- 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
- 43 lines of Python from the credited upstream file cherry-pickup-ii.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.
123 4import itertools5 6 7class Solution(object):8 def cherryPickup(self, grid):9 """10 :type grid: List[List[int]]11 :rtype: int12 """13 dp = [[[float("-inf")]*(len(grid[0])+2) for _ in xrange(len(grid[0])+2)] for _ in xrange(2)]14 dp[0][1][len(grid[0])] = grid[0][0] + grid[0][len(grid[0])-1]15 for i in xrange(1, len(grid)):16 for j in xrange(1, len(grid[0])+1):17 for k in xrange(1, len(grid[0])+1):18 dp[i%2][j][k] = max(dp[(i-1)%2][j+d1][k+d2] for d1 in xrange(-1, 2) for d2 in xrange(-1, 2)) + \19 ((grid[i][j-1]+grid[i][k-1]) if j != k else grid[i][j-1])20 return max(itertools.imap(max, *dp[(len(grid)-1)%2]))21 22 232425import itertools26 27 28class Solution2(object):29 def cherryPickup(self, grid):30 """31 :type grid: List[List[int]]32 :rtype: int33 """34 dp = [[[float("-inf")]*len(grid[0]) for _ in xrange(len(grid[0]))] for _ in xrange(2)]35 dp[0][0][len(grid[0])-1] = grid[0][0] + grid[0][len(grid[0])-1]36 for i in xrange(1, len(grid)):37 for j in xrange(len(grid[0])):38 for k in xrange(len(grid[0])):39 dp[i%2][j][k] = max(dp[(i-1)%2][j+d1][k+d2] for d1 in xrange(-1, 2) for d2 in xrange(-1, 2)40 if 0 <= j+d1 < len(grid[0]) and 0 <= k+d2 < len(grid[0])) + \41 ((grid[i][j]+grid[i][k]) if j != k else grid[i][j])42 return max(itertools.imap(max, *dp[(len(grid)-1)%2]))43