Problem:


Score : 500 points

Problem Statement

There are K items placed on a grid of squares with R rows and C columns. Let (i, j) denote the square at the i-th row (1 \leq i \leq R) and the j-th column (1 \leq j \leq C). The i-th item is at (r_i, c_i) and has the value v_i.

Takahashi will begin at (1, 1), the start, and get to (R, C), the goal. When he is at (i, j), he can move to (i + 1, j) or (i, j + 1) (but cannot move to a non-existent square).

He can pick up items on the squares he visits, including the start and the goal, but at most three for each row. It is allowed to ignore the item on a square he visits.

Find the maximum possible sum of the values of items he picks up.

Constraints

1 \leq R, C \leq 3000

1 \leq K \leq \min(2 \times 10^5, R \times C)

1 \leq r_i \leq R

1 \leq c_i \leq C

(r_i, c_i) \neq (r_j, c_j) (i \neq j)

1 \leq v_i \leq 10^9

All values in input are integers.

Input

Input is given from Standard Input in the following format:

R C K
r_1 c_1 v_1
r_2 c_2 v_2
:
r_K c_K v_K

Output

Print the maximum possible sum of the values of items Takahashi picks up.

Sample Input 1

2 2 3
1 1 3
2 1 4
1 2 5

Sample Output 1

8

He has two ways to get to the goal:

Visit (1, 1), (1, 2), and (2, 2), in this order. In this case, the total value of the items he can pick up is 3 + 5 = 8.

Visit (1, 1), (2, 1), and (2, 2), in this order. In this case, the total value of the items he can pick up is 3 + 4 = 7.

Thus, the maximum possible sum of the values of items he picks up is 8.

Sample Input 2

2 5 5
1 1 3
2 4 20
1 2 1
1 3 4
1 4 2

Sample Output 2

29

We have four items in the 1-st row. The optimal choices are as follows:

Visit (1, 1) (1, 2), (1, 3), (1, 4), (2, 4), and (2, 5), in this order, and pick up all items except the one on (1, 2). Then, the total value of the items he picks up will be 3 + 4 + 2 + 20 = 29.

Sample Input 3

4 5 10
2 5 12
1 5 12
2 3 15
1 2 20
1 1 28
2 4 26
3 2 27
4 5 21
3 5 10
1 3 10

Sample Output 3

142



Sample input:
2 2 3
1 1 3
2 1 4
1 2 5


Sample output:
8

Submission status: Wrong Answer

Solution code:
# compiled by pythran
"""
# pythran export main2()
import numpy as np
i4 = np.int32
i8 = np.int64


def solve(a):
    dp = np.zeros_like(a)
    dp0 = np.zeros((a.shape[1], 4), i8)
    if a[0, 0] > 0:
        dp0[0, 1] = a[0, 0]
    for c in range(1, a.shape[1]):
        if a[0, c] > 0:
            dp0[c, 0] = dp0[c - 1, 0]
            dp0[c, 1] = max(dp0[c - 1, 1], dp0[c - 1, 0] + a[0, c])
            dp0[c, 2] = max(dp0[c - 1, 2], dp0[c - 1, 1] + a[0, c])
            dp0[c, 3] = max(dp0[c - 1, 3], dp0[c - 1, 2] + a[0, c])
        else:
            dp0[c, 0] = dp0[c - 1, 0]
            dp0[c, 1] = dp0[c - 1, 1]
            dp0[c, 2] = dp0[c - 1, 2]
            dp0[c, 3] = dp0[c - 1, 3]
        dp[0, c] = max(dp0[c, 0], dp0[c, 1], dp0[c, 2], dp0[c, 3])
    for r in range(1, a.shape[0]):
        dp0 = np.zeros((a.shape[1], 4), i8)
        dp0[0, 0] = dp[r - 1, 0]
        if a[r, 0] > 0:
            dp0[0, 1] = dp[r - 1, 0] + a[r, 0]
        for c in range(1, a.shape[1]):
            if a[r, c] > 0:
                dp0[c, 0] = max(dp0[c - 1, 0], dp[r - 1, c])
                dp0[c, 1] = max(dp0[c - 1, 1], dp0[c - 1, 0] + a[r, c], dp[r - 1, c] + a[r, c])
                dp0[c, 2] = max(dp0[c - 1, 2], dp0[c - 1, 1] + a[r, c])
                dp0[c, 3] = max(dp0[c - 1, 3], dp0[c - 1, 2] + a[r, c])
            else:
                dp0[c, 0] = max(dp0[c - 1, 0], dp[r - 1, c])
                dp0[c, 1] = dp0[c - 1, 1]
                dp0[c, 2] = dp0[c - 1, 2]
                dp0[c, 3] = dp0[c - 1, 3]
            dp[r, c] = max(dp0[c, 0], dp0[c, 1], dp0[c, 2], dp0[c, 3])
    return dp[a.shape[0] - 1, a.shape[1] - 1]


def main(file_name):
    stdin = open(file_name)
    R, C, K = [int(x) for x in stdin.readline().split()]
    a = np.zeros((R, C), dtype=i8)
    for i in range(K):
        r, c, v = [int(x) for x in stdin.readline().split()]
        a[r - 1, c - 1] = v
    ans = solve(a)
    print(ans)


def main2():
    main('/dev/stdin')
"""
import os, sys
p = os.path.dirname(__file__)
pymain = os.path.join(p, "a.out")
if len(sys.argv) == 1:
    import subprocess
    if p == "":
        pymain = "./" + pymain
    subprocess.call(pymain)
else:
    import gzip, base64, stat
    gz = b'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'
    bin = gzip.decompress(base64.b64decode(gz))
    with open(pymain, "wb") as f:
        f.write(bin)
    os.chmod(pymain, 0o775)

Submission status: Wrong Answer

Solution code:
R, C, K = map(int, input().split())
table = [[0]*C for _ in range(R)]
for _ in range(K):
    r, c, v = map(int, input().split())
    table[r-1][c-1] = v

dp = [[[0, []] for _ in range(C)] for _ in range(R)]

if table[0][0]:
    dp[0][0] = [table[0][0], [table[0][0]]]
else:
    dp[0][0] = [0, []]

for i in range(R):
    for j in range(C):

        if i == 0 and j == 0:
            continue

        if i == 0:
            if table[i][j] == 0:
                dp[i][j] = dp[i][j-1][:]

            elif len(dp[i][j-1][1]) < 3:
                dp[i][j][0] = dp[i][j-1][0] + table[i][j]
                dp[i][j][1] = dp[i][j-1][1] + [table[i][j]]

            else:
                if min(dp[i][j-1][1]) > table[i][j]:
                    dp[i][j] = dp[i][j-1].copy()
                else:
                    dp[i][j][0] = dp[i][j-1][0] + table[i][j] - min(dp[i][j-1][1])
                    dp[i][j][1] = sorted(dp[i][j-1][1], reverse=True)[:2] + [table[i][j]]
            continue

        if j == 0:
            dp[i][j][0] = dp[i-1][j][0] + table[i][j]
            if table[i][j]:
                dp[i][j][1] = [table[i][j]]
            else:
                dp[i][j][1] = []
        else:
            tmp1 = [0, []]
            tmp2 = [0, []]

            if table[i][j] == 0:
                tmp1 = dp[i][j-1].copy()

            elif len(dp[i][j-1][1]) < 3:
                tmp1[0] = dp[i][j-1][0] + table[i][j]
                tmp1[1] = dp[i][j-1][1] + [table[i][j]]

            else:
                if min(dp[i][j-1][1]) > table[i][j]:
                    tmp1 = dp[i][j-1].copy()
                else:
                    tmp1[0] = dp[i][j-1][0] + table[i][j] - min(dp[i][j-1][1])
                    tmp1[1] = sorted(dp[i][j-1][1], reverse=True)[:2] + [table[i][j]]

            tmp2[0] = dp[i - 1][j][0] + table[i][j]
            if table[i][j]:
                tmp2[1] = [table[i][j]]
            else:
                tmp2[1] = []

            if tmp2[0] >= tmp1[0]:
                dp[i][j] = tmp2.copy()
            else:
                dp[i][j] = tmp1.copy()

print(dp[R-1][C-1][0])