dynamic-programming

问题 I have understood that to find the solution of LIS problem, we need to find a LIS for every subsequence starting from initial element of the array to the each element that ends with a particular element(the last element), but I am not able to understand how would that help in finally finding a LIS of a given unsorted array, I also understand that this leads to an optimal substructure property and then can be solved, but as mentioned, I dont see how finding LIS(j) that ends with arr[j] will

问题 I am attempting to calculate the Levenshtein distance between two strings using dynamic programming. This is being done through Hackerrank, so I have timing constraints. I used a techenique I saw in: How are Dynamic Programming algorithms implemented in idiomatic Haskell? and it seems to be working. Unfortunaly, it is timing out in one test case. I do not have access to the specific test case, so I don't know the exact size of the input. import Control.Monad import Data.Array.IArray import

问题 I am attempting to calculate the Levenshtein distance between two strings using dynamic programming. This is being done through Hackerrank, so I have timing constraints. I used a techenique I saw in: How are Dynamic Programming algorithms implemented in idiomatic Haskell? and it seems to be working. Unfortunaly, it is timing out in one test case. I do not have access to the specific test case, so I don't know the exact size of the input. import Control.Monad import Data.Array.IArray import

问题 We have a 3x3 grid with the following values: 1 3 4 7 2 1 4 1 8 A person starts on the leftmost column and can then move either northeast, east or southeast. Now I have to find the optimal path through this grid. The starting point can be anywhere on the leftmost column. In this case the answer was 17 because the optimal path was 7->2->8. I would like to know how to compute this optimal path and how to compute it for larger grids. Thanks! 回答1: You can solve this problem with a bottom-up

问题 I was trying to understand the differences between Dynamic and Greedy algorithms, and This answer by Neil G was quite helpful, but, there was this one statement that he made which caused a debate in the comments section. The difference between dynamic programming and greedy algorithms is that with dynamic programming, the subproblems overlap. That means that by "memoizing" solutions to some subproblems, you can solve other subproblems more quickly. Comments aren't the best place to solve a

问题 I have interval data: A = (0,50) B = (20,500) C = (80,420) .... And realized that there's an associated graph with this data, the interval graph I'd like to find the most efficient path to go from A to G (assume I know all of the positive vertex weights, wa, wb, wc...). I need to start at A and go to G, so the minimum spanning tree must be bound between these points. One of the constraints in our application is that the interval starting at A and ending at G must be covered in full (no gaps).

问题 This is an interview question. Given an integer array find a subsequence (not necessarily contiguous) for which the absolute value of the sum of its elements is minimized. It looks like a DP problem. Let S1[i] is a subsequence ending at a[i] for which its sum > 0 and abs (sum) is minimized. Let S2[i] is a subsequence ending at a[i] for which its sum < 0 and abs (sum) is minimized. S1[i] is the minimum of all S1[j] + a[i] for j < i if S1[j] + a[i] > 0 && a[i] < 0 S2[i] is the minimum of all S2

问题 Problem Statement The rod-cutting problem is the following. Given a rod of length n inches and a table of prices Pi for i = 1, 2, 3,....n , determine the maximum revenue Rn obtain- able by cutting up the rod and selling the pieces. Note that if the price Pn for a rod of length n is large enough, an optimal solution may require no cutting at all. Consider the case when n=4 . Figure shows all the ways to cut up a rod of 4 inches in length, including the way with no cuts at all. We see that

问题 Edit: Codes are fixed. I'm trying to solve the problem below with dynamic programming but it gives 15 instead of 18. I'm looking for an error for about an hour but I couldn't figure it out. Problem: There is two knapsack with capacities c1, c2 and an n-element set. Each element has a w1 (its weight when put in the first knapsack), a w2 (its weight when put in the second knapsack) and a value. I need to find two disjoint subsets of the n-element set that fits in the corresponding knapsacks and

问题 I found this very handy example code which implements a DP solution to the knapsack problem (kudos to the person who posted it). https://codereview.stackexchange.com/questions/20569/dynamic-programming-solution-to-knapsack-problem I am trying to modify it to include a constraint on the number of items k in the knapsack. I added a third argument def knapsack(items, maxweight, maxitems): and modified the reconstruction as follows: while i > 0: if bestvalues[i][j] != bestvalues[i - 1][j] and len