost)

    for a in ans:
        print(a)

def sol():
    N = int(input())
    XYP = np.asarray([list(map(int, input().split())) for _ in range(N)])
    solve(N, XYP)

sol()

Buggy python submission (verdict: Time Limit Exceeded):
import sys
input = sys.stdin.buffer.readline
from itertools import product
from random import sample
INF = 10**18

def sol():
    N = int(input())
    XYP = [tuple(map(int, input().split())) for _ in range(N)]
    XYP.sort(key=lambda xyp: xyp[2], reverse=False)

    X = [x for x, _, _ in XYP]
    Y = [y for _, y, _ in XYP]

    def calcCost(AP, A):
        ret = [[INF] * N for _ in range(1 << N)]
        for state in range(1 << N):
            useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
            for i, (a, p) in enumerate(AP):
                cost = abs(a)
                for z in useA:
                    cost = min(cost, abs(a - z))
                ret[state][i] = cost * p
        return ret

    costX = calcCost([(x, p) for x, _, p in XYP], X)
    costY = calcCost([(y, p) for _, y, p in XYP], Y)

    ans = [INF] * (N + 1)
    for state in product((0, 1, 2), repeat=N):
        stateX = 0
        stateY = 0
        M = N
        for s in state:
            stateX <<= 1
            stateY <<= 1
            if s == 0: M -= 1
            if s == 1: stateX |= 1
            if s == 2: stateY |= 1

        cx, cy = costX[stateX], costY[stateY]
        cost = 0
        for i in sample(range(N), k=N):
            cost += min(cx[i], cy[i])
            if cost >= ans[M]:
                break
        else:
            ans[M] = cost

    print(*ans, sep='\n')

sol()


Fixed submission:
import sys
input = sys.stdin.buffer.readline
from numba import njit
import numpy as np
INF = 10**18

@njit('(i8, i8[:,:])')
def solve(N, XYP):
    X = XYP[:, 0]
    Y = XYP[:, 1]

    def calcCost(sign, A):
        ret = np.ones((1 << N, N), dtype=np.int64) * INF
        for state in range(1 << N):
            useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
            for i in range(N):
                xyp = XYP[i]
                cost = abs(xyp[sign])
                for z in useA:
                    cost = min(cost, abs(xyp[sign] - z))
                ret[state][i] = cost * xyp[2]
        return ret

    costX = calcCost(0, X)
    costY = calcCost(1, Y)
    ans = np.ones(N + 1, dtype=np.int64) * INF

    for state in range(pow(3, N)):
        stateX = 0
        stateY = 0
        M = N
        for _ in range(N):
            state, r = divmod(state, 3)
            stateX <<= 1
            stateY <<= 1
            if r == 0: M -= 1
            if r == 1: stateX |= 1
            if r == 2: stateY |= 1

        cx, cy = costX[stateX], costY[stateY]
        cost = 0
        for i in range(N):
            cost += min(cx[i], cy[i])
        ans[M] = min(ans[M], cost)

    for a in ans:
        print(a)

def sol():
    N = int(input())
    XYP = np.asarray([list(map(int, input().split())) for _ in range(N)])
    solve(N, XYP)

sol()

Buggy python submission (verdict: Time Limit Exceeded):
import sys
input = sys.stdin.buffer.readline
from itertools import product
INF = 10**18

def sol():
    N = int(input())
    XYP = [tuple(map(int, input().split())) for _ in range(N)]
    XYP.sort(key=lambda xyp: max(abs(xyp[0]), abs(xyp[1])) * xyp[2], reverse=False)

    X = [x for x, _, _ in XYP]
    Y = [y for _, y, _ in XYP]

    def calcCost(AP, A):
        ret = [[INF] * N for _ in range(1 << N)]
        for state in range(1 << N):
            useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
            for i, (a, p) in enumerate(AP):
                cost = abs(a)
                for z in useA:
                    cost = min(cost, abs(a - z))
                ret[state][i] = cost * p
        return ret

    costX = calcCost([(x, p) for x, _, p in XYP], X)
    costY = calcCost([(y, p) for _, y, p in XYP], Y)

    ans = [INF] * (N + 1)
    for state in product((0, 1, 2), repeat=N):
        stateX = 0
        stateY = 0
        M = N
        for s in state:
            stateX <<= 1
            stateY <<= 1
            if s == 0: M -= 1
            if s == 1: stateX |= 1
            if s == 2: stateY |= 1

        cx, cy = costX[stateX], costY[stateY]
        cost = 0
        for i in range(N):
            cost += min(cx[i], cy[i])
            if cost >= ans[M]:
                break
        else:
            ans[M] = cost

    print(*ans, sep='\n')

sol()


Fixed submission:
import sys
input = sys.stdin.buffer.readline
from numba import njit
import numpy as np
INF = 10**18

@njit('(i8, i8[:,:])')
def solve(N, XYP):
    X = XYP[:, 0]
    Y = XYP[:, 1]

    def calcCost(sign, A):
        ret = np.ones((1 << N, N), dtype=np.int64) * INF
        for state in range(1 << N):
            useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
            for i in range(N):
                xyp = XYP[i]
                cost = abs(xyp[sign])
                for z in useA:
                    cost = min(cost, abs(xyp[sign] - z))
                ret[state][i] = cost * xyp[2]
        return ret

    costX = calcCost(0, X)
    costY = calcCost(1, Y)
    ans = np.ones(N + 1, dtype=np.int64) * INF

    for state in range(pow(3, N)):
        stateX = 0
        stateY = 0
        M = N
        for _ in range(N):
            state, r = divmod(state, 3)
            stateX <<= 1
            stateY <<= 1
            if r == 0: M -= 1
            if r == 1: stateX |= 1
            if r == 2: stateY |= 1

        cx, cy = costX[stateX], costY[stateY]
        cost = 0
        for i in range(N):
            cost += min(cx[i], cy[i])
        ans[M] = min(ans[M], cost)

    for a in ans:
        print(a)

def sol():
    N = int(input())
    XYP = np.asarray([list(map(int, input().split())) for _ in range(N)])
    solve(N, XYP)

sol()

Buggy python submission (verdict: Time Limit Exceeded):
import sys
input = sys.stdin.buffer.readline
from itertools import product
INF = 10**18

def sol():
    N = int(input())
    XYP = [tuple(map(int, input().split())) for _ in range(N)]
    XYP.sort(key=lambda xyp: max(abs(xyp[0]), abs(xyp[1])) * xyp[2], reverse=True)

    X = [x for x, _, _ in XYP]
    Y = [y for _, y, _ in XYP]

    def calcCost(AP, A):
        ret = [[INF] * N for _ in range(1 << N)]
        for state in range(1 << N):
            useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
            for i, (a, p) in enumerate(AP):
                cost = abs(a)
                for z in useA:
                    cost = min(cost, abs(a - z))
                ret[state][i] = cost * p
        return ret

    costX = calcCost([(x, p) for x, _, p in XYP], X)
    costY = calcCost([(y, p) for _, y, p in XYP], Y)

    ans = [INF] * (N + 1)
    for state in product((0, 1, 2), repeat=N):
        stateX = 0
        stateY = 0
        M = N
        for s in state:
            stateX <<= 1
            stateY <<= 1
            if s == 0: M -= 1
            if s == 1: stateX |= 1
            if s == 2: stateY |= 1

        cx, cy = costX[stateX], costY[stateY]
        cost = 0
        for i in range(N):
            cost += min(cx[i], cy[i])
            if cost >= ans[M]:
                break
        else:
            ans[M] = cost

    print(*ans, sep='\n')

sol()


Fixed submission:
import sys
input = sys.stdin.buffer.readline
from numba import njit
import numpy as np
INF = 10**18

@njit('(i8, i8[:,:])')
def solve(N, XYP):
    X = XYP[:, 0]
    Y = XYP[:, 1]

    def calcCost(sign, A):
        ret = np.ones((1 << N, N), dtype=np.int64) * INF
        for state in range(1 << N):
            useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
            for i in range(N):
                xyp = XYP[i]
                cost = abs(xyp[sign])
                for z in useA:
                    cost = min(cost, abs(xyp[sign] - z))
                ret[state][i] = cost * xyp[2]
        return ret

    costX = calcCost(0, X)
    costY = calcCost(1, Y)
    ans = np.ones(N + 1, dtype=np.int64) * INF

    for state in range(pow(3, N)):
        stateX = 0
        stateY = 0
        M = N
        for _ in range(N):
            state, r = divmod(state, 3)
            stateX <<= 1
            stateY <<= 1
            if r == 0: M -= 1
            if r == 1: stateX |= 1
            if r == 2: stateY |= 1

        cx, cy = costX[stateX], costY[stateY]
        cost = 0
        for i in range(N):
            cost += min(cx[i], cy[i])
        ans[M] = min(ans[M], cost)

    for a in ans:
        print(a)

def sol():
    N = int(input())
    XYP = np.asarray([list(map(int, input().split())) for _ in range(N)])
    solve(N, XYP)

sol()

Buggy python submission (verdict: Runtime Error):
import sys
input = sys.stdin.buffer.readline
from itertools import product
INF = 10**18

def sol():
    N = int(input())
    XYP = [tuple(map(int, input().split())) for _ in range(N)]
    XYP.sort(key=lambda xyp: max(abs(xyp[0], xyp[1])) * xyp[2], reverse=True)

    X = [x for x, _, _ in XYP]
    Y = [y for _, y, _ in XYP]

    def calcCost(AP, A):
        ret = [[INF] * N for _ in range(1 << N)]
        for state in range(1 << N):
            useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
            for i, (a, p) in enumerate(AP):
                cost = abs(a)
                for z in useA:
                    cost = min(cost, abs(a - z))
                ret[state][i] = cost * p
        return ret

    costX = calcCost([(x, p) for x, _, p in XYP], X)
    costY = calcCost([(y, p) for _, y, p in XYP], Y)

    ans = [INF] * (N + 1)
    for state in product((0, 1, 2), repeat=N):
        stateX = 0
        stateY = 0
        M = N
        for s in state:
            stateX <<= 1
            stateY <<= 1
            if s == 0: M -= 1
            if s == 1: stateX |= 1
            if s == 2: stateY |= 1

        cx, cy = costX[stateX], costY[stateY]
        cost = 0
        for i in range(N):
            cost += min(cx[i], cy[i])
            if cost >= ans[M]:
                break
        else:
            ans[M] = cost

    print(*ans, sep='\n')

sol()


Fixed submission:
import sys
input = sys.stdin.buffer.readline
from numba import njit
import numpy as np
INF = 10**18

@njit('(i8, i8[:,:])')
def solve(N, XYP):
    X = XYP[:, 0]
    Y = XYP[:, 1]

    def calcCost(sign, A):
        ret = np.ones((1 << N, N), dtype=np.int64) * INF
        for state in range(1 << N):
            useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
            for i in range(N):
                xyp = XYP[i]
                cost = abs(xyp[sign])
                for z in useA:
                    cost = min(cost, abs(xyp[sign] - z))
                ret[state][i] = cost * xyp[2]
        return ret

    costX = calcCost(0, X)
    costY = calcCost(1, Y)
    ans = np.ones(N + 1, dtype=np.int64) * INF

    for state in range(pow(3, N)):
        stateX = 0
        stateY = 0
        M = N
        for _ in range(N):
            state, r = divmod(state, 3)
            stateX <<= 1
            stateY <<= 1
            if r == 0: M -= 1
            if r == 1: stateX |= 1
            if r == 2: stateY |= 1

        cx, cy = costX[stateX], costY[stateY]
        cost = 0
        for i in range(N):
            cost += min(cx[i], cy[i])
        ans[M] = min(ans[M], cost)

    for a in ans:
        print(a)

def sol():
    N = int(input())
    XYP = np.asarray([list(map(int, input().split())) for _ in range(N)])
    solve(N, XYP)

sol()

Buggy python submission (verdict: Time Limit Exceeded):
import sys
input = sys.stdin.buffer.readline
from itertools import product
INF = 10**18

def sol():
    N = int(input())
    XYP = [tuple(map(int, input().split())) for _ in range(N)]
    XYP.sort(key=lambda xyp: xyp[2], reverse=False)

    X = [x for x, _, _ in XYP]
    Y = [y for _, y, _ in XYP]

    def calcCost(AP, A):
        ret = [[INF] * N for _ in range(1 << N)]
        for state in range(1 << N):
            useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
            for i, (a, p) in enumerate(AP):
                cost = abs(a)
                for z in useA:
                    cost = min(cost, abs(a - z))
                ret[state][i] = cost * p
        return ret

    costX = calcCost([(x, p) for x, _, p in XYP], X)
    costY = calcCost([(y, p) for _, y, p in XYP], Y)

    ans = [INF] * (N + 1)
    for state in product((0, 1, 2), repeat=N):
        stateX = 0
        stateY = 0
        M = N
        for s in state:
            stateX <<= 1
            stateY <<= 1
            if s == 0: M -= 1
            if s == 1: stateX |= 1
            if s == 2: stateY |= 1

        cx, cy = costX[stateX], costY[stateY]
        cost = 0
        for i in range(N):
            cost += min(cx[i], cy[i])
            if cost >= ans[M]:
                break
        else:
            ans[M] = cost

    print(*ans, sep='\n')

sol()


Fixed submission:
import sys
input = sys.stdin.buffer.readline
from numba import njit
import numpy as np
INF = 10**18

@njit('(i8, i8[:,:])')
def solve(N, XYP):
    X = XYP[:, 0]
    Y = XYP[:, 1]

    def calcCost(sign, A):
        ret = np.ones((1 << N, N), dtype=np.int64) * INF
        for state in range(1 << N):
            useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
            for i in range(N):
                xyp = XYP[i]
                cost = abs(xyp[sign])
                for z in useA:
                    cost = min(cost, abs(xyp[sign] - z))
                ret[state][i] = cost * xyp[2]
        return ret

    costX = calcCost(0, X)
    costY = calcCost(1, Y)
    ans = np.ones(N + 1, dtype=np.int64) * INF

    for state in range(pow(3, N)):
        stateX = 0
        stateY = 0
        M = N
        for _ in range(N):
            state, r = divmod(state, 3)
            stateX <<= 1
            stateY <<= 1
            if r == 0: M -= 1
            if r == 1: stateX |= 1
            if r == 2: stateY |= 1

        cx, cy = costX[stateX], costY[stateY]
        cost = 0
        for i in range(N):
            cost += min(cx[i], cy[i])
        ans[M] = min(ans[M], cost)

    for a in ans:
        print(a)

def sol():
    N = int(input())
    XYP = np.asarray([list(map(int, input().split())) for _ in range(N)])
    solve(N, XYP)

sol()

Buggy python submission (verdict: Time Limit Exceeded):
import sys
input = sys.stdin.buffer.readline
from itertools import product
INF = 10**18

def sol():
    N = int(input())
    XYP = [tuple(map(int, input().split())) for _ in range(N)]
    XYP.sort(key=lambda xyp: xyp[2], reverse=True)

    X = [x for x, _, _ in XYP]
    Y = [y for _, y, _ in XYP]

    def calcCost(AP, A):
        ret = [[INF] * N for _ in range(1 << N)]
        for state in range(1 << N):
            useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
            for i, (a, p) in enumerate(AP):
                cost = abs(a)
                for z in useA:
                    cost = min(cost, abs(a - z))
                ret[state][i] = cost * p
        return ret

    costX = calcCost([(x, p) for x, _, p in XYP], X)
    costY = calcCost([(y, p) for _, y, p in XYP], Y)

    ans = [INF] * (N + 1)
    for state in product((0, 1, 2), repeat=N):
        stateX = 0
        stateY = 0
        M = N
        for s in state:
            stateX <<= 1
            stateY <<= 1
            if s == 0: M -= 1
            if s == 1: stateX |= 1
            if s == 2: stateY |= 1

        cx, cy = costX[stateX], costY[stateY]
        cost = 0
        for i in range(N):
            cost += min(cx[i], cy[i])
            if cost >= ans[M]:
                break
        else:
            ans[M] = cost

    print(*ans, sep='\n')

sol()


Fixed submission:
import sys
input = sys.stdin.buffer.readline
from numba import njit
import numpy as np
INF = 10**18

@njit('(i8, i8[:,:])')
def solve(N, XYP):
    X = XYP[:, 0]
    Y = XYP[:, 1]

    def calcCost(sign, A):
        ret = np.ones((1 << N, N), dtype=np.int64) * INF
        for state in range(1 << N):
            useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
            for i in range(N):
                xyp = XYP[i]
                cost = abs(xyp[sign])
                for z in useA:
                    cost = min(cost, abs(xyp[sign] - z))
                ret[state][i] = cost * xyp[2]
        return ret

    costX = calcCost(0, X)
    costY = calcCost(1, Y)
    ans = np.ones(N + 1, dtype=np.int64) * INF

    for state in range(pow(3, N)):
        stateX = 0
        stateY = 0
        M = N
        for _ in range(N):
            state, r = divmod(state, 3)
            stateX <<= 1
            stateY <<= 1
            if r == 0: M -= 1
            if r == 1: stateX |= 1
            if r == 2: stateY |= 1

        cx, cy = costX[stateX], costY[stateY]
        cost = 0
        for i in range(N):
            cost += min(cx[i], cy[i])
        ans[M] = min(ans[M], cost)

    for a in ans:
        print(a)

def sol():
    N = int(input())
    XYP = np.asarray([list(map(int, input().split())) for _ in range(N)])
    solve(N, XYP)

sol()

Buggy python submission (verdict: Time Limit Exceeded):
from itertools import product
INF = 10**18

def sol():
    N = int(input())
    XYP = [tuple(map(int, input().split())) for _ in range(N)]
    XYP.sort(key=lambda xyp: xyp[2], reverse=True)

    X = [x for x, _, _ in XYP]
    Y = [y for _, y, _ in XYP]

    def calcCost(AP, A):
        ret = [[INF] * N for _ in range(1 << N)]
        for state in range(1 << N):
            useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
            for i, (a, p) in enumerate(AP):
                cost = abs(a)
                for z in useA:
                    cost = min(cost, abs(a - z))
                ret[state][i] = cost * p
        return ret

    costX = calcCost([(x, p) for x, _, p in XYP], X)
    costY = calcCost([(y, p) for _, y, p in XYP], Y)

    ans = [INF] * (N + 1)
    for state in product((0, 1, 2), repeat=N):
        stateX = 0
        stateY = 0
        M = N
        for s in state:
            stateX <<= 1
            stateY <<= 1
            if s == 0: M -= 1
            if s == 1: stateX |= 1
            if s == 2: stateY |= 1

        cx, cy = costX[stateX], costY[stateY]
        cost = 0
        for i in range(N):
            cost += min(cx[i], cy[i])
            if cost >= ans[M]:
                break
        else:
            ans[M] = cost

    print(*ans, sep='\n')

sol()


Fixed submission:
import sys
input = sys.stdin.buffer.readline
from numba import njit
import numpy as np
INF = 10**18

@njit('(i8, i8[:,:])')
def solve(N, XYP):
    X = XYP[:, 0]
    Y = XYP[:, 1]

    def calcCost(sign, A):
        ret = np.ones((1 << N, N), dtype=np.int64) * INF
        for state in range(1 << N):
            useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
            for i in range(N):
                xyp = XYP[i]
                cost = abs(xyp[sign])
                for z in useA:
                    cost = min(cost, abs(xyp[sign] - z))
                ret[state][i] = cost * xyp[2]
        return ret

    costX = calcCost(0, X)
    costY = calcCost(1, Y)
    ans = np.ones(N + 1, dtype=np.int64) * INF

    for state in range(pow(3, N)):
        stateX = 0
        stateY = 0
        M = N
        for _ in range(N):
            state, r = divmod(state, 3)
            stateX <<= 1
            stateY <<= 1
            if r == 0: M -= 1
            if r == 1: stateX |= 1
            if r == 2: stateY |= 1

        cx, cy = costX[stateX], costY[stateY]
        cost = 0
        for i in range(N):
            cost += min(cx[i], cy[i])
        ans[M] = min(ans[M], cost)

    for a in ans:
        print(a)

def sol():
    N = int(input())
    XYP = np.asarray([list(map(int, input().split())) for _ in range(N)])
    solve(N, XYP)

sol()

Buggy python submission (verdict: Time Limit Exceeded):
from itertools import product
INF = 10**18
N = int(input())

def calcCost(AP, A):
    ret = [[INF] * N for _ in range(1 << N)]
    for state in range(1 << N):
        useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
        for i, (a, p) in enumerate(AP):
            cost = abs(a)
            for z in useA:
                cost = min(cost, abs(a - z))
            ret[state][i] = cost * p
    return ret

def sol():
    XYP = [tuple(map(int, input().split())) for _ in range(N)]
    XYP.sort(key=lambda xyp: xyp[2], reverse=True)

    X = [x for x, _, _ in XYP]
    Y = [y for _, y, _ in XYP]

    costX = calcCost([(x, p) for x, _, p in XYP], X)
    costY = calcCost([(y, p) for _, y, p in XYP], Y)

    ans = [INF] * (N + 1)
    for state in product((0, 1, 2), repeat=N):
        stateX = 0
        stateY = 0
        M = N
        for s in state:
            stateX <<= 1
            stateY <<= 1
            if s == 0: M -= 1
            if s == 1: stateX |= 1
            if s == 2: stateY |= 1

        cx, cy = costX[stateX], costY[stateY]
        cost = 0
        for i in range(N):
            cost += min(cx[i], cy[i])
            if cost >= ans[M]:
                break
        else:
            ans[M] = cost

    print(*ans, sep='\n')

sol()


Fixed submission:
import sys
input = sys.stdin.buffer.readline
from numba import njit
import numpy as np
INF = 10**18

@njit('(i8, i8[:,:])')
def solve(N, XYP):
    X = XYP[:, 0]
    Y = XYP[:, 1]

    def calcCost(sign, A):
        ret = np.ones((1 << N, N), dtype=np.int64) * INF
        for state in range(1 << N):
            useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
            for i in range(N):
                xyp = XYP[i]
                cost = abs(xyp[sign])
                for z in useA:
                    cost = min(cost, abs(xyp[sign] - z))
                ret[state][i] = cost * xyp[2]
        return ret

    costX = calcCost(0, X)
    costY = calcCost(1, Y)
    ans = np.ones(N + 1, dtype=np.int64) * INF

    for state in range(pow(3, N)):
        stateX = 0
        stateY = 0
        M = N
        for _ in range(N):
            state, r = divmod(state, 3)
            stateX <<= 1
            stateY <<= 1
            if r == 0: M -= 1
            if r == 1: stateX |= 1
            if r == 2: stateY |= 1

        cx, cy = costX[stateX], costY[stateY]
        cost = 0
        for i in range(N):
            cost += min(cx[i], cy[i])
        ans[M] = min(ans[M], cost)

    for a in ans:
        print(a)

def sol():
    N = int(input())
    XYP = np.asarray([list(map(int, input().split())) for _ in range(N)])
    solve(N, XYP)

sol()

Buggy python submission (verdict: Time Limit Exceeded):
from itertools import product
from random import randint, seed
from time import time
seed(time())

def sol():
    N = int(input())
    XYP = [tuple(map(int, input().split())) for _ in range(N)]
    XYP.sort(key=lambda xyp: xyp[2], reverse=(randint(0, 3) != 0))

    X = [x for x, _, _ in XYP]
    Y = [y for _, y, _ in XYP]
    INF = 10**18

    def calcCost(AP, A):
        ret = [[INF] * N for _ in range(1 << N)]
        for state in range(1 << N):
            useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
            for i, (a, p) in enumerate(AP):
                cost = abs(a)
                for z in useA:
                    cost = min(cost, abs(a - z))
                ret[state][i] = cost * p
        return ret

    costX = calcCost([(x, p) for x, _, p in XYP], X)
    costY = calcCost([(y, p) for _, y, p in XYP], Y)

    ans = [INF] * (N + 1)
    for state in product((0, 1, 2), repeat=N):
        stateX = 0
        stateY = 0
        M = N
        for s in state:
            stateX <<= 1
            stateY <<= 1
            if s == 0: M -= 1
            if s == 1: stateX |= 1
            if s == 2: stateY |= 1

        cx, cy = costX[stateX], costY[stateY]
        cost = 0
        for i in range(N):
            cost += min(cx[i], cy[i])
            if cost >= ans[M]:
                break
        else:
            ans[M] = cost

    print(*ans, sep='\n')

sol()

Fixed submission:
import sys
input = sys.stdin.buffer.readline
from numba import njit
import numpy as np
INF = 10**18

@njit('(i8, i8[:,:])')
def solve(N, XYP):
    X = XYP[:, 0]
    Y = XYP[:, 1]

    def calcCost(sign, A):
        ret = np.ones((1 << N, N), dtype=np.int64) * INF
        for state in range(1 << N):
            useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
            for i in range(N):
                xyp = XYP[i]
                cost = abs(xyp[sign])
                for z in useA:
                    cost = min(cost, abs(xyp[sign] - z))
                ret[state][i] = cost * xyp[2]
        return ret

    costX = calcCost(0, X)
    costY = calcCost(1, Y)
    ans = np.ones(N + 1, dtype=np.int64) * INF

    for state in range(pow(3, N)):
        stateX = 0
        stateY = 0
        M = N
        for _ in range(N):
            state, r = divmod(state, 3)
            stateX <<= 1
            stateY <<= 1
            if r == 0: M -= 1
            if r == 1: stateX |= 1
            if r == 2: stateY |= 1

        cx, cy = costX[stateX], costY[stateY]
        cost = 0
        for i in range(N):
            cost += min(cx[i], cy[i])
        ans[M] = min(ans[M], cost)

    for a in ans:
        print(a)

def sol():
    N = int(input())
    XYP = np.asarray([list(map(int, input().split())) for _ in range(N)])
    solve(N, XYP)

sol()

Buggy python submission (verdict: Time Limit Exceeded):
from itertools import product
from random import randint

def sol():
    N = int(input())
    XYP = [tuple(map(int, input().split())) for _ in range(N)]
    XYP.sort(key=lambda xyp: xyp[2], reverse=(randint(0, 3) != 0))

    X = [x for x, _, _ in XYP]
    Y = [y for _, y, _ in XYP]
    INF = 10**18

    def calcCost(AP, A):
        ret = [[INF] * N for _ in range(1 << N)]
        for state in range(1 << N):
            useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
            for i, (a, p) in enumerate(AP):
                cost = abs(a)
                for z in useA:
                    cost = min(cost, abs(a - z))
                ret[state][i] = cost * p
        return ret

    costX = calcCost([(x, p) for x, _, p in XYP], X)
    costY = calcCost([(y, p) for _, y, p in XYP], Y)

    ans = [INF] * (N + 1)
    for state in product((0, 1, 2), repeat=N):
        stateX = 0
        stateY = 0
        M = N
        for s in state:
            stateX <<= 1
            stateY <<= 1
            if s == 0: M -= 1
            if s == 1: stateX |= 1
            if s == 2: stateY |= 1

        cx, cy = costX[stateX], costY[stateY]
        cost = 0
        for i in range(N):
            cost += min(cx[i], cy[i])
            if cost >= ans[M]:
                break
        else:
            ans[M] = cost

    print(*ans, sep='\n')

sol()

Fixed submission:
import sys
input = sys.stdin.buffer.readline
from numba import njit
import numpy as np
INF = 10**18

@njit('(i8, i8[:,:])')
def solve(N, XYP):
    X = XYP[:, 0]
    Y = XYP[:, 1]

    def calcCost(sign, A):
        ret = np.ones((1 << N, N), dtype=np.int64) * INF
        for state in range(1 << N):
            useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
            for i in range(N):
                xyp = XYP[i]
                cost = abs(xyp[sign])
                for z in useA:
                    cost = min(cost, abs(xyp[sign] - z))
                ret[state][i] = cost * xyp[2]
        return ret

    costX = calcCost(0, X)
    costY = calcCost(1, Y)
    ans = np.ones(N + 1, dtype=np.int64) * INF

    for state in range(pow(3, N)):
        stateX = 0
        stateY = 0
        M = N
        for _ in range(N):
            state, r = divmod(state, 3)
            stateX <<= 1
            stateY <<= 1
            if r == 0: M -= 1
            if r == 1: stateX |= 1
            if r == 2: stateY |= 1

        cx, cy = costX[stateX], costY[stateY]
        cost = 0
        for i in range(N):
            cost += min(cx[i], cy[i])
        ans[M] = min(ans[M], cost)

    for a in ans:
        print(a)

def sol():
    N = int(input())
    XYP = np.asarray([list(map(int, input().split())) for _ in range(N)])
    solve(N, XYP)

sol()

Buggy python submission (verdict: Time Limit Exceeded):
from itertools import product
from random import randint

def sol():
    N = int(input())
    XYP = [tuple(map(int, input().split())) for _ in range(N)]
    XYP.sort(key=lambda xyp: xyp[2], reverse=randint(0, 3)==0)

    X = [x for x, _, _ in XYP]
    Y = [y for _, y, _ in XYP]
    INF = 10**18

    def calcCost(AP, A):
        ret = [[INF] * N for _ in range(1 << N)]
        for state in range(1 << N):
            useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
            for i, (a, p) in enumerate(AP):
                cost = abs(a)
                for z in useA:
                    cost = min(cost, abs(a - z))
                ret[state][i] = cost * p
        return ret

    costX = calcCost([(x, p) for x, _, p in XYP], X)
    costY = calcCost([(y, p) for _, y, p in XYP], Y)

    ans = [INF] * (N + 1)
    for state in product((0, 1, 2), repeat=N):
        stateX = 0
        stateY = 0
        M = N
        for s in state:
            stateX <<= 1
            stateY <<= 1
            if s == 0: M -= 1
            if s == 1: stateX |= 1
            if s == 2: stateY |= 1

        cx, cy = costX[stateX], costY[stateY]
        cost = 0
        for i in range(N):
            cost += min(cx[i], cy[i])
            if cost >= ans[M]:
                break
        else:
            ans[M] = cost

    print(*ans, sep='\n')

sol()

Fixed submission:
import sys
input = sys.stdin.buffer.readline
from numba import njit
import numpy as np
INF = 10**18

@njit('(i8, i8[:,:])')
def solve(N, XYP):
    X = XYP[:, 0]
    Y = XYP[:, 1]

    def calcCost(sign, A):
        ret = np.ones((1 << N, N), dtype=np.int64) * INF
        for state in range(1 << N):
            useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
            for i in range(N):
                xyp = XYP[i]
                cost = abs(xyp[sign])
                for z in useA:
                    cost = min(cost, abs(xyp[sign] - z))
                ret[state][i] = cost * xyp[2]
        return ret

    costX = calcCost(0, X)
    costY = calcCost(1, Y)
    ans = np.ones(N + 1, dtype=np.int64) * INF

    for state in range(pow(3, N)):
        stateX = 0
        stateY = 0
        M = N
        for _ in range(N):
            state, r = divmod(state, 3)
            stateX <<= 1
            stateY <<= 1
            if r == 0: M -= 1
            if r == 1: stateX |= 1
            if r == 2: stateY |= 1

        cx, cy = costX[stateX], costY[stateY]
        cost = 0
        for i in range(N):
            cost += min(cx[i], cy[i])
        ans[M] = min(ans[M], cost)

    for a in ans:
        print(a)

def sol():
    N = int(input())
    XYP = np.asarray([list(map(int, input().split())) for _ in range(N)])
    solve(N, XYP)

sol()

Buggy python submission (verdict: Time Limit Exceeded):
from itertools import product
from random import randint

def sol():
    N = int(input())
    XYP = [tuple(map(int, input().split())) for _ in range(N)]
    XYP.sort(key=lambda xyp: xyp[2], reverse=randint(0, 1))

    X = [x for x, _, _ in XYP]
    Y = [y for _, y, _ in XYP]
    INF = 10**18

    def calcCost(AP, A):
        ret = [[INF] * N for _ in range(1 << N)]
        for state in range(1 << N):
            useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
            for i, (a, p) in enumerate(AP):
                cost = abs(a)
                for z in useA:
                    cost = min(cost, abs(a - z))
                ret[state][i] = cost * p
        return ret

    costX = calcCost([(x, p) for x, _, p in XYP], X)
    costY = calcCost([(y, p) for _, y, p in XYP], Y)

    ans = [INF] * (N + 1)
    for state in product((0, 1, 2), repeat=N):
        stateX = 0
        stateY = 0
        M = N
        for s in state:
            stateX <<= 1
            stateY <<= 1
            if s == 0: M -= 1
            if s == 1: stateX |= 1
            if s == 2: stateY |= 1

        cx, cy = costX[stateX], costY[stateY]
        cost = 0
        for i in range(N):
            cost += min(cx[i], cy[i])
            if cost >= ans[M]:
                break
        else:
            ans[M] = cost

    print(*ans, sep='\n')

sol()

Fixed submission:
import sys
input = sys.stdin.buffer.readline
from numba import njit
import numpy as np
INF = 10**18

@njit('(i8, i8[:,:])')
def solve(N, XYP):
    X = XYP[:, 0]
    Y = XYP[:, 1]

    def calcCost(sign, A):
        ret = np.ones((1 << N, N), dtype=np.int64) * INF
        for state in range(1 << N):
            useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
            for i in range(N):
                xyp = XYP[i]
                cost = abs(xyp[sign])
                for z in useA:
                    cost = min(cost, abs(xyp[sign] - z))
                ret[state][i] = cost * xyp[2]
        return ret

    costX = calcCost(0, X)
    costY = calcCost(1, Y)
    ans = np.ones(N + 1, dtype=np.int64) * INF

    for state in range(pow(3, N)):
        stateX = 0
        stateY = 0
        M = N
        for _ in range(N):
            state, r = divmod(state, 3)
            stateX <<= 1
            stateY <<= 1
            if r == 0: M -= 1
            if r == 1: stateX |= 1
            if r == 2: stateY |= 1

        cx, cy = costX[stateX], costY[stateY]
        cost = 0
        for i in range(N):
            cost += min(cx[i], cy[i])
        ans[M] = min(ans[M], cost)

    for a in ans:
        print(a)

def sol():
    N = int(input())
    XYP = np.asarray([list(map(int, input().split())) for _ in range(N)])
    solve(N, XYP)

sol()

Buggy python submission (verdict: Time Limit Exceeded):
from itertools import product

def sol():
    N = int(input())
    XYP = [tuple(map(int, input().split())) for _ in range(N)]
    XYP.sort(key=lambda xyp: xyp[2], reverse=True)

    X = [x for x, _, _ in XYP]
    Y = [y for _, y, _ in XYP]
    INF = 10**18

    def calcCost(AP, A):
        ret = [[INF] * N for _ in range(1 << N)]
        for state in range(1 << N):
            useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
            for i, (a, p) in enumerate(AP):
                cost = abs(a)
                for z in useA:
                    cost = min(cost, abs(a - z))
                ret[state][i] = cost * p
        return ret

    costX = calcCost([(x, p) for x, _, p in XYP], X)
    costY = calcCost([(y, p) for _, y, p in XYP], Y)

    ans = [INF] * (N + 1)
    for state in product((0, 1, 2), repeat=N):
        stateX = 0
        stateY = 0
        M = N
        for s in state:
            stateX <<= 1
            stateY <<= 1
            if s == 0: M -= 1
            if s == 1: stateX |= 1
            if s == 2: stateY |= 1

        cx, cy = costX[stateX], costY[stateY]
        cost = 0
        for i in range(N):
            cost += min(cx[i], cy[i])
            if cost >= ans[M]:
                break
        else:
            ans[M] = cost

    print(*ans, sep='\n')

sol()

Fixed submission:
import sys
input = sys.stdin.buffer.readline
from numba import njit
import numpy as np
INF = 10**18

@njit('(i8, i8[:,:])')
def solve(N, XYP):
    X = XYP[:, 0]
    Y = XYP[:, 1]

    def calcCost(sign, A):
        ret = np.ones((1 << N, N), dtype=np.int64) * INF
        for state in range(1 << N):
            useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
            for i in range(N):
                xyp = XYP[i]
                cost = abs(xyp[sign])
                for z in useA:
                    cost = min(cost, abs(xyp[sign] - z))
                ret[state][i] = cost * xyp[2]
        return ret

    costX = calcCost(0, X)
    costY = calcCost(1, Y)
    ans = np.ones(N + 1, dtype=np.int64) * INF

    for state in range(pow(3, N)):
        stateX = 0
        stateY = 0
        M = N
        for _ in range(N):
            state, r = divmod(state, 3)
            stateX <<= 1
            stateY <<= 1
            if r == 0: M -= 1
            if r == 1: stateX |= 1
            if r == 2: stateY |= 1

        cx, cy = costX[stateX], costY[stateY]
        cost = 0
        for i in range(N):
            cost += min(cx[i], cy[i])
        ans[M] = min(ans[M], cost)

    for a in ans:
        print(a)

def sol():
    N = int(input())
    XYP = np.asarray([list(map(int, input().split())) for _ in range(N)])
    solve(N, XYP)

sol()

Buggy python submission (verdict: Time Limit Exceeded):
from itertools import product

def sol():
    N = int(input())
    XYP = [tuple(map(int, input().split())) for _ in range(N)]
    XYP.sort(key=lambda xyp: xyp[2], reverse=True)

    X = [x for x, _, _ in XYP]
    Y = [y for _, y, _ in XYP]
    INF = 10**18

    def calcCost(AP, A):
        ret = [[INF] * N for _ in range(1 << N)]
        for state in range(1 << N):
            useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
            for i, (a, p) in enumerate(AP):
                cost = abs(a)
                for z in useA:
                    cost = min(cost, abs(a - z))
                ret[state][i] = cost * p
        return ret

    costX = calcCost([(x, p) for x, _, p in XYP], X)
    costY = calcCost([(y, p) for _, y, p in XYP], Y)

    ans = [INF] * (N + 1)
    for state in product((0, 1, 2), repeat=N):
        stateX = 0
        stateY = 0
        M = N
        for s in state:
            stateX <<= 1
            stateY <<= 1
            if s == 0: M -= 1
            if s == 1: stateX |= 1
            if s == 2: stateY |= 1

        cx, cy = costX[stateX], costY[stateY]
        cost = 0
        for i in range(N):
            cost += min(cx[i], cy[i])
            if cost >= ans[M]:
                break
        else:
            ans[M] = cost

    print(*ans, sep='\n')

sol()

Fixed submission:
import sys
input = sys.stdin.buffer.readline
from numba import njit
import numpy as np
INF = 10**18

@njit('(i8, i8[:,:])')
def solve(N, XYP):
    X = XYP[:, 0]
    Y = XYP[:, 1]

    def calcCost(sign, A):
        ret = np.ones((1 << N, N), dtype=np.int64) * INF
        for state in range(1 << N):
            useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
            for i in range(N):
                xyp = XYP[i]
                cost = abs(xyp[sign])
                for z in useA:
                    cost = min(cost, abs(xyp[sign] - z))
                ret[state][i] = cost * xyp[2]
        return ret

    costX = calcCost(0, X)
    costY = calcCost(1, Y)
    ans = np.ones(N + 1, dtype=np.int64) * INF

    for state in range(pow(3, N)):
        stateX = 0
        stateY = 0
        M = N
        for _ in range(N):
            state, r = divmod(state, 3)
            stateX <<= 1
            stateY <<= 1
            if r == 0: M -= 1
            if r == 1: stateX |= 1
            if r == 2: stateY |= 1

        cx, cy = costX[stateX], costY[stateY]
        cost = 0
        for i in range(N):
            cost += min(cx[i], cy[i])
        ans[M] = min(ans[M], cost)

    for a in ans:
        print(a)

def sol():
    N = int(input())
    XYP = np.asarray([list(map(int, input().split())) for _ in range(N)])
    solve(N, XYP)

sol()

Buggy python submission (verdict: Time Limit Exceeded):
from itertools import product

def sol():
    N = int(input())
    XYP = [tuple(map(int, input().split())) for _ in range(N)]
    XYP.sort(key=lambda xyp: abs(xyp[0] * xyp[1] * xyp[2]), reverse=True)

    X = [x for x, _, _ in XYP]
    Y = [y for _, y, _ in XYP]
    P = [p for _, _, p in XYP]
    INF = 10**18

    def calcCost(AP, A):
        ret = [[INF] * N for _ in range(1 << N)]
        for state in range(1 << N):
            useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
            for i, (a, p) in enumerate(AP):
                cost = abs(a)
                for z in useA:
                    cost = min(cost, abs(a - z))
                ret[state][i] = cost * p
        return ret

    costX = calcCost([(x, p) for x, _, p in XYP], X)
    costY = calcCost([(y, p) for _, y, p in XYP], Y)

    ans = [INF] * (N + 1)
    for state in product((0, 1, 2), repeat=N):
        stateX = 0
        stateY = 0
        M = N
        for s in state:
            stateX <<= 1
            stateY <<= 1
            if s == 0: M -= 1
            if s == 1: stateX |= 1
            if s == 2: stateY |= 1

        cx, cy = costX[stateX], costY[stateY]
        cost = 0
        for i in range(N):
            cost += min(cx[i], cy[i])
            if cost >= ans[M]:
                break
        else:
            ans[M] = cost

    print(*ans, sep='\n')

sol()

Fixed submission:
import sys
input = sys.stdin.buffer.readline
from numba import njit
import numpy as np
INF = 10**18

@njit('(i8, i8[:,:])')
def solve(N, XYP):
    X = XYP[:, 0]
    Y = XYP[:, 1]

    def calcCost(sign, A):
        ret = np.ones((1 << N, N), dtype=np.int64) * INF
        for state in range(1 << N):
            useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
            for i in range(N):
                xyp = XYP[i]
                cost = abs(xyp[sign])
                for z in useA:
                    cost = min(cost, abs(xyp[sign] - z))
                ret[state][i] = cost * xyp[2]
        return ret

    costX = calcCost(0, X)
    costY = calcCost(1, Y)
    ans = np.ones(N + 1, dtype=np.int64) * INF

    for state in range(pow(3, N)):
        stateX = 0
        stateY = 0
        M = N
        for _ in range(N):
            state, r = divmod(state, 3)
            stateX <<= 1
            stateY <<= 1
            if r == 0: M -= 1
            if r == 1: stateX |= 1
            if r == 2: stateY |= 1

        cx, cy = costX[stateX], costY[stateY]
        cost = 0
        for i in range(N):
            cost += min(cx[i], cy[i])
        ans[M] = min(ans[M], cost)

    for a in ans:
        print(a)

def sol():
    N = int(input())
    XYP = np.asarray([list(map(int, input().split())) for _ in range(N)])
    solve(N, XYP)

sol()

Buggy python submission (verdict: Time Limit Exceeded):
from itertools import product

def sol():
    N = int(input())
    XYP = [tuple(map(int, input().split())) for _ in range(N)]
    X = [x for x, _, _ in XYP]
    Y = [y for _, y, _ in XYP]
    P = [p for _, _, p in XYP]
    INF = 10**18

    def calcCost(AP, A):
        ret = [[INF] * N for _ in range(1 << N)]
        for state in range(1 << N):
            useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
            for i, (a, p) in enumerate(AP):
                cost = abs(a)
                for z in useA:
                    cost = min(cost, abs(a - z))
                ret[state][i] = cost * p
        return ret

    costX = calcCost([(x, p) for x, _, p in XYP], X)
    costY = calcCost([(y, p) for _, y, p in XYP], Y)

    ans = [INF] * (N + 1)
    for state in product((0, 1, 2), repeat=N):
        stateX = 0
        stateY = 0
        M = N
        for s in state:
            stateX <<= 1
            stateY <<= 1
            if s == 0: M -= 1
            if s == 1: stateX |= 1
            if s == 2: stateY |= 1

        cx, cy = costX[stateX], costY[stateY]
        cost = 0
        for i in range(N):
            cost += min(cx[i], cy[i])
            if cost >= ans[M]:
                break
        else:
            ans[M] = cost

    print(*ans, sep='\n')

sol()

Fixed submission:
import sys
input = sys.stdin.buffer.readline
from numba import njit
import numpy as np
INF = 10**18

@njit('(i8, i8[:,:])')
def solve(N, XYP):
    X = XYP[:, 0]
    Y = XYP[:, 1]

    def calcCost(sign, A):
        ret = np.ones((1 << N, N), dtype=np.int64) * INF
        for state in range(1 << N):
            useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
            for i in range(N):
                xyp = XYP[i]
                cost = abs(xyp[sign])
                for z in useA:
                    cost = min(cost, abs(xyp[sign] - z))
                ret[state][i] = cost * xyp[2]
        return ret

    costX = calcCost(0, X)
    costY = calcCost(1, Y)
    ans = np.ones(N + 1, dtype=np.int64) * INF

    for state in range(pow(3, N)):
        stateX = 0
        stateY = 0
        M = N
        for _ in range(N):
            state, r = divmod(state, 3)
            stateX <<= 1
            stateY <<= 1
            if r == 0: M -= 1
            if r == 1: stateX |= 1
            if r == 2: stateY |= 1

        cx, cy = costX[stateX], costY[stateY]
        cost = 0
        for i in range(N):
            cost += min(cx[i], cy[i])
        ans[M] = min(ans[M], cost)

    for a in ans:
        print(a)

def sol():
    N = int(input())
    XYP = np.asarray([list(map(int, input().split())) for _ in range(N)])
    solve(N, XYP)

sol()

Buggy python submission (verdict: Time Limit Exceeded):
from itertools import product

def sol():
    N = int(input())
    XYP = [tuple(map(int, input().split())) for _ in range(N)]
    X = [x for x, _, _ in XYP]
    Y = [y for _, y, _ in XYP]
    P = [p for _, _, p in XYP]
    INF = 10**18

    def calcCost(AP, A):
        ret = [[INF] * N for _ in range(1 << N)]
        for state in range(1 << N):
            useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
            for i, (a, p) in enumerate(AP):
                cost = abs(a)
                for z in useA:
                    cost = min(cost, abs(a - z))
                ret[state][i] = cost * p
        return ret

    costX = calcCost([(x, p) for x, _, p in XYP], X)
    costY = calcCost([(y, p) for _, y, p in XYP], Y)

    ans = [INF] * (N + 1)
    for state in product((0, 1, 2), repeat=N):
        stateX = 0
        stateY = 0
        M = N
        for s in state:
            stateX <<= 1
            stateY <<= 1
            if s == 0: M -= 1
            if s == 1: stateX |= 1
            if s == 2: stateY |= 1

        cx, cy = costX[stateX], costY[stateY]
        cost = 0
        for i in range(N):
            cost += min(cx[i], cy[i])
            if cost >= ans[M]:
                break
        else:
            ans[M] = cost

    print(*ans, sep='\n')

sol()

Fixed submission:
import sys
input = sys.stdin.buffer.readline
from numba import njit
import numpy as np
INF = 10**18

@njit('(i8, i8[:,:])')
def solve(N, XYP):
    X = XYP[:, 0]
    Y = XYP[:, 1]

    def calcCost(sign, A):
        ret = np.ones((1 << N, N), dtype=np.int64) * INF
        for state in range(1 << N):
            useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
            for i in range(N):
                xyp = XYP[i]
                cost = abs(xyp[sign])
                for z in useA:
                    cost = min(cost, abs(xyp[sign] - z))
                ret[state][i] = cost * xyp[2]
        return ret

    costX = calcCost(0, X)
    costY = calcCost(1, Y)
    ans = np.ones(N + 1, dtype=np.int64) * INF

    for state in range(pow(3, N)):
        stateX = 0
        stateY = 0
        M = N
        for _ in range(N):
            state, r = divmod(state, 3)
            stateX <<= 1
            stateY <<= 1
            if r == 0: M -= 1
            if r == 1: stateX |= 1
            if r == 2: stateY |= 1

        cx, cy = costX[stateX], costY[stateY]
        cost = 0
        for i in range(N):
            cost += min(cx[i], cy[i])
        ans[M] = min(ans[M], cost)

    for a in ans:
        print(a)

def sol():
    N = int(input())
    XYP = np.asarray([list(map(int, input().split())) for _ in range(N)])
    solve(N, XYP)

sol()

Buggy python submission (verdict: Time Limit Exceeded):
from itertools import product

def sol():
    N = int(input())
    XYP = [tuple(map(int, input().split())) for _ in range(N)]
    X = [x for x, _, _ in XYP]
    Y = [y for _, y, _ in XYP]
    P = [p for _, _, p in XYP]
    INF = 10**18

    def calcCost(AP, A):
        ret = [[INF] * N for _ in range(1 << N)]
        for state in range(1 << N):
            useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
            for i, (a, p) in enumerate(AP):
                cost = abs(a)
                for z in useA:
                    cost = min(cost, abs(a - z))
                ret[state][i] = cost * p
        return ret

    costX = calcCost([(x, p) for x, _, p in XYP], X)
    costY = calcCost([(y, p) for _, y, p in XYP], Y)

    ans = [INF] * (N + 1)
    for state in product((0, 1, 2), repeat=N):
        stateX = 0
        stateY = 0
        M = N
        for s in state:
            stateX <<= 1
            stateY <<= 1
            if s == 0: M -= 1
            if s == 1: stateX |= 1
            if s == 2: stateY |= 1

        cx, cy = costX[stateX], costY[stateY]
        cost = 0
        for s in map(min, zip(cx, cy)):
            cost += s
            if cost >= ans[M]:
                break
        else:
            ans[M] = cost

    print(*ans, sep='\n')

sol()

Fixed submission:
import sys
input = sys.stdin.buffer.readline
from numba import njit
import numpy as np
INF = 10**18

@njit('(i8, i8[:,:])')
def solve(N, XYP):
    X = XYP[:, 0]
    Y = XYP[:, 1]

    def calcCost(sign, A):
        ret = np.ones((1 << N, N), dtype=np.int64) * INF
        for state in range(1 << N):
            useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
            for i in range(N):
                xyp = XYP[i]
                cost = abs(xyp[sign])
                for z in useA:
                    cost = min(cost, abs(xyp[sign] - z))
                ret[state][i] = cost * xyp[2]
        return ret

    costX = calcCost(0, X)
    costY = calcCost(1, Y)
    ans = np.ones(N + 1, dtype=np.int64) * INF

    for state in range(pow(3, N)):
        stateX = 0
        stateY = 0
        M = N
        for _ in range(N):
            state, r = divmod(state, 3)
            stateX <<= 1
            stateY <<= 1
            if r == 0: M -= 1
            if r == 1: stateX |= 1
            if r == 2: stateY |= 1

        cx, cy = costX[stateX], costY[stateY]
        cost = 0
        for i in range(N):
            cost += min(cx[i], cy[i])
        ans[M] = min(ans[M], cost)

    for a in ans:
        print(a)

def sol():
    N = int(input())
    XYP = np.asarray([list(map(int, input().split())) for _ in range(N)])
    solve(N, XYP)

sol()

Buggy python submission (verdict: Time Limit Exceeded):
from itertools import product

def sol():
    N = int(input())
    XYP = [tuple(map(int, input().split())) for _ in range(N)]
    X = [x for x, _, _ in XYP]
    Y = [y for _, y, _ in XYP]
    P = [p for _, _, p in XYP]
    INF = 10**18

    def calcCost(AP, A):
        ret = [[INF] * N for _ in range(1 << N)]
        for state in range(1 << N):
            useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
            for i, (a, p) in enumerate(AP):
                cost = abs(a)
                for z in useA:
                    cost = min(cost, abs(a - z))
                ret[state][i] = cost * p
        return ret

    costX = calcCost([(x, p) for x, _, p in XYP], X)
    costY = calcCost([(y, p) for _, y, p in XYP], Y)

    ans = [INF] * (N + 1)
    for state in product((0, 1, 2), repeat=N):
        stateX = 0
        stateY = 0
        M = N
        for s in state:
            stateX <<= 1
            stateY <<= 1
            if s == 0: M -= 1
            if s == 1: stateX |= 1
            if s == 2: stateY |= 1

        cx, cy = costX[stateX], costY[stateY]
        cost, an = 0, ans[M]
        for s in map(min, zip(cx, cy)):
            cost += s
            if cost >= an:
                break
        else:
            ans[M] = cost

    print(*ans, sep='\n')

sol()

Fixed submission:
import sys
input = sys.stdin.buffer.readline
from numba import njit
import numpy as np
INF = 10**18

@njit('(i8, i8[:,:])')
def solve(N, XYP):
    X = XYP[:, 0]
    Y = XYP[:, 1]

    def calcCost(sign, A):
        ret = np.ones((1 << N, N), dtype=np.int64) * INF
        for state in range(1 << N):
            useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
            for i in range(N):
                xyp = XYP[i]
                cost = abs(xyp[sign])
                for z in useA:
                    cost = min(cost, abs(xyp[sign] - z))
                ret[state][i] = cost * xyp[2]
        return ret

    costX = calcCost(0, X)
    costY = calcCost(1, Y)
    ans = np.ones(N + 1, dtype=np.int64) * INF

    for state in range(pow(3, N)):
        stateX = 0
        stateY = 0
        M = N
        for _ in range(N):
            state, r = divmod(state, 3)
            stateX <<= 1
            stateY <<= 1
            if r == 0: M -= 1
            if r == 1: stateX |= 1
            if r == 2: stateY |= 1

        cx, cy = costX[stateX], costY[stateY]
        cost = 0
        for i in range(N):
            cost += min(cx[i], cy[i])
        ans[M] = min(ans[M], cost)

    for a in ans:
        print(a)

def sol():
    N = int(input())
    XYP = np.asarray([list(map(int, input().split())) for _ in range(N)])
    solve(N, XYP)

sol()

Buggy python submission (verdict: Time Limit Exceeded):
from itertools import product

N = int(input())
XYP = [tuple(map(int, input().split())) for _ in range(N)]
X = [x for x, _, _ in XYP]
Y = [y for _, y, _ in XYP]
P = [p for _, _, p in XYP]
INF = 10**18

def calcCost(AP, A):
    ret = [[INF] * N for _ in range(1 << N)]
    for state in range(1 << N):
        useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
        for i, (a, p) in enumerate(AP):
            cost = abs(a)
            for z in useA:
                cost = min(cost, abs(a - z))
            ret[state][i] = cost * p
    return ret

costX = calcCost([(x, p) for x, _, p in XYP], X)
costY = calcCost([(y, p) for _, y, p in XYP], Y)

ans = [INF] * (N + 1)
for state in product((0, 1, 2), repeat=N):
    stateX = 0
    stateY = 0
    M = N
    for s in state:
        stateX <<= 1
        stateY <<= 1
        if s == 0: M -= 1
        if s == 1: stateX |= 1
        if s == 2: stateY |= 1

    cx, cy = costX[stateX], costY[stateY]
    cost, an = 0, ans[M]
    for s in map(min, zip(cx, cy)):
        cost += s
        if cost >= an:
            break
    else:
        ans[M] = cost

print(*ans, sep='\n')


Fixed submission:
import sys
input = sys.stdin.buffer.readline
from numba import njit
import numpy as np
INF = 10**18

@njit('(i8, i8[:,:])')
def solve(N, XYP):
    X = XYP[:, 0]
    Y = XYP[:, 1]

    def calcCost(sign, A):
        ret = np.ones((1 << N, N), dtype=np.int64) * INF
        for state in range(1 << N):
            useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
            for i in range(N):
                xyp = XYP[i]
                cost = abs(xyp[sign])
                for z in useA:
                    cost = min(cost, abs(xyp[sign] - z))
                ret[state][i] = cost * xyp[2]
        return ret

    costX = calcCost(0, X)
    costY = calcCost(1, Y)
    ans = np.ones(N + 1, dtype=np.int64) * INF

    for state in range(pow(3, N)):
        stateX = 0
        stateY = 0
        M = N
        for _ in range(N):
            state, r = divmod(state, 3)
            stateX <<= 1
            stateY <<= 1
            if r == 0: M -= 1
            if r == 1: stateX |= 1
            if r == 2: stateY |= 1

        cx, cy = costX[stateX], costY[stateY]
        cost = 0
        for i in range(N):
            cost += min(cx[i], cy[i])
        ans[M] = min(ans[M], cost)

    for a in ans:
        print(a)

def sol():
    N = int(input())
    XYP = np.asarray([list(map(int, input().split())) for _ in range(N)])
    solve(N, XYP)

sol()

Buggy python submission (verdict: Time Limit Exceeded):
from itertools import product

N = int(input())
XYP = [tuple(map(int, input().split())) for _ in range(N)]
X = [x for x, _, _ in XYP]
Y = [y for _, y, _ in XYP]
P = [p for _, _, p in XYP]
INF = 10**18

def calcCost(AP, A):
    ret = [[INF] * N for _ in range(1 << N)]
    for state in range(1 << N):
        useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
        for i, (a, p) in enumerate(AP):
            cost = abs(a)
            for z in useA:
                cost = min(cost, abs(a - z))
            ret[state][i] = cost * p
    return ret

costX = calcCost([(x, p) for x, _, p in XYP], X)
costY = calcCost([(y, p) for _, y, p in XYP], Y)

ans = [INF] * (N + 1)
for state in product((0, 1, 2), repeat=N):
    stateX = 0
    stateY = 0
    M = N
    for s in state:
        stateX <<= 1
        stateY <<= 1
        if s == 0: M -= 1
        if s == 1: stateX |= 1
        if s == 2: stateY |= 1

    cx, cy = costX[stateX], costY[stateY]
    cost = 0
    for s in map(min, zip(cx, cy)):
        cost += s
        if cost >= ans[M]:
            break
    else:
        ans[M] = cost

print(*ans, sep='\n')


Fixed submission:
import sys
input = sys.stdin.buffer.readline
from numba import njit
import numpy as np
INF = 10**18

@njit('(i8, i8[:,:])')
def solve(N, XYP):
    X = XYP[:, 0]
    Y = XYP[:, 1]

    def calcCost(sign, A):
        ret = np.ones((1 << N, N), dtype=np.int64) * INF
        for state in range(1 << N):
            useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
            for i in range(N):
                xyp = XYP[i]
                cost = abs(xyp[sign])
                for z in useA:
                    cost = min(cost, abs(xyp[sign] - z))
                ret[state][i] = cost * xyp[2]
        return ret

    costX = calcCost(0, X)
    costY = calcCost(1, Y)
    ans = np.ones(N + 1, dtype=np.int64) * INF

    for state in range(pow(3, N)):
        stateX = 0
        stateY = 0
        M = N
        for _ in range(N):
            state, r = divmod(state, 3)
            stateX <<= 1
            stateY <<= 1
            if r == 0: M -= 1
            if r == 1: stateX |= 1
            if r == 2: stateY |= 1

        cx, cy = costX[stateX], costY[stateY]
        cost = 0
        for i in range(N):
            cost += min(cx[i], cy[i])
        ans[M] = min(ans[M], cost)

    for a in ans:
        print(a)

def sol():
    N = int(input())
    XYP = np.asarray([list(map(int, input().split())) for _ in range(N)])
    solve(N, XYP)

sol()

Buggy python submission (verdict: Time Limit Exceeded):
from itertools import product

N = int(input())
XYP = [tuple(map(int, input().split())) for _ in range(N)]
X = [x for x, _, _ in XYP]
Y = [y for _, y, _ in XYP]
P = [p for _, _, p in XYP]
INF = 10**18

def calcCost(AP, A):
    ret = [[INF] * N for _ in range(1 << N)]
    for state in range(1 << N):
        useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
        for i, (a, p) in enumerate(AP):
            cost = abs(a)
            for z in useA:
                cost = min(cost, abs(a - z))
            ret[state][i] = cost * p
    return ret

costX = calcCost([(x, p) for x, _, p in XYP], X)
costY = calcCost([(y, p) for _, y, p in XYP], Y)

ans = [[] for _ in range(N + 1)]
for state in product((0, 1, 2), repeat=N):
    stateX = 0
    stateY = 0
    M = N
    for s in state:
        stateX <<= 1
        stateY <<= 1
        if s == 0: M -= 1
        if s == 1: stateX |= 1
        if s == 2: stateY |= 1

    ans[M].append((costX[stateX], costY[stateY]))

for xy in ans:
    res = INF
    for a in xy:
        res = min(res, sum(map(min, zip(*a))))
    print(res)


Fixed submission:
import sys
input = sys.stdin.buffer.readline
from numba import njit
import numpy as np
INF = 10**18

@njit('(i8, i8[:,:])')
def solve(N, XYP):
    X = XYP[:, 0]
    Y = XYP[:, 1]

    def calcCost(sign, A):
        ret = np.ones((1 << N, N), dtype=np.int64) * INF
        for state in range(1 << N):
            useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
            for i in range(N):
                xyp = XYP[i]
                cost = abs(xyp[sign])
                for z in useA:
                    cost = min(cost, abs(xyp[sign] - z))
                ret[state][i] = cost * xyp[2]
        return ret

    costX = calcCost(0, X)
    costY = calcCost(1, Y)
    ans = np.ones(N + 1, dtype=np.int64) * INF

    for state in range(pow(3, N)):
        stateX = 0
        stateY = 0
        M = N
        for _ in range(N):
            state, r = divmod(state, 3)
            stateX <<= 1
            stateY <<= 1
            if r == 0: M -= 1
            if r == 1: stateX |= 1
            if r == 2: stateY |= 1

        cx, cy = costX[stateX], costY[stateY]
        cost = 0
        for i in range(N):
            cost += min(cx[i], cy[i])
        ans[M] = min(ans[M], cost)

    for a in ans:
        print(a)

def sol():
    N = int(input())
    XYP = np.asarray([list(map(int, input().split())) for _ in range(N)])
    solve(N, XYP)

sol()

Buggy python submission (verdict: Time Limit Exceeded):
from itertools import product

N = int(input())
XYP = [tuple(map(int, input().split())) for _ in range(N)]
X = [x for x, _, _ in XYP]
Y = [y for _, y, _ in XYP]
P = [p for _, _, p in XYP]
INF = 10**18

def calcCost(AP, A):
    ret = [[INF] * N for _ in range(1 << N)]
    for state in range(1 << N):
        useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
        for i, (a, p) in enumerate(AP):
            cost = abs(a)
            for z in useA:
                cost = min(cost, abs(a - z))
            ret[state][i] = cost * p
    return ret

costX = calcCost([(x, p) for x, _, p in XYP], X)
costY = calcCost([(y, p) for _, y, p in XYP], Y)

ans = [INF] * (N + 1)
for state in product((0, 1, 2), repeat=N):
    stateX = 0
    stateY = 0
    M = N
    for s in state:
        stateX <<= 1
        stateY <<= 1
        if s == 0: M -= 1
        if s == 1: stateX |= 1
        if s == 2: stateY |= 1

    cost = sum(map(min, zip(costX[stateX], costY[stateY])))
    if ans[M] > cost:
        ans[M] = cost

print(*ans, sep='\n')


Fixed submission:
import sys
input = sys.stdin.buffer.readline
from numba import njit
import numpy as np
INF = 10**18

@njit('(i8, i8[:,:])')
def solve(N, XYP):
    X = XYP[:, 0]
    Y = XYP[:, 1]

    def calcCost(sign, A):
        ret = np.ones((1 << N, N), dtype=np.int64) * INF
        for state in range(1 << N):
            useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
            for i in range(N):
                xyp = XYP[i]
                cost = abs(xyp[sign])
                for z in useA:
                    cost = min(cost, abs(xyp[sign] - z))
                ret[state][i] = cost * xyp[2]
        return ret

    costX = calcCost(0, X)
    costY = calcCost(1, Y)
    ans = np.ones(N + 1, dtype=np.int64) * INF

    for state in range(pow(3, N)):
        stateX = 0
        stateY = 0
        M = N
        for _ in range(N):
            state, r = divmod(state, 3)
            stateX <<= 1
            stateY <<= 1
            if r == 0: M -= 1
            if r == 1: stateX |= 1
            if r == 2: stateY |= 1

        cx, cy = costX[stateX], costY[stateY]
        cost = 0
        for i in range(N):
            cost += min(cx[i], cy[i])
        ans[M] = min(ans[M], cost)

    for a in ans:
        print(a)

def sol():
    N = int(input())
    XYP = np.asarray([list(map(int, input().split())) for _ in range(N)])
    solve(N, XYP)

sol()

Buggy python submission (verdict: Time Limit Exceeded):
from itertools import product

N = int(input())
XYP = [tuple(map(int, input().split())) for _ in range(N)]
X = [x for x, _, _ in XYP]
Y = [y for _, y, _ in XYP]
P = [p for _, _, p in XYP]
INF = 10**18

def calcCost(AP, A):
    ret = [[INF] * N for _ in range(1 << N)]
    for state in range(1 << N):
        useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
        for i, (a, p) in enumerate(AP):
            cost = abs(a)
            for z in useA:
                cost = min(cost, abs(a - z))
            ret[state][i] = cost * p
    return ret

costX = calcCost([(x, p) for x, _, p in XYP], X)
costY = calcCost([(y, p) for _, y, p in XYP], Y)

ans = [INF] * (N + 1)
for state in product((0, 1, 2), repeat=N):
    stateX = 0
    stateY = 0
    M = N
    for s in state:
        stateX <<= 1
        stateY <<= 1
        if s == 0: M -= 1
        if s == 1: stateX |= 1
        if s == 2: stateY |= 1

    cost = 0
    for i in range(N):
        cost += min(costX[stateX][i], costY[stateY][i])

    if ans[M] > cost:
        ans[M] = cost

print(*ans, sep='\n')


Fixed submission:
import sys
input = sys.stdin.buffer.readline
from numba import njit
import numpy as np
INF = 10**18

@njit('(i8, i8[:,:])')
def solve(N, XYP):
    X = XYP[:, 0]
    Y = XYP[:, 1]

    def calcCost(sign, A):
        ret = np.ones((1 << N, N), dtype=np.int64) * INF
        for state in range(1 << N):
            useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
            for i in range(N):
                xyp = XYP[i]
                cost = abs(xyp[sign])
                for z in useA:
                    cost = min(cost, abs(xyp[sign] - z))
                ret[state][i] = cost * xyp[2]
        return ret

    costX = calcCost(0, X)
    costY = calcCost(1, Y)
    ans = np.ones(N + 1, dtype=np.int64) * INF

    for state in range(pow(3, N)):
        stateX = 0
        stateY = 0
        M = N
        for _ in range(N):
            state, r = divmod(state, 3)
            stateX <<= 1
            stateY <<= 1
            if r == 0: M -= 1
            if r == 1: stateX |= 1
            if r == 2: stateY |= 1

        cx, cy = costX[stateX], costY[stateY]
        cost = 0
        for i in range(N):
            cost += min(cx[i], cy[i])
        ans[M] = min(ans[M], cost)

    for a in ans:
        print(a)

def sol():
    N = int(input())
    XYP = np.asarray([list(map(int, input().split())) for _ in range(N)])
    solve(N, XYP)

sol()

Buggy python submission (verdict: Time Limit Exceeded):
from itertools import product

N = int(input())
XYP = [tuple(map(int, input().split())) for _ in range(N)]
X = [x for x, _, _ in XYP]
Y = [y for _, y, _ in XYP]
P = [p for _, _, p in XYP]
INF = 10**18

def calcCost(AP, A):
    ret = [[INF] * N for _ in range(1 << N)]
    for state in range(1 << N):
        useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
        for i, (a, _) in enumerate(AP):
            cost = abs(a)
            for z in useA:
                cost = min(cost, abs(a - z))
            ret[state][i] = cost
    return ret

costX = calcCost([(x, p) for x, _, p in XYP], X)
costY = calcCost([(y, p) for _, y, p in XYP], Y)

ans = [INF] * (N + 1)
for state in product((0, 1, 2), repeat=N):
    stateX = 0
    stateY = 0
    M = N
    for s in state:
        stateX <<= 1
        stateY <<= 1
        if s == 0: M -= 1
        if s == 1: stateX |= 1
        if s == 2: stateY |= 1

    cx = costX[stateX]
    cy = costY[stateY]
    cost = 0

    for i, p in enumerate(P):
        cost += p * min(cx[i], cy[i])

    if ans[M] > cost:
        ans[M] = cost

print(*ans, sep='\n')


Fixed submission:
import sys
input = sys.stdin.buffer.readline
from numba import njit
import numpy as np
INF = 10**18

@njit('(i8, i8[:,:])')
def solve(N, XYP):
    X = XYP[:, 0]
    Y = XYP[:, 1]

    def calcCost(sign, A):
        ret = np.ones((1 << N, N), dtype=np.int64) * INF
        for state in range(1 << N):
            useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
            for i in range(N):
                xyp = XYP[i]
                cost = abs(xyp[sign])
                for z in useA:
                    cost = min(cost, abs(xyp[sign] - z))
                ret[state][i] = cost * xyp[2]
        return ret

    costX = calcCost(0, X)
    costY = calcCost(1, Y)
    ans = np.ones(N + 1, dtype=np.int64) * INF

    for state in range(pow(3, N)):
        stateX = 0
        stateY = 0
        M = N
        for _ in range(N):
            state, r = divmod(state, 3)
            stateX <<= 1
            stateY <<= 1
            if r == 0: M -= 1
            if r == 1: stateX |= 1
            if r == 2: stateY |= 1

        cx, cy = costX[stateX], costY[stateY]
        cost = 0
        for i in range(N):
            cost += min(cx[i], cy[i])
        ans[M] = min(ans[M], cost)

    for a in ans:
        print(a)

def sol():
    N = int(input())
    XYP = np.asarray([list(map(int, input().split())) for _ in range(N)])
    solve(N, XYP)

sol()

Buggy python submission (verdict: Time Limit Exceeded):
from itertools import product

N = int(input())
XYP = [tuple(map(int, input().split())) for _ in range(N)]
X = [x for x, _, _ in XYP]
Y = [y for _, y, _ in XYP]
INF = 10**18

def calcCost(AP, A):
    ret = [[INF] * N for _ in range(1 << N)]
    for state in range(1 << N):
        useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
        for i, (a, p) in enumerate(AP):
            cost = abs(a)
            for z in useA:
                cost = min(cost, abs(a - z))
            ret[state][i] = cost * p
    return ret

costX = calcCost([(x, p) for x, _, p in XYP], X)
costY = calcCost([(y, p) for _, y, p in XYP], Y)

ans = [INF] * (N + 1)
for state in product((0, 1, 2), repeat=N):
    stateX = 0
    stateY = 0
    M = N
    for s in state:
        stateX <<= 1
        stateY <<= 1
        if s == 0: M -= 1
        if s == 1: stateX |= 1
        if s == 2: stateY |= 1

    cost = 0
    for x, y in zip(costX[stateX], costY[stateY]):
        cost += min(x, y)

    if ans[M] > cost:
        ans[M] = cost

print(*ans, sep='\n')


Fixed submission:
import sys
input = sys.stdin.buffer.readline
from numba import njit
import numpy as np
INF = 10**18

@njit('(i8, i8[:,:])')
def solve(N, XYP):
    X = XYP[:, 0]
    Y = XYP[:, 1]

    def calcCost(sign, A):
        ret = np.ones((1 << N, N), dtype=np.int64) * INF
        for state in range(1 << N):
            useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
            for i in range(N):
                xyp = XYP[i]
                cost = abs(xyp[sign])
                for z in useA:
                    cost = min(cost, abs(xyp[sign] - z))
                ret[state][i] = cost * xyp[2]
        return ret

    costX = calcCost(0, X)
    costY = calcCost(1, Y)
    ans = np.ones(N + 1, dtype=np.int64) * INF

    for state in range(pow(3, N)):
        stateX = 0
        stateY = 0
        M = N
        for _ in range(N):
            state, r = divmod(state, 3)
            stateX <<= 1
            stateY <<= 1
            if r == 0: M -= 1
            if r == 1: stateX |= 1
            if r == 2: stateY |= 1

        cx, cy = costX[stateX], costY[stateY]
        cost = 0
        for i in range(N):
            cost += min(cx[i], cy[i])
        ans[M] = min(ans[M], cost)

    for a in ans:
        print(a)

def sol():
    N = int(input())
    XYP = np.asarray([list(map(int, input().split())) for _ in range(N)])
    solve(N, XYP)

sol()

Buggy python submission (verdict: Time Limit Exceeded):
from itertools import product

N = int(input())
XYP = [tuple(map(int, input().split())) for _ in range(N)]
X = [x for x, _, _ in XYP]
Y = [y for _, y, _ in XYP]
INF = 10**18

def calcCost(AP, A):
    ret = [[INF] * N for _ in range((1 << N) + 100)]
    for state in range(1 << N):
        useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
        for i, (a, p) in enumerate(AP):
            cost = abs(a)
            for z in useA:
                cost = min(cost, abs(a - z))
            ret[state + 100][i] = cost * p
    return ret

costX = calcCost([(x, p) for x, _, p in XYP], X)
costY = calcCost([(y, p) for _, y, p in XYP], Y)

ans = [INF] * (N + 1)
for state in product((0, 1, 2), repeat=N):
    stateX = 0
    stateY = 0
    M = N
    for s in state:
        stateX <<= 1
        stateY <<= 1
        if s == 0: M -= 1
        if s == 1: stateX |= 1
        if s == 2: stateY |= 1

    cost = 0
    for x, y in zip(costX[stateX + 100], costY[stateY + 100]):
        cost += min(x, y)

    if ans[M] > cost:
        ans[M] = cost

print(*ans, sep='\n')


Fixed submission:
import sys
input = sys.stdin.buffer.readline
from numba import njit
import numpy as np
INF = 10**18

@njit('(i8, i8[:,:])')
def solve(N, XYP):
    X = XYP[:, 0]
    Y = XYP[:, 1]

    def calcCost(sign, A):
        ret = np.ones((1 << N, N), dtype=np.int64) * INF
        for state in range(1 << N):
            useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
            for i in range(N):
                xyp = XYP[i]
                cost = abs(xyp[sign])
                for z in useA:
                    cost = min(cost, abs(xyp[sign] - z))
                ret[state][i] = cost * xyp[2]
        return ret

    costX = calcCost(0, X)
    costY = calcCost(1, Y)
    ans = np.ones(N + 1, dtype=np.int64) * INF

    for state in range(pow(3, N)):
        stateX = 0
        stateY = 0
        M = N
        for _ in range(N):
            state, r = divmod(state, 3)
            stateX <<= 1
            stateY <<= 1
            if r == 0: M -= 1
            if r == 1: stateX |= 1
            if r == 2: stateY |= 1

        cx, cy = costX[stateX], costY[stateY]
        cost = 0
        for i in range(N):
            cost += min(cx[i], cy[i])
        ans[M] = min(ans[M], cost)

    for a in ans:
        print(a)

def sol():
    N = int(input())
    XYP = np.asarray([list(map(int, input().split())) for _ in range(N)])
    solve(N, XYP)

sol()

Buggy python submission (verdict: Time Limit Exceeded):
from itertools import product

N = int(input())
XYP = [tuple(map(int, input().split())) for _ in range(N)]
X = [x for x, _, _ in XYP]
Y = [y for _, y, _ in XYP]
INF = 10**18

def calcCost(AP, A):
    ret = [[INF] * N for _ in range(1 << (N + 1))]
    for state in range(1 << N):
        useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
        for i, (a, p) in enumerate(AP):
            cost = abs(a)
            for z in useA:
                cost = min(cost, abs(a - z))
            ret[state << 1][i] = cost * p
    return ret

costX = calcCost([(x, p) for x, _, p in XYP], X)
costY = calcCost([(y, p) for _, y, p in XYP], Y)

ans = [INF] * (N + 1)
for state in product((0, 1, 2), repeat=N):
    stateX = 0
    stateY = 0
    M = N
    for s in state:
        if s == 0: M -= 1
        if s == 1: stateX |= 1
        if s == 2: stateY |= 1
        stateX <<= 1
        stateY <<= 1

    cx = costX[stateX]
    cy = costY[stateY]

    cost = 0
    for x, y in zip(costX[stateX], costY[stateY]):
        cost += min(x, y)

    if ans[M] > cost:
        ans[M] = cost

print(*ans, sep='\n')


Fixed submission:
import sys
input = sys.stdin.buffer.readline
from numba import njit
import numpy as np
INF = 10**18

@njit('(i8, i8[:,:])')
def solve(N, XYP):
    X = XYP[:, 0]
    Y = XYP[:, 1]

    def calcCost(sign, A):
        ret = np.ones((1 << N, N), dtype=np.int64) * INF
        for state in range(1 << N):
            useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
            for i in range(N):
                xyp = XYP[i]
                cost = abs(xyp[sign])
                for z in useA:
                    cost = min(cost, abs(xyp[sign] - z))
                ret[state][i] = cost * xyp[2]
        return ret

    costX = calcCost(0, X)
    costY = calcCost(1, Y)
    ans = np.ones(N + 1, dtype=np.int64) * INF

    for state in range(pow(3, N)):
        stateX = 0
        stateY = 0
        M = N
        for _ in range(N):
            state, r = divmod(state, 3)
            stateX <<= 1
            stateY <<= 1
            if r == 0: M -= 1
            if r == 1: stateX |= 1
            if r == 2: stateY |= 1

        cx, cy = costX[stateX], costY[stateY]
        cost = 0
        for i in range(N):
            cost += min(cx[i], cy[i])
        ans[M] = min(ans[M], cost)

    for a in ans:
        print(a)

def sol():
    N = int(input())
    XYP = np.asarray([list(map(int, input().split())) for _ in range(N)])
    solve(N, XYP)

sol()

Buggy python submission (verdict: Time Limit Exceeded):
from itertools import product

MEM = [0] * (10**6)

N = int(input())
XYP = [tuple(map(int, input().split())) for _ in range(N)]
X = [x for x, _, _ in XYP]
Y = [y for _, y, _ in XYP]
INF = 10**18

def calcCost(AP, A):
    ret = [[INF] * N for _ in range(1 << N)]
    for state in range(1 << N):
        useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
        for i, (a, p) in enumerate(AP):
            cost = abs(a)
            for z in useA:
                cost = min(cost, abs(a - z))
            ret[state][i] = cost * p
    return ret

costX = calcCost([(x, p) for x, _, p in XYP], X)
costY = calcCost([(y, p) for _, y, p in XYP], Y)

ans = [INF] * (N + 1)
for state in product((0, 1, 2), repeat=N):
    stateX = 0
    stateY = 0
    M = N
    for s in state:
        stateX <<= 1
        stateY <<= 1
        if s == 0: M -= 1
        if s == 1: stateX |= 1
        if s == 2: stateY |= 1

    cx = costX[stateX]
    cy = costY[stateY]

    cost = 0
    for x, y in zip(costX[stateX], costY[stateY]):
        cost += min(x, y)

    if ans[M] > cost:
        ans[M] = cost

print(*ans, sep='\n')


Fixed submission:
import sys
input = sys.stdin.buffer.readline
from numba import njit
import numpy as np
INF = 10**18

@njit('(i8, i8[:,:])')
def solve(N, XYP):
    X = XYP[:, 0]
    Y = XYP[:, 1]

    def calcCost(sign, A):
        ret = np.ones((1 << N, N), dtype=np.int64) * INF
        for state in range(1 << N):
            useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
            for i in range(N):
                xyp = XYP[i]
                cost = abs(xyp[sign])
                for z in useA:
                    cost = min(cost, abs(xyp[sign] - z))
                ret[state][i] = cost * xyp[2]
        return ret

    costX = calcCost(0, X)
    costY = calcCost(1, Y)
    ans = np.ones(N + 1, dtype=np.int64) * INF

    for state in range(pow(3, N)):
        stateX = 0
        stateY = 0
        M = N
        for _ in range(N):
            state, r = divmod(state, 3)
            stateX <<= 1
            stateY <<= 1
            if r == 0: M -= 1
            if r == 1: stateX |= 1
            if r == 2: stateY |= 1

        cx, cy = costX[stateX], costY[stateY]
        cost = 0
        for i in range(N):
            cost += min(cx[i], cy[i])
        ans[M] = min(ans[M], cost)

    for a in ans:
        print(a)

def sol():
    N = int(input())
    XYP = np.asarray([list(map(int, input().split())) for _ in range(N)])
    solve(N, XYP)

sol()

Buggy python submission (verdict: Time Limit Exceeded):
from itertools import product

MEM = [0] * (10**6)

N = int(input())
XYP = [tuple(map(int, input().split())) for _ in range(N)]
X = [x for x, _, _ in XYP]
Y = [y for _, y, _ in XYP]
INF = 10**18

def calcCost(AP, A):
    ret = [[INF] * N for _ in range(1 << N)]
    for state in range(1 << N):
        useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
        for i, (a, p) in enumerate(AP):
            cost = abs(a)
            for z in useA:
                cost = min(cost, abs(a - z))
            ret[state][i] = cost * p
    return ret

costX = calcCost([(x, p) for x, _, p in XYP], X)
costY = calcCost([(y, p) for _, y, p in XYP], Y)

def makeState(state, sign):
    ret = 0
    for s in state:
        ret <<= 1
        if s == sign:
            ret |= 1
    return ret

ans = [INF] * (N + 1)
for state in product((0, 1, 2), repeat=N):
    stateX = 0
    stateY = 0
    M = N
    for s in state:
        stateX <<= 1
        stateY <<= 1
        if s == 0: M -= 1
        if s == 1: stateX |= 1
        if s == 2: stateY |= 1

    cx = costX[stateX]
    cy = costY[stateY]

    cost = 0
    for i in range(N):
        cost += min(cx[i], cy[i])

    if ans[M] > cost:
        ans[M] = cost

print(*ans, sep='\n')


Fixed submission:
import sys
input = sys.stdin.buffer.readline
from numba import njit
import numpy as np
INF = 10**18

@njit('(i8, i8[:,:])')
def solve(N, XYP):
    X = XYP[:, 0]
    Y = XYP[:, 1]

    def calcCost(sign, A):
        ret = np.ones((1 << N, N), dtype=np.int64) * INF
        for state in range(1 << N):
            useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
            for i in range(N):
                xyp = XYP[i]
                cost = abs(xyp[sign])
                for z in useA:
                    cost = min(cost, abs(xyp[sign] - z))
                ret[state][i] = cost * xyp[2]
        return ret

    costX = calcCost(0, X)
    costY = calcCost(1, Y)
    ans = np.ones(N + 1, dtype=np.int64) * INF

    for state in range(pow(3, N)):
        stateX = 0
        stateY = 0
        M = N
        for _ in range(N):
            state, r = divmod(state, 3)
            stateX <<= 1
            stateY <<= 1
            if r == 0: M -= 1
            if r == 1: stateX |= 1
            if r == 2: stateY |= 1

        cx, cy = costX[stateX], costY[stateY]
        cost = 0
        for i in range(N):
            cost += min(cx[i], cy[i])
        ans[M] = min(ans[M], cost)

    for a in ans:
        print(a)

def sol():
    N = int(input())
    XYP = np.asarray([list(map(int, input().split())) for _ in range(N)])
    solve(N, XYP)

sol()

Buggy python submission (verdict: Time Limit Exceeded):
from itertools import product

def sol():
    N = int(input())
    XYP = [tuple(map(int, input().split())) for _ in range(N)]
    X = [x for x, _, _ in XYP]
    Y = [y for _, y, _ in XYP]
    INF = 10**18

    def calcCost(AP, A):
        ret = [[INF] * N for _ in range(1 << N)]
        for state in range(1 << N):
            useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
            for i, (a, p) in enumerate(AP):
                cost = abs(a)
                for z in useA:
                    cost = min(cost, abs(a - z))
                ret[state][i] = cost * p
        return ret

    costX = calcCost([(x, p) for x, _, p in XYP], X)
    costY = calcCost([(y, p) for _, y, p in XYP], Y)

    def makeState(state, sign):
        ret = 0
        for s in state:
            ret <<= 1
            if s == sign:
                ret |= 1
        return ret

    ans = [INF] * (N + 1)
    for state in product((0, 1, 2), repeat=N):
        stateX = 0
        stateY = 0
        M = N
        for s in state:
            stateX <<= 1
            stateY <<= 1
            if s == 0: M -= 1
            if s == 1: stateX |= 1
            if s == 2: stateY |= 1

        cx = costX[stateX]
        cy = costY[stateY]

        cost = 0
        for i in range(N):
            cost += min(cx[i], cy[i])

        if ans[M] > cost:
            ans[M] = cost

    print(*ans, sep='\n')

sol()

Fixed submission:
import sys
input = sys.stdin.buffer.readline
from numba import njit
import numpy as np
INF = 10**18

@njit('(i8, i8[:,:])')
def solve(N, XYP):
    X = XYP[:, 0]
    Y = XYP[:, 1]

    def calcCost(sign, A):
        ret = np.ones((1 << N, N), dtype=np.int64) * INF
        for state in range(1 << N):
            useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
            for i in range(N):
                xyp = XYP[i]
                cost = abs(xyp[sign])
                for z in useA:
                    cost = min(cost, abs(xyp[sign] - z))
                ret[state][i] = cost * xyp[2]
        return ret

    costX = calcCost(0, X)
    costY = calcCost(1, Y)
    ans = np.ones(N + 1, dtype=np.int64) * INF

    for state in range(pow(3, N)):
        stateX = 0
        stateY = 0
        M = N
        for _ in range(N):
            state, r = divmod(state, 3)
            stateX <<= 1
            stateY <<= 1
            if r == 0: M -= 1
            if r == 1: stateX |= 1
            if r == 2: stateY |= 1

        cx, cy = costX[stateX], costY[stateY]
        cost = 0
        for i in range(N):
            cost += min(cx[i], cy[i])
        ans[M] = min(ans[M], cost)

    for a in ans:
        print(a)

def sol():
    N = int(input())
    XYP = np.asarray([list(map(int, input().split())) for _ in range(N)])
    solve(N, XYP)

sol()

Buggy python submission (verdict: Time Limit Exceeded):
from itertools import product

N = int(input())
XYP = [tuple(map(int, input().split())) for _ in range(N)]
X = [x for x, _, _ in XYP]
Y = [y for _, y, _ in XYP]
INF = 10**18

def calcCost(AP, A):
    ret = [[INF] * N for _ in range(1 << N)]
    for state in range(1 << N):
        useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
        for i, (a, p) in enumerate(AP):
            cost = abs(a)
            for z in useA:
                cost = min(cost, abs(a - z))
            ret[state][i] = cost * p
    return ret

costX = calcCost([(x, p) for x, _, p in XYP], X)
costY = calcCost([(y, p) for _, y, p in XYP], Y)

def makeState(state, sign):
    ret = 0
    for s in state:
        ret <<= 1
        if s == sign:
            ret |= 1
    return ret

ans = [INF] * (N + 1)
for state in product((0, 1, 2), repeat=N):
    stateX = 0
    stateY = 0
    M = N
    for s in state:
        stateX <<= 1
        stateY <<= 1
        if s == 0: M -= 1
        if s == 1: stateX |= 1
        if s == 2: stateY |= 1

    cx = costX[stateX]
    cy = costY[stateY]

    cost = 0
    for i in range(N):
        cost += min(cx[i], cy[i])

    if ans[M] > cost:
        ans[M] = cost

print(*ans, sep='\n')


Fixed submission:
import sys
input = sys.stdin.buffer.readline
from numba import njit
import numpy as np
INF = 10**18

@njit('(i8, i8[:,:])')
def solve(N, XYP):
    X = XYP[:, 0]
    Y = XYP[:, 1]

    def calcCost(sign, A):
        ret = np.ones((1 << N, N), dtype=np.int64) * INF
        for state in range(1 << N):
            useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
            for i in range(N):
                xyp = XYP[i]
                cost = abs(xyp[sign])
                for z in useA:
                    cost = min(cost, abs(xyp[sign] - z))
                ret[state][i] = cost * xyp[2]
        return ret

    costX = calcCost(0, X)
    costY = calcCost(1, Y)
    ans = np.ones(N + 1, dtype=np.int64) * INF

    for state in range(pow(3, N)):
        stateX = 0
        stateY = 0
        M = N
        for _ in range(N):
            state, r = divmod(state, 3)
            stateX <<= 1
            stateY <<= 1
            if r == 0: M -= 1
            if r == 1: stateX |= 1
            if r == 2: stateY |= 1

        cx, cy = costX[stateX], costY[stateY]
        cost = 0
        for i in range(N):
            cost += min(cx[i], cy[i])
        ans[M] = min(ans[M], cost)

    for a in ans:
        print(a)

def sol():
    N = int(input())
    XYP = np.asarray([list(map(int, input().split())) for _ in range(N)])
    solve(N, XYP)

sol()

Buggy python submission (verdict: Time Limit Exceeded):
from itertools import product

N = int(input())
XYP = [tuple(map(int, input().split())) for _ in range(N)]
X = [x for x, _, _ in XYP]
Y = [y for _, y, _ in XYP]
INF = 10**18

def calcCost(AP, A):
    ret = [[INF] * N for _ in range(1 << N)]
    for state in range(1 << N):
        useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
        for i, (a, p) in enumerate(AP):
            cost = abs(a)
            for z in useA:
                cost = min(cost, abs(a - z))
            ret[state][i] = cost * p
    return ret

costX = calcCost([(x, p) for x, _, p in XYP], X)
costY = calcCost([(y, p) for _, y, p in XYP], Y)

def makeState(state, sign):
    ret = 0
    for s in state:
        ret <<= 1
        if s == sign:
            ret |= 1
    return ret

ans = [INF] * (N + 1)
for state in product((0, 1, 2), repeat=N):
    stateX = 0
    stateY = 0
    M = N
    for s in state:
        stateX <<= 1
        stateY <<= 1
        if s == 0: M -= 1
        if s == 1: stateX |= 1
        if s == 2: stateY |= 1

    cx = costX[stateX]
    cy = costY[stateY]

    cost = 0
    for i in range(N):
        cost += min(cx[i], cy[i])

    if ans[M] > cost:
        ans[M] = cost

print(*ans, sep='\n')


Fixed submission:
import sys
input = sys.stdin.buffer.readline
from numba import njit
import numpy as np
INF = 10**18

@njit('(i8, i8[:,:])')
def solve(N, XYP):
    X = XYP[:, 0]
    Y = XYP[:, 1]

    def calcCost(sign, A):
        ret = np.ones((1 << N, N), dtype=np.int64) * INF
        for state in range(1 << N):
            useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
            for i in range(N):
                xyp = XYP[i]
                cost = abs(xyp[sign])
                for z in useA:
                    cost = min(cost, abs(xyp[sign] - z))
                ret[state][i] = cost * xyp[2]
        return ret

    costX = calcCost(0, X)
    costY = calcCost(1, Y)
    ans = np.ones(N + 1, dtype=np.int64) * INF

    for state in range(pow(3, N)):
        stateX = 0
        stateY = 0
        M = N
        for _ in range(N):
            state, r = divmod(state, 3)
            stateX <<= 1
            stateY <<= 1
            if r == 0: M -= 1
            if r == 1: stateX |= 1
            if r == 2: stateY |= 1

        cx, cy = costX[stateX], costY[stateY]
        cost = 0
        for i in range(N):
            cost += min(cx[i], cy[i])
        ans[M] = min(ans[M], cost)

    for a in ans:
        print(a)

def sol():
    N = int(input())
    XYP = np.asarray([list(map(int, input().split())) for _ in range(N)])
    solve(N, XYP)

sol()

Buggy python submission (verdict: Time Limit Exceeded):
from itertools import product

N = int(input())
XYP = [tuple(map(int, input().split())) for _ in range(N)]
X = [x for x, _, _ in XYP]
Y = [y for _, y, _ in XYP]
INF = 10**18

def calcCost(AP, A):
    ret = [[INF] * N for _ in range(1 << N)]
    for state in range(1 << N):
        useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
        for i, (a, p) in enumerate(AP):
            cost = abs(a)
            for z in useA:
                cost = min(cost, abs(a - z))
            ret[state][i] = cost * p
    return ret

costX = calcCost([(x, p) for x, _, p in XYP], X)
costY = calcCost([(y, p) for _, y, p in XYP], Y)

def makeState(state, sign):
    ret = 0
    for s in state:
        ret <<= 1
        if s == sign:
            ret |= 1
    return ret

ans = [INF] * (N + 1)
for state in product((0, 1, 2), repeat=N):
    stateX = 0
    stateY = 0
    M = N
    for s in state:
        stateX <<= 1
        stateY <<= 1
        if s == 0: M -= 1
        if s == 1: stateX |= 1
        if s == 2: stateY |= 1

    cx = costX[stateX]
    cy = costY[stateY]

    cost = sum(min(x, y) for x, y in zip(cx, cy))

    if ans[M] > cost:
        ans[M] = cost

print(*ans, sep='\n')


Fixed submission:
import sys
input = sys.stdin.buffer.readline
from numba import njit
import numpy as np
INF = 10**18

@njit('(i8, i8[:,:])')
def solve(N, XYP):
    X = XYP[:, 0]
    Y = XYP[:, 1]

    def calcCost(sign, A):
        ret = np.ones((1 << N, N), dtype=np.int64) * INF
        for state in range(1 << N):
            useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
            for i in range(N):
                xyp = XYP[i]
                cost = abs(xyp[sign])
                for z in useA:
                    cost = min(cost, abs(xyp[sign] - z))
                ret[state][i] = cost * xyp[2]
        return ret

    costX = calcCost(0, X)
    costY = calcCost(1, Y)
    ans = np.ones(N + 1, dtype=np.int64) * INF

    for state in range(pow(3, N)):
        stateX = 0
        stateY = 0
        M = N
        for _ in range(N):
            state, r = divmod(state, 3)
            stateX <<= 1
            stateY <<= 1
            if r == 0: M -= 1
            if r == 1: stateX |= 1
            if r == 2: stateY |= 1

        cx, cy = costX[stateX], costY[stateY]
        cost = 0
        for i in range(N):
            cost += min(cx[i], cy[i])
        ans[M] = min(ans[M], cost)

    for a in ans:
        print(a)

def sol():
    N = int(input())
    XYP = np.asarray([list(map(int, input().split())) for _ in range(N)])
    solve(N, XYP)

sol()

Buggy python submission (verdict: Runtime Error):
from itertools import product

N = int(input())
XYP = [tuple(map(int, input().split())) for _ in range(N)]
X = [x for x, _, _ in XYP]
Y = [y for _, y, _ in XYP]
INF = 10**18

def calcCost(AP, A):
    ret = [[INF] * N for _ in range(1 << N)]
    for state in range(1 << N):
        useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
        for i, (a, p) in enumerate(AP):
            cost = abs(a)
            for z in useA:
                cost = min(cost, abs(a - z))
            ret[state][i] = cost * p
    return ret

costX = calcCost([(x, p) for x, _, p in XYP], X)
costY = calcCost([(y, p) for _, y, p in XYP], Y)

def makeState(state, sign):
    ret = 0
    for s in state:
        ret <<= 1
        if s == sign:
            ret |= 1
    return ret

ans = [INF] * (N + 1)
for state in product((0, 1, 2), repeat=N):
    stateX = 0
    stateY = 0
    M = N
    for s in state:
        stateX <<= 1
        stateY <<= 1
        if s == 0: N -= 1
        if s == 1: stateX |= 1
        if s == 2: stateY |= 1

    cx = costX[stateX]
    cy = costY[stateY]

    cost = sum(min(x, y) for x, y in zip(cx, cy))

    if ans[M] > cost:
        ans[M] = cost

print(*ans, sep='\n')


Fixed submission:
import sys
input = sys.stdin.buffer.readline
from numba import njit
import numpy as np
INF = 10**18

@njit('(i8, i8[:,:])')
def solve(N, XYP):
    X = XYP[:, 0]
    Y = XYP[:, 1]

    def calcCost(sign, A):
        ret = np.ones((1 << N, N), dtype=np.int64) * INF
        for state in range(1 << N):
            useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
            for i in range(N):
                xyp = XYP[i]
                cost = abs(xyp[sign])
                for z in useA:
                    cost = min(cost, abs(xyp[sign] - z))
                ret[state][i] = cost * xyp[2]
        return ret

    costX = calcCost(0, X)
    costY = calcCost(1, Y)
    ans = np.ones(N + 1, dtype=np.int64) * INF

    for state in range(pow(3, N)):
        stateX = 0
        stateY = 0
        M = N
        for _ in range(N):
            state, r = divmod(state, 3)
            stateX <<= 1
            stateY <<= 1
            if r == 0: M -= 1
            if r == 1: stateX |= 1
            if r == 2: stateY |= 1

        cx, cy = costX[stateX], costY[stateY]
        cost = 0
        for i in range(N):
            cost += min(cx[i], cy[i])
        ans[M] = min(ans[M], cost)

    for a in ans:
        print(a)

def sol():
    N = int(input())
    XYP = np.asarray([list(map(int, input().split())) for _ in range(N)])
    solve(N, XYP)

sol()

Buggy python submission (verdict: Time Limit Exceeded):
from itertools import product

N = int(input())
XYP = [tuple(map(int, input().split())) for _ in range(N)]
X = [x for x, _, _ in XYP]
Y = [y for _, y, _ in XYP]
INF = 10**18

def calcCost(AP, A):
    ret = [[INF] * N for _ in range(1 << N)]
    for state in range(1 << N):
        useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
        for i, (a, p) in enumerate(AP):
            cost = abs(a)
            for z in useA:
                cost = min(cost, abs(a - z))
            ret[state][i] = cost * p
    return ret

costX = calcCost([(x, p) for x, _, p in XYP], X)
costY = calcCost([(y, p) for _, y, p in XYP], Y)

def makeState(state, sign):
    ret = 0
    for s in state:
        ret <<= 1
        if s == sign:
            ret |= 1
    return ret

ans = [INF] * (N + 1)
for state in product((0, 1, 2), repeat=N):
    cx = costX[makeState(state, 1)]
    cy = costY[makeState(state, 2)]

    cost = sum(min(x, y) for x, y in zip(cx, cy))

    M = N - state.count(0)
    if ans[M] > cost:
        ans[M] = cost

print(*ans, sep='\n')


Fixed submission:
import sys
input = sys.stdin.buffer.readline
from numba import njit
import numpy as np
INF = 10**18

@njit('(i8, i8[:,:])')
def solve(N, XYP):
    X = XYP[:, 0]
    Y = XYP[:, 1]

    def calcCost(sign, A):
        ret = np.ones((1 << N, N), dtype=np.int64) * INF
        for state in range(1 << N):
            useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
            for i in range(N):
                xyp = XYP[i]
                cost = abs(xyp[sign])
                for z in useA:
                    cost = min(cost, abs(xyp[sign] - z))
                ret[state][i] = cost * xyp[2]
        return ret

    costX = calcCost(0, X)
    costY = calcCost(1, Y)
    ans = np.ones(N + 1, dtype=np.int64) * INF

    for state in range(pow(3, N)):
        stateX = 0
        stateY = 0
        M = N
        for _ in range(N):
            state, r = divmod(state, 3)
            stateX <<= 1
            stateY <<= 1
            if r == 0: M -= 1
            if r == 1: stateX |= 1
            if r == 2: stateY |= 1

        cx, cy = costX[stateX], costY[stateY]
        cost = 0
        for i in range(N):
            cost += min(cx[i], cy[i])
        ans[M] = min(ans[M], cost)

    for a in ans:
        print(a)

def sol():
    N = int(input())
    XYP = np.asarray([list(map(int, input().split())) for _ in range(N)])
    solve(N, XYP)

sol()

Buggy python submission (verdict: Time Limit Exceeded):
from itertools import product

def sol():
    N = int(input())
    XYP = [tuple(map(int, input().split())) for _ in range(N)]
    X = [x for x, _, _ in XYP]
    Y = [y for _, y, _ in XYP]
    INF = 10**18

    def calcCost(AP, A):
        ret = [[INF] * N for _ in range(1 << N)]
        for state in range(1 << N):
            useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
            for i, (a, p) in enumerate(AP):
                cost = abs(a)
                for z in useA:
                    cost = min(cost, abs(a - z))
                ret[state][i] = cost * p
        return ret

    costX = calcCost([(x, p) for x, _, p in XYP], X)
    costY = calcCost([(y, p) for _, y, p in XYP], Y)

    def makeState(state, sign):
        ret = 0
        for s in state[::-1]:
            ret <<= 1
            if s == sign:
                ret |= 1
        return ret

    ans = [INF] * (N + 1)
    for state in product((0, 1, 2), repeat=N):
        stateX = makeState(state, 1)
        stateY = makeState(state, 2)

        cost = 0
        for i in range(N):
            cost += min(costX[stateX][i], costY[stateY][i])

        M = N - state.count(0)
        if ans[M] > cost:
            ans[M] = cost

    print(*ans, sep='\n')

sol()

Fixed submission:
import sys
input = sys.stdin.buffer.readline
from numba import njit
import numpy as np
INF = 10**18

@njit('(i8, i8[:,:])')
def solve(N, XYP):
    X = XYP[:, 0]
    Y = XYP[:, 1]

    def calcCost(sign, A):
        ret = np.ones((1 << N, N), dtype=np.int64) * INF
        for state in range(1 << N):
            useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
            for i in range(N):
                xyp = XYP[i]
                cost = abs(xyp[sign])
                for z in useA:
                    cost = min(cost, abs(xyp[sign] - z))
                ret[state][i] = cost * xyp[2]
        return ret

    costX = calcCost(0, X)
    costY = calcCost(1, Y)
    ans = np.ones(N + 1, dtype=np.int64) * INF

    for state in range(pow(3, N)):
        stateX = 0
        stateY = 0
        M = N
        for _ in range(N):
            state, r = divmod(state, 3)
            stateX <<= 1
            stateY <<= 1
            if r == 0: M -= 1
            if r == 1: stateX |= 1
            if r == 2: stateY |= 1

        cx, cy = costX[stateX], costY[stateY]
        cost = 0
        for i in range(N):
            cost += min(cx[i], cy[i])
        ans[M] = min(ans[M], cost)

    for a in ans:
        print(a)

def sol():
    N = int(input())
    XYP = np.asarray([list(map(int, input().split())) for _ in range(N)])
    solve(N, XYP)

sol()

Buggy python submission (verdict: Time Limit Exceeded):
from itertools import product

def sol():
    N = int(input())
    XYP = [tuple(map(int, input().split())) for _ in range(N)]
    X = [x for x, _, _ in XYP]
    Y = [y for _, y, _ in XYP]
    INF = 10**18

    def calcCost(AP, A):
        ret = [[INF] * N for _ in range(1 << N)]
        for state in range(1 << N):
            useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
            for i, (a, p) in enumerate(AP):
                cost = abs(a)
                for z in useA:
                    cost = min(cost, abs(a - z))
                ret[state][i] = cost * p
        return ret

    costX = calcCost([(x, p) for x, _, p in XYP], X)
    costY = calcCost([(y, p) for _, y, p in XYP], Y)

    def makeState(state, sign):
        ret = 0
        for s in state[::-1]:
            ret <<= 1
            if s == sign:
                ret |= 1
        return ret

    ans = [INF] * (N + 1)
    for state in product((0, 1, 2), repeat=N):
        stateX = makeState(state, 1)
        stateY = makeState(state, 2)

        cost = 0
        for i in range(N):
            cost += min(costX[stateX][i], costY[stateY][i])

        M = N - state.count(0)
        if ans[M] > cost:
            ans[M] = cost

    print(*ans, sep='\n')

sol()

Fixed submission:
import sys
input = sys.stdin.buffer.readline
from numba import njit
import numpy as np
INF = 10**18

@njit('(i8, i8[:,:])')
def solve(N, XYP):
    X = XYP[:, 0]
    Y = XYP[:, 1]

    def calcCost(sign, A):
        ret = np.ones((1 << N, N), dtype=np.int64) * INF
        for state in range(1 << N):
            useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
            for i in range(N):
                xyp = XYP[i]
                cost = abs(xyp[sign])
                for z in useA:
                    cost = min(cost, abs(xyp[sign] - z))
                ret[state][i] = cost * xyp[2]
        return ret

    costX = calcCost(0, X)
    costY = calcCost(1, Y)
    ans = np.ones(N + 1, dtype=np.int64) * INF

    for state in range(pow(3, N)):
        stateX = 0
        stateY = 0
        M = N
        for _ in range(N):
            state, r = divmod(state, 3)
            stateX <<= 1
            stateY <<= 1
            if r == 0: M -= 1
            if r == 1: stateX |= 1
            if r == 2: stateY |= 1

        cx, cy = costX[stateX], costY[stateY]
        cost = 0
        for i in range(N):
            cost += min(cx[i], cy[i])
        ans[M] = min(ans[M], cost)

    for a in ans:
        print(a)

def sol():
    N = int(input())
    XYP = np.asarray([list(map(int, input().split())) for _ in range(N)])
    solve(N, XYP)

sol()

Buggy python submission (verdict: Time Limit Exceeded):
def sol():
    N = int(input())
    XYP = [tuple(map(int, input().split())) for _ in range(N)]
    X = [x for x, _, _ in XYP]
    Y = [y for _, y, _ in XYP]
    INF = 10**18

    def calcCost(AP, A):
        ret = [[INF] * N for _ in range(1 << N)]
        for state in range(1 << N):
            useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
            for i, (a, p) in enumerate(AP):
                cost = abs(a)
                for z in useA:
                    cost = min(cost, abs(a - z))
                ret[state][i] = cost * p
        return ret

    costX = calcCost([(x, p) for x, _, p in XYP], X)
    costY = calcCost([(y, p) for _, y, p in XYP], Y)

    def makeState(state, sign):
        ret = 0
        for s in state[::-1]:
            ret <<= 1
            if s == sign:
                ret |= 1
        return ret

    ans = [INF] * (N + 1)
    for state in range(pow(3, N)):
        stateX = 0
        stateY = 0
        M = N
        for _ in range(N):
            state, r = divmod(state, 3)
            stateX <<= 1
            stateY <<= 1
            if r == 0: M -= 1
            if r == 1: stateX |= 1
            if r == 2: stateY |= 1

        cost = 0
        for i in range(N):
            cost += min(costX[stateX][i], costY[stateY][i])

        if ans[M] > cost:
            ans[M] = cost

    print(*ans, sep='\n')

sol()

Fixed submission:
import sys
input = sys.stdin.buffer.readline
from numba import njit
import numpy as np
INF = 10**18

@njit('(i8, i8[:,:])')
def solve(N, XYP):
    X = XYP[:, 0]
    Y = XYP[:, 1]

    def calcCost(sign, A):
        ret = np.ones((1 << N, N), dtype=np.int64) * INF
        for state in range(1 << N):
            useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
            for i in range(N):
                xyp = XYP[i]
                cost = abs(xyp[sign])
                for z in useA:
                    cost = min(cost, abs(xyp[sign] - z))
                ret[state][i] = cost * xyp[2]
        return ret

    costX = calcCost(0, X)
    costY = calcCost(1, Y)
    ans = np.ones(N + 1, dtype=np.int64) * INF

    for state in range(pow(3, N)):
        stateX = 0
        stateY = 0
        M = N
        for _ in range(N):
            state, r = divmod(state, 3)
            stateX <<= 1
            stateY <<= 1
            if r == 0: M -= 1
            if r == 1: stateX |= 1
            if r == 2: stateY |= 1

        cx, cy = costX[stateX], costY[stateY]
        cost = 0
        for i in range(N):
            cost += min(cx[i], cy[i])
        ans[M] = min(ans[M], cost)

    for a in ans:
        print(a)

def sol():
    N = int(input())
    XYP = np.asarray([list(map(int, input().split())) for _ in range(N)])
    solve(N, XYP)

sol()

Buggy python submission (verdict: Time Limit Exceeded):
from itertools import product

N = int(input())
XYP = [tuple(map(int, input().split())) for _ in range(N)]
X = [x for x, _, _ in XYP]
Y = [y for _, y, _ in XYP]
INF = 10**18

def calcCost(AP, A):
    ret = [[INF] * N for _ in range(1 << N)]
    for state in range(1 << N):
        useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
        for i, (a, p) in enumerate(AP):
            cost = abs(a)
            for z in useA:
                cost = min(cost, abs(a - z))
            ret[state][i] = cost * p
    return ret

costX = calcCost([(x, p) for x, _, p in XYP], X)
costY = calcCost([(y, p) for _, y, p in XYP], Y)

def makeState(state, sign):
    ret = 0
    for s in state[::-1]:
        ret <<= 1
        if s == sign:
            ret |= 1
    return ret

ans = [INF] * (N + 1)
for state in range(pow(3, N)):
    stateX = 0
    stateY = 0
    M = N
    for _ in range(N):
        state, r = divmod(state, 3)
        stateX <<= 1
        stateY <<= 1
        if r == 0: M -= 1
        if r == 1: stateX |= 1
        if r == 2: stateY |= 1

    cost = 0
    for i in range(N):
        cost += min(costX[stateX][i], costY[stateY][i])

    if ans[M] > cost:
        ans[M] = cost

print(*ans, sep='\n')


Fixed submission:
import sys
input = sys.stdin.buffer.readline
from numba import njit
import numpy as np
INF = 10**18

@njit('(i8, i8[:,:])')
def solve(N, XYP):
    X = XYP[:, 0]
    Y = XYP[:, 1]

    def calcCost(sign, A):
        ret = np.ones((1 << N, N), dtype=np.int64) * INF
        for state in range(1 << N):
            useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
            for i in range(N):
                xyp = XYP[i]
                cost = abs(xyp[sign])
                for z in useA:
                    cost = min(cost, abs(xyp[sign] - z))
                ret[state][i] = cost * xyp[2]
        return ret

    costX = calcCost(0, X)
    costY = calcCost(1, Y)
    ans = np.ones(N + 1, dtype=np.int64) * INF

    for state in range(pow(3, N)):
        stateX = 0
        stateY = 0
        M = N
        for _ in range(N):
            state, r = divmod(state, 3)
            stateX <<= 1
            stateY <<= 1
            if r == 0: M -= 1
            if r == 1: stateX |= 1
            if r == 2: stateY |= 1

        cx, cy = costX[stateX], costY[stateY]
        cost = 0
        for i in range(N):
            cost += min(cx[i], cy[i])
        ans[M] = min(ans[M], cost)

    for a in ans:
        print(a)

def sol():
    N = int(input())
    XYP = np.asarray([list(map(int, input().split())) for _ in range(N)])
    solve(N, XYP)

sol()

Buggy python submission (verdict: Time Limit Exceeded):
from itertools import product

N = int(input())
XYP = [tuple(map(int, input().split())) for _ in range(N)]
X = [x for x, _, _ in XYP]
Y = [y for _, y, _ in XYP]
INF = 10**18

def calcCost(AP, A):
    ret = [[INF] * N for _ in range(1 << N)]
    for state in range(1 << N):
        useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
        for i, (a, p) in enumerate(AP):
            cost = abs(a)
            for z in useA:
                cost = min(cost, abs(a - z))
            ret[state][i] = cost * p
    return ret

costX = calcCost([(x, p) for x, _, p in XYP], X)
costY = calcCost([(y, p) for _, y, p in XYP], Y)

def makeState(state, sign):
    ret = 0
    for s in state[::-1]:
        ret <<= 1
        if s == sign:
            ret |= 1
    return ret

ans = [INF] * (N + 1)
for state in range(pow(3, N)):
    stateX = 0
    stateY = 0
    M = N
    for _ in range(N):
        state, r = divmod(state, 3)
        stateX <<= 1
        stateY <<= 1
        if r == 0: M -= 1
        if r == 1: stateX |= 1
        if r == 2: stateY |= 1

    cost = 0
    for i in range(N):
        cost += min(costX[stateX][i], costY[stateY][i])

    if ans[M] > cost:
        ans[M] = cost

print(*ans, sep='\n')


Fixed submission:
import sys
input = sys.stdin.buffer.readline
from numba import njit
import numpy as np
INF = 10**18

@njit('(i8, i8[:,:])')
def solve(N, XYP):
    X = XYP[:, 0]
    Y = XYP[:, 1]

    def calcCost(sign, A):
        ret = np.ones((1 << N, N), dtype=np.int64) * INF
        for state in range(1 << N):
            useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
            for i in range(N):
                xyp = XYP[i]
                cost = abs(xyp[sign])
                for z in useA:
                    cost = min(cost, abs(xyp[sign] - z))
                ret[state][i] = cost * xyp[2]
        return ret

    costX = calcCost(0, X)
    costY = calcCost(1, Y)
    ans = np.ones(N + 1, dtype=np.int64) * INF

    for state in range(pow(3, N)):
        stateX = 0
        stateY = 0
        M = N
        for _ in range(N):
            state, r = divmod(state, 3)
            stateX <<= 1
            stateY <<= 1
            if r == 0: M -= 1
            if r == 1: stateX |= 1
            if r == 2: stateY |= 1

        cx, cy = costX[stateX], costY[stateY]
        cost = 0
        for i in range(N):
            cost += min(cx[i], cy[i])
        ans[M] = min(ans[M], cost)

    for a in ans:
        print(a)

def sol():
    N = int(input())
    XYP = np.asarray([list(map(int, input().split())) for _ in range(N)])
    solve(N, XYP)

sol()

Buggy python submission (verdict: Time Limit Exceeded):
from itertools import product

N = int(input())
XYP = [tuple(map(int, input().split())) for _ in range(N)]
X = [x for x, _, _ in XYP]
Y = [y for _, y, _ in XYP]
INF = 10**18

def calcCost(AP, A):
    ret = [[INF] * N for _ in range(1 << N)]
    for state in range(1 << N):
        useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
        for i, (a, p) in enumerate(AP):
            cost = abs(a)
            for z in useA:
                cost = min(cost, abs(a - z))
            ret[state][i] = cost * p
    return ret

costX = calcCost([(x, p) for x, _, p in XYP], X)
costY = calcCost([(y, p) for _, y, p in XYP], Y)

def makeState(state, sign):
    ret = 0
    for s in state[::-1]:
        ret <<= 1
        if s == sign:
            ret |= 1
    return ret

ans = [INF] * (N + 1)
for state in product((0, 1, 2), repeat=N):
    stateX = makeState(state, 1)
    stateY = makeState(state, 2)

    cost = 0
    for i, (x, y, p) in enumerate(XYP):
        cost += min(costX[stateX][i], costY[stateY][i])

    M = N - state.count(0)
    if ans[M] > cost:
        ans[M] = cost

print(*ans, sep='\n')


Fixed submission:
import sys
input = sys.stdin.buffer.readline
from numba import njit
import numpy as np
INF = 10**18

@njit('(i8, i8[:,:])')
def solve(N, XYP):
    X = XYP[:, 0]
    Y = XYP[:, 1]

    def calcCost(sign, A):
        ret = np.ones((1 << N, N), dtype=np.int64) * INF
        for state in range(1 << N):
            useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
            for i in range(N):
                xyp = XYP[i]
                cost = abs(xyp[sign])
                for z in useA:
                    cost = min(cost, abs(xyp[sign] - z))
                ret[state][i] = cost * xyp[2]
        return ret

    costX = calcCost(0, X)
    costY = calcCost(1, Y)
    ans = np.ones(N + 1, dtype=np.int64) * INF

    for state in range(pow(3, N)):
        stateX = 0
        stateY = 0
        M = N
        for _ in range(N):
            state, r = divmod(state, 3)
            stateX <<= 1
            stateY <<= 1
            if r == 0: M -= 1
            if r == 1: stateX |= 1
            if r == 2: stateY |= 1

        cx, cy = costX[stateX], costY[stateY]
        cost = 0
        for i in range(N):
            cost += min(cx[i], cy[i])
        ans[M] = min(ans[M], cost)

    for a in ans:
        print(a)

def sol():
    N = int(input())
    XYP = np.asarray([list(map(int, input().split())) for _ in range(N)])
    solve(N, XYP)

sol()

Buggy python submission (verdict: Time Limit Exceeded):
from itertools import product

N = int(input())
XYP = [tuple(map(int, input().split())) for _ in range(N)]
X = [x for x, _, _ in XYP]
Y = [y for _, y, _ in XYP]
INF = 10**18

def calcCost(AP, A):
    ret = [[INF] * N for _ in range(1 << N)]
    for state in range(1 << N):
        useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
        for i, (a, p) in enumerate(AP):
            cost = abs(a)
            for z in useA:
                cost = min(cost, abs(a - z))
            ret[state][i] = cost * p
    return ret

costX = calcCost([(x, p) for x, _, p in XYP], X)
costY = calcCost([(y, p) for _, y, p in XYP], Y)

def makeState(state, sign):
    ret = 0
    for s in state[::-1]:
        ret <<= 1
        if s == sign:
            ret |= 1
    return ret

ans = [INF] * (N + 1)
for state in product((0, 1, 2), repeat=N):
    useX = set([x for i, x in enumerate(X) if state[i] == 1])
    useY = set([y for i, y in enumerate(Y) if state[i] == 2])

    stateX = makeState(state, 1)
    stateY = makeState(state, 2)

    cost = 0
    for i, (x, y, p) in enumerate(XYP):
        if x in useX or y in useY:
            continue
        cost += min(costX[stateX][i], costY[stateY][i])

    M = len(useX) + len(useY)
    if ans[M] > cost:
        ans[M] = cost

print(*ans, sep='\n')


Fixed submission:
import sys
input = sys.stdin.buffer.readline
from numba import njit
import numpy as np
INF = 10**18

@njit('(i8, i8[:,:])')
def solve(N, XYP):
    X = XYP[:, 0]
    Y = XYP[:, 1]

    def calcCost(sign, A):
        ret = np.ones((1 << N, N), dtype=np.int64) * INF
        for state in range(1 << N):
            useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
            for i in range(N):
                xyp = XYP[i]
                cost = abs(xyp[sign])
                for z in useA:
                    cost = min(cost, abs(xyp[sign] - z))
                ret[state][i] = cost * xyp[2]
        return ret

    costX = calcCost(0, X)
    costY = calcCost(1, Y)
    ans = np.ones(N + 1, dtype=np.int64) * INF

    for state in range(pow(3, N)):
        stateX = 0
        stateY = 0
        M = N
        for _ in range(N):
            state, r = divmod(state, 3)
            stateX <<= 1
            stateY <<= 1
            if r == 0: M -= 1
            if r == 1: stateX |= 1
            if r == 2: stateY |= 1

        cx, cy = costX[stateX], costY[stateY]
        cost = 0
        for i in range(N):
            cost += min(cx[i], cy[i])
        ans[M] = min(ans[M], cost)

    for a in ans:
        print(a)

def sol():
    N = int(input())
    XYP = np.asarray([list(map(int, input().split())) for _ in range(N)])
    solve(N, XYP)

sol()

Buggy python submission (verdict: Time Limit Exceeded):
from itertools import product

N = int(input())
XYP = [tuple(map(int, input().split())) for _ in range(N)]
X = [x for x, _, _ in XYP]
Y = [y for _, y, _ in XYP]
INF = 10**18

def calcCost(AP, A):
    ret = [[INF] * N for _ in range(1 << N)]
    for state in range(1 << N):
        useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
        for i, (a, p) in enumerate(AP):
            cost = abs(a)
            for z in useA:
                cost = min(cost, abs(a - z))
            ret[state][i] = cost * p
    return ret

costX = calcCost([(x, p) for x, _, p in XYP], X)
costY = calcCost([(y, p) for _, y, p in XYP], Y)

def makeState(state, sign):
    ret = 0
    for s in state[::-1]:
        ret <<= 1
        if s == sign:
            ret |= 1
    return ret

ans = [INF] * (N + 1)
for state in product((0, 1, 2), repeat=N):
    useX = set([x for i, x in enumerate(X) if state[i] == 1])
    useY = set([y for i, y in enumerate(Y) if state[i] == 2])

    stateX = makeState(state, 1)
    stateY = makeState(state, 2)

    cost = 0
    for i, (x, y, p) in enumerate(XYP):
        if x in useX or y in useY:
            continue
        cost += min(costX[stateX][i], costY[stateY][i])

    M = len(useX) + len(useY)
    if ans[M] > cost:
        ans[M] = cost

print(*ans, sep='\n')


Fixed submission:
import sys
input = sys.stdin.buffer.readline
from numba import njit
import numpy as np
INF = 10**18

@njit('(i8, i8[:,:])')
def solve(N, XYP):
    X = XYP[:, 0]
    Y = XYP[:, 1]

    def calcCost(sign, A):
        ret = np.ones((1 << N, N), dtype=np.int64) * INF
        for state in range(1 << N):
            useA = [a for i, a in enumerate(A) if (state & (1 << i)) != 0]
            for i in range(N):
                xyp = XYP[i]
                cost = abs(xyp[sign])
                for z in useA:
                    cost = min(cost, abs(xyp[sign] - z))
                ret[state][i] = cost * xyp[2]
        return ret

    costX = calcCost(0, X)
    costY = calcCost(1, Y)
    ans = np.ones(N + 1, dtype=np.int64) * INF

    for state in range(pow(3, N)):
        stateX = 0
        stateY = 0
        M = N
        for _ in range(N):
            state, r = divmod(state, 3)
            stateX <<= 1
            stateY <<= 1
            if r == 0: M -= 1
            if r == 1: stateX |= 1
            if r == 2: stateY |= 1

        cx, cy = costX[stateX], costY[stateY]
        cost = 0
        for i in range(N):
            cost += min(cx[i], cy[i])
        ans[M] = min(ans[M], cost)

    for a in ans:
        print(a)

def sol():
    N = int(input())
    XYP = np.asarray([list(map(int, input().split())) for _ in range(N)])
    solve(N, XYP)

sol()