- Define precisely what one DP state represents.
- Establish the base cases before transitions are evaluated.
- Process states in dependency order and combine only already-known values.
Code notes
- 37 lines of Python from the credited upstream file 1444.py.
- The implementation visibly relies on sequence storage, cached states.
- No explicit loop blocks detected.
Complexity
Multiply the number of reachable states by the work performed for each transition, then include the stored state table in memory usage.
Check the problem constraints before deciding whether this complexity will pass.
Use this to learn the idea, then write your own version.
1class Solution:2 def ways(self, pizza: list[str], k: int) -> int:3 MOD = 1_000_000_0074 M = len(pizza)5 N = len(pizza[0])6 prefix = [[0] * (N + 1) for _ in range(M + 1)]7 8 for i in range(M):9 for j in range(N):10 prefix[i + 1][j + 1] = ((pizza[i][j] == 'A') + prefix[i][j + 1] +11 prefix[i + 1][j] - prefix[i][j])12 13 def hasApple(row1: int, row2: int, col1: int, col2: int) -> bool:14 """Returns True if pizza[row1..row2)[col1..col2) has apple."""15 return (prefix[row2][col2] - prefix[row1][col2] -16 prefix[row2][col1] + prefix[row1][col1]) > 017 18 @functools.lru_cache(None)19 def dp(m: int, n: int, k: int) -> int:20 """Returns the number of ways to cut pizza[m..M)[n..N) with k cuts."""21 if k == 0:22 return 1 if hasApple(m, M, n, N) else 023 24 res = 025 26 for i in range(m + 1, M): 27 if hasApple(m, i, n, N) and hasApple(i, M, n, N):28 res += dp(i, n, k - 1)29 30 for j in range(n + 1, N): 31 if hasApple(m, M, n, j) and hasApple(m, M, j, N):32 res += dp(m, j, k - 1)33 34 return res % MOD35 36 return dp(0, 0, k - 1)37