Problem:
 Score : 100 points    Problem Statement You are given a grid of N rows and M columns. The square at the i -th row and j -th column will be denoted as (i,j) .
A nonnegative integer A_{i,j} is written for each square (i,j) .  You choose some of the squares so that each row and column contains at most K chosen squares.
Under this constraint, calculate the maximum value of the sum of the integers written on the chosen squares.
Additionally, calculate a way to choose squares that acheives the maximum.      Constraints  1 \leq N \leq 50  1 \leq K \leq N  0 \leq A_{i,j} \leq 10^9  All values in Input are integer.         Input Input is given from Standard Input in the following format:  N  K  A_{1,1}  A_{1,2}  \cdots  A_{1,N}  A_{2,1}  A_{2,2}  \cdots  A_{2,N}  \vdots  A_{N,1}  A_{N,2}  \cdots  A_{N,N}       Output On the first line, print the maximum value of the sum of the integers written on the chosen squares.  On the next N lines, print a way that achieves the maximum.  Precisely, output the strings t_1,t_2,\cdots,t_N , that satisfies t_{i,j}= X if you choose (i,j) and t_{i,j}= . otherwise.  You may print any way to choose squares that maximizes the sum.        Sample Input 1 3 1
5 3 2
1 4 8
7 6 9      Sample Output 1 19
X..
..X
.X.       Sample Input 2 3 2
10 10 1
10 10 1
1 1 10      Sample Output 2 50
XX.
XX.
..X

Solution (Python):
from __future__ import annotations

from typing import NamedTuple, Optional, List
from heapq import heappush, heappop


class MCFGraph:
    class Edge(NamedTuple):
        src: int
        dst: int
        cap: int
        flow: int
        cost: int

    class _Edge:
        def __init__(self, dst: int, cap: int, cost: int) -> None:
            self.dst = dst
            self.cap = cap
            self.cost = cost
            self.rev: Optional[MCFGraph._Edge] = None

    def __init__(self, n: int) -> None:
        self._n = n
        self._g: List[List[MCFGraph._Edge]] = [[] for _ in range(n)]
        self._edges: List[MCFGraph._Edge] = []

    def add_edge(self, src: int, dst: int, cap: int, cost: int) -> int:
        assert 0 <= src < self._n
        assert 0 <= dst < self._n
        assert 0 <= cap
        m = len(self._edges)
        e = MCFGraph._Edge(dst, cap, cost)
        re = MCFGraph._Edge(src, 0, -cost)
        e.rev = re
        re.rev = e
        self._g[src].append(e)
        self._g[dst].append(re)
        self._edges.append(e)
        return m

    def get_edge(self, i: int) -> Edge:
        assert 0 <= i < len(self._edges)
        e = self._edges[i]
        re = e.rev
        return MCFGraph.Edge(
            re.dst,
            e.dst,
            e.cap + re.cap,
            re.cap,
            e.cost
        )

    def edges(self) -> List[Edge]:
        return [self.get_edge(i) for i in range(len(self._edges))]

    def flow(self, s: int, t: int, flow_limit: Optional[int] = None) -> (int, int):
        return self.slope(s, t, flow_limit)[-1]

    def slope(self, s: int, t: int, flow_limit: Optional[int] = None) -> List[(int, int)]:
        assert 0 <= s < self._n
        assert 0 <= t < self._n
        assert s != t
        if flow_limit is None:
            flow_limit = sum(e.cap for e in self._g[s])

        dual = [0] * self._n
        prev: List[Optional[(int, MCFGraph._Edge)]] = [None] * self._n

        def refine_dual() -> bool:
            pq = [(0, s)]
            visited = [False] * self._n
            dist: List[Optional[int]] = [None] * self._n
            dist[s] = 0
            while pq:
                (dist_v, v) = heappop(pq)
                if visited[v]:
                    continue
                visited[v] = True
                if v == t:
                    break
                dual_v = dual[v]
                for e in self._g[v]:
                    w = e.dst
                    if visited[w] or e.cap == 0:
                        continue
                    reduced_cost = e.cost - dual[w] + dual_v
                    new_dist = dist_v + reduced_cost
                    dist_w = dist[w]
                    if dist_w is None or new_dist < dist_w:
                        dist[w] = new_dist
                        prev[w] = (v, e)
                        heappush(pq, (new_dist, w))
            else:
                return False
            dist_t = dist[t]
            for v in range(self._n):
                if visited[v]:
                    dual[v] -= dist_t - dist[v]
            return True

        flow = 0
        cost = 0
        prev_cost_per_flow: Optional[int] = None
        result = [(flow, cost)]
        while flow < flow_limit:
            if not refine_dual():
                break
            f = flow_limit - flow
            v = t
            while prev[v] is not None:
                (u, e) = prev[v]
                f = min(f, e.cap)
                v = u
            v = t
            while prev[v] is not None:
                (u, e) = prev[v]
                e.cap -= f
                e.rev.cap += f
                v = u
            c = -dual[s]
            flow += f
            cost += f * c
            if c == prev_cost_per_flow:
                result.pop()
            result.append((flow, cost))
            prev_cost_per_flow = c
        return result



def main() -> None:
    import sys
    n, k = map(int, sys.stdin.readline().split())
    s = n * 2
    t = s + 1
    g = MCFGraph(t + 1)
    grid = [list(map(int, sys.stdin.readline().split())) for _ in range(n)]
    big = 10 ** 9
    edges = list([] for _ in range(n))
    for i in range(n):
        g.add_edge(s, i, k, 0)
        g.add_edge(i + n, t, k, 0)
        for j in range(n):
            edges[i].append(g.add_edge(i, j + n, 1, big - grid[i][j]))
    g.add_edge(s, t, n * k, big)

    cost = g.flow(s, t, n * k)[1]
    print(big * n * k - cost)

    result = list(['.'] * n for _ in range(n))
    for i in range(n):
        for j in range(n):
            if g.get_edge(edges[i][j]).flow > 0:
                result[i][j] = 'X'

    for s in result:
        print("".join(s))


if __name__ == '__main__':
    main()

Solution (Python):
from __future__ import annotations

from typing import NamedTuple, Optional, List
from heapq import heappush, heappop


class MCFGraph:
    class Edge(NamedTuple):
        src: int
        dst: int
        cap: int
        flow: int
        cost: int

    class _Edge:
        def __init__(self, dst: int, cap: int, cost: int) -> None:
            self.dst = dst
            self.cap = cap
            self.cost = cost
            self.rev: Optional[MCFGraph._Edge] = None

    def __init__(self, n: int) -> None:
        self._n = n
        self._g: List[List[MCFGraph._Edge]] = [[] for _ in range(n)]
        self._edges: List[MCFGraph._Edge] = []

    def add_edge(self, src: int, dst: int, cap: int, cost: int) -> int:
        assert 0 <= src < self._n
        assert 0 <= dst < self._n
        assert 0 <= cap
        m = len(self._edges)
        e = MCFGraph._Edge(dst, cap, cost)
        re = MCFGraph._Edge(src, 0, -cost)
        e.rev = re
        re.rev = e
        self._g[src].append(e)
        self._g[dst].append(re)
        self._edges.append(e)
        return m

    def get_edge(self, i: int) -> Edge:
        assert 0 <= i < len(self._edges)
        e = self._edges[i]
        re = e.rev
        return MCFGraph.Edge(
            re.dst,
            e.dst,
            e.cap + re.cap,
            re.cap,
            e.cost
        )

    def edges(self) -> List[Edge]:
        return [self.get_edge(i) for i in range(len(self._edges))]

    def flow(self, s: int, t: int, flow_limit: Optional[int] = None) -> (int, int):
        return self.slope(s, t, flow_limit)[-1]

    def slope(self, s: int, t: int, flow_limit: Optional[int] = None) -> List[(int, int)]:
        assert 0 <= s < self._n
        assert 0 <= t < self._n
        assert s != t
        if flow_limit is None:
            flow_limit = sum(e.cap for e in self._g[s])

        dual = [0] * self._n
        prev: List[Optional[(int, MCFGraph._Edge)]] = [None] * self._n

        def refine_dual() -> bool:
            pq = [(0, s)]
            visited = [False] * self._n
            dist: List[Optional[int]] = [None] * self._n
            dist[s] = 0
            while pq:
                (_, v) = heappop(pq)
                if visited[v]:
                    continue
                visited[v] = True
                if v == t:
                    break
                for e in self._g[v]:
                    if visited[e.dst] or e.cap == 0:
                        continue
                    reduced_cost = e.cost - dual[e.dst] + dual[v]
                    new_dist = dist[v] + reduced_cost
                    if dist[e.dst] is None or new_dist < dist[e.dst]:
                        dist[e.dst] = new_dist
                        prev[e.dst] = (v, e)
                        heappush(pq, (dist[e.dst], e.dst))
            else:
                return False
            for v in range(self._n):
                if visited[v]:
                    dual[v] -= dist[t] - dist[v]
            return True

        flow = 0
        cost = 0
        prev_cost_per_flow: Optional[int] = None
        result = [(flow, cost)]
        while flow < flow_limit:
            if not refine_dual():
                break
            f = flow_limit - flow
            v = t
            while prev[v] is not None:
                (u, e) = prev[v]
                f = min(f, e.cap)
                v = u
            v = t
            while prev[v] is not None:
                (u, e) = prev[v]
                e.cap -= f
                e.rev.cap += f
                v = u
            c = -dual[s]
            flow += f
            cost += f * c
            if c == prev_cost_per_flow:
                result.pop()
            result.append((flow, cost))
            prev_cost_per_flow = c
        return result




def main() -> None:
    import sys
    n, k = map(int, sys.stdin.readline().split())
    s = n * 2
    t = s + 1
    g = MCFGraph(t + 1)
    grid = [list(map(int, sys.stdin.readline().split())) for _ in range(n)]
    big = 10 ** 9
    edges = list([] for _ in range(n))
    for i in range(n):
        g.add_edge(s, i, k, 0)
        g.add_edge(i+n, t, k, 0)
        for j in range(n):
            edges[i].append(g.add_edge(i, j+n, 1, big - grid[i][j]))
    g.add_edge(s, t, n*k, big)

    cost = g.flow(s, t, n * k)[1]
    print(big * n * k - cost)

    result = list(['.'] * n for _ in range(n))
    for i in range(n):
        for j in range(n):
            if g.get_edge(edges[i][j]).flow > 0:
                result[i][j] = 'X'

    for s in result:
        print("".join(s))


if __name__ == '__main__':
    main()

Solution (Python):
import heapq
class mcf_graph:
    def __init__(self, n=0):
        self._n = n
        self.g = [[] for _ in range(n)]
        self.pos = []
    
    def add_edge(self, frm, to, cap, cost):
        m = len(self.pos)
        e1 = self._edge(to, cap, cost)
        e2 = self._edge(frm, 0, -cost)
        e1.rev = e2
        e2.rev = e1
        self.pos.append(e1)
        self.g[frm].append(e1)
        self.g[to].append(e2)
        return m
    
    class edge:
        def __init__(self, frm, to, cap, flow, cost):
            self.frm = frm
            self.to = to
            self.cap = cap
            self.flow = flow
            self.cost = cost
        
        def __iter__(self):
            yield self.frm
            yield self.to
            yield self.cap
            yield self.flow
            yield self.cost
    
    def get_edge(self, i):
        e = self.pos[i]
        re = e.rev
        return self.edge(re.to, e.to, e.cap + re.cap, re.cap, e.cost)
    
    def edges(self):
        return [self.get_edge(i) for i in range(len(self.pos))]
    
    def flow(self, s, t, flow_limit=float('inf')):
        return self.slope(s, t, flow_limit)[-1]
    
    def slope(self, s, t, flow_limit=float('inf')):
        pop, push = heapq.heappop, heapq.heappush
        n = self._n
        g = self.g
        dual = [0] * n
        dist = [0] * n
        pv = [0] * n
        pe = [None] * n
        vis = [False] * n
        INF = float('inf')    
        flow = 0
        cost = 0
        prev_cost = -1
        result = [(flow, cost)]
        while flow < flow_limit:
            for i in range(n):
                dist[i] = INF
                vis[i] = False
            dist[s] = 0
            que = [(0, s)]
            while que:
                _, v = pop(que)
                if vis[v]:
                    continue
                vis[v] = True
                if v == t:
                    break
                for e in g[v]:
                    w = e.to
                    if vis[w] or not e.cap:
                        continue
                    ndist = dist[v] + e.cost - dual[w] + dual[v]
                    if ndist < dist[w]:
                        dist[w] = ndist
                        pv[w] = v
                        pe[w] = e
                        push(que, (dist[w], w))
            else:
                break
            for v in range(n):
                if not vis[v]:
                    continue
                dual[v] -= dist[t] - dist[v]
            c = flow_limit - flow
            v = t
            while v != s:
                c = min(c, pe[v].cap)
                v = pv[v]
            v = t
            while v != s:
                e = pe[v]
                e.cap -= c
                e.rev.cap += c
                v = pv[v]
            d = -dual[s]
            flow += c
            cost += c * d
            if prev_cost == d:
                result[-1] = (flow, cost)
            else:
                result.append((flow, cost))
            prev_cost = cost
        return result
    
    class _edge:
        def __init__(self, to, cap, cost):
            self.to = to
            self.cap = cap
            self.cost = cost
    
    class _edge:
        def __init__(self, to, cap, cost):
            self.to = to
            self.cap = cap
            self.cost = cost

n, k = map(int, input().split())
a = [list(map(int, input().split())) for _ in range(n)]
mx = max(aij for ai in a for aij in ai)
s = n * n
t = s + 1
rs = t + 1
cs = rs + n
vcnt = cs + n
d = mcf_graph(vcnt)
for i in range(n):
    d.add_edge(s, rs + i, k, 0)
for i in range(n):
    for j in range(n):
        d.add_edge(rs + i, i * n + j, 1, mx - a[i][j])
for i in range(n):
    for j in range(n):
        d.add_edge(i * n + j, cs + j, 1, 0)
for j in range(n):
    d.add_edge(cs + j, t, k, 0)
d.add_edge(s, t, n * n, mx)
_, res = d.flow(s, t, n * n)
print(n * n * mx - res)
ans = [['.'] * n for _ in range(n)]
for frm, to, cap, flow, cost in d.edges():
    if frm < n * n and flow:
        i, j = frm // n, frm % n
        ans[i][j] = 'X'
for t in ans:
    print(''.join(t))

Solution (Python):
import heapq
class mcf_graph:
    def __init__(self, n=0):
        self._n = n
        self.g = [[] for _ in range(n)]
        self.pos = []
    
    def add_edge(self, frm, to, cap, cost):
        m = len(self.pos)
        e1 = self._edge(to, cap, cost)
        e2 = self._edge(frm, 0, -cost)
        e1.rev = e2
        e2.rev = e1
        self.pos.append(e1)
        self.g[frm].append(e1)
        self.g[to].append(e2)
        return m
    
    class edge:
        def __init__(self, frm, to, cap, flow, cost):
            self.frm = frm
            self.to = to
            self.cap = cap
            self.flow = flow
            self.cost = cost
        
        def __iter__(self):
            yield self.frm
            yield self.to
            yield self.cap
            yield self.flow
            yield self.cost
    
    def get_edge(self, i):
        e = self.pos[i]
        re = e.rev
        return self.edge(re.to, e.to, e.cap + re.cap, re.cap, e.cost)
    
    def edges(self):
        return [self.get_edge(i) for i in range(len(self.pos))]
    
    def flow(self, s, t, flow_limit=float('inf')):
        return self.slope(s, t, flow_limit)[-1]
    
    def slope(self, s, t, flow_limit=float('inf')):
        n = self._n
        g = self.g
        dual = [0] * n
        dist = [0] * n
        pv = [0] * n
        pe = [None] * n
        vis = [False] * n
        INF = float('inf')
        def dual_ref():
            for i in range(n):
                dist[i] = INF
                vis[i] = False
            dist[s] = 0
            que = [(0, s)]
            while que:
                _, v = heapq.heappop(que)
                if vis[v]:
                    continue
                vis[v] = True
                if v == t:
                    break
                for e in g[v]:
                    w = e.to
                    if vis[w] or not e.cap:
                        continue
                    cost = e.cost - dual[w] + dual[v]
                    if dist[w] - dist[v] > cost:
                        dist[w] = dist[v] + cost
                        pv[w] = v
                        pe[w] = e
                        heapq.heappush(que, (dist[w], w))
            else:
                return False
            for v in range(n):
                if not vis[v]:
                    continue
                dual[v] -= dist[t] - dist[v]
            return True
        flow = 0
        cost = 0
        prev_cost = -1
        result = [(flow, cost)]
        while flow < flow_limit:
            if not dual_ref():
                break
            c = flow_limit - flow
            v = t
            while v != s:
                c = min(c, pe[v].cap)
                v = pv[v]
            v = t
            while v != s:
                e = pe[v]
                e.cap -= c
                e.rev.cap += c
                v = pv[v]
            d = -dual[s]
            flow += c
            cost += c * d
            if prev_cost == d:
                result.pop()
            result.append((flow, cost))
            prev_cost = cost
        return result
    
    class _edge:
        def __init__(self, to, cap, cost):
            self.to = to
            self.cap = cap
            self.cost = cost

n, k = map(int, input().split())
a = [list(map(int, input().split())) for _ in range(n)]
mx = max(aij for ai in a for aij in ai)
s = n * n
t = s + 1
rs = t + 1
cs = rs + n
vcnt = cs + n
d = mcf_graph(vcnt)
for i in range(n):
    d.add_edge(s, rs + i, k, 0)
for i in range(n):
    for j in range(n):
        d.add_edge(rs + i, i * n + j, 1, mx - a[i][j])
for i in range(n):
    for j in range(n):
        d.add_edge(i * n + j, cs + j, 1, 0)
for j in range(n):
    d.add_edge(cs + j, t, k, 0)
d.add_edge(s, t, n * n, mx)
_, res = d.flow(s, t, n * n)
print(n * n * mx - res)
ans = [['.'] * n for _ in range(n)]
for frm, to, cap, flow, cost in d.edges():
    if frm < n * n and flow:
        i, j = frm // n, frm % n
        ans[i][j] = 'X'
for t in ans:
    print(''.join(t))