dynamic-programming

问题 Problem Given n , find the smallest positive x such that f(x) = n . f(x) is the sum of the digit sum of the factorials of the digits of x . For example, f(15) = digit_sum(1!) + digit_sum(5!) = digit_sum(1) + digit_sum(120) = (1) + (1 + 2 + 0) = 4 Breath first search can find the answer. Are there faster ways? Breath First Search def bfs(target, d_map): # Track which values of f(x) have we visited visited = set([0]) # f(x) of the current level of the search tree todo = [0] # Digits of x for

问题 I understand the target approach for both the methods where Optimal Substructure calculates the optimal solution based on an input n while Overlapping Subproblems targets all the solutions for the range of input say from 1 to n. For a problem like the Rod Cutting Problem. In this case while finding the optimal cut, do we consider each cut hence it can be considered as Overlapping Subproblem and work bottom-up. Or do we consider the optimal cut for a given input n and work top-down. Hence,

问题 I understand the target approach for both the methods where Optimal Substructure calculates the optimal solution based on an input n while Overlapping Subproblems targets all the solutions for the range of input say from 1 to n. For a problem like the Rod Cutting Problem. In this case while finding the optimal cut, do we consider each cut hence it can be considered as Overlapping Subproblem and work bottom-up. Or do we consider the optimal cut for a given input n and work top-down. Hence,

问题 I understand the target approach for both the methods where Optimal Substructure calculates the optimal solution based on an input n while Overlapping Subproblems targets all the solutions for the range of input say from 1 to n. For a problem like the Rod Cutting Problem. In this case while finding the optimal cut, do we consider each cut hence it can be considered as Overlapping Subproblem and work bottom-up. Or do we consider the optimal cut for a given input n and work top-down. Hence,

问题 I feel like Kadane's algorithm is a modified version of the true dynamic programming solution of maximum subarray problem.Why do I feel so? I feel because the way to calculate the maximum subarray can be taken by: for(i=0;i<N;i++) { DP[i][A[i]]=true; for(j= -ve maximum ;j<= +ve maximum ;j++) if(DP[i-1][j]) DP[i][j+A[i]]=true; } The recurrence being if it is possible to form j with a subarray ending at i-1 elements i can form j+A[i] using the i th element and also form A[i] alone by starting a

问题 I feel like Kadane's algorithm is a modified version of the true dynamic programming solution of maximum subarray problem.Why do I feel so? I feel because the way to calculate the maximum subarray can be taken by: for(i=0;i<N;i++) { DP[i][A[i]]=true; for(j= -ve maximum ;j<= +ve maximum ;j++) if(DP[i-1][j]) DP[i][j+A[i]]=true; } The recurrence being if it is possible to form j with a subarray ending at i-1 elements i can form j+A[i] using the i th element and also form A[i] alone by starting a

问题 I feel like Kadane's algorithm is a modified version of the true dynamic programming solution of maximum subarray problem.Why do I feel so? I feel because the way to calculate the maximum subarray can be taken by: for(i=0;i<N;i++) { DP[i][A[i]]=true; for(j= -ve maximum ;j<= +ve maximum ;j++) if(DP[i-1][j]) DP[i][j+A[i]]=true; } The recurrence being if it is possible to form j with a subarray ending at i-1 elements i can form j+A[i] using the i th element and also form A[i] alone by starting a

问题 I feel like Kadane's algorithm is a modified version of the true dynamic programming solution of maximum subarray problem.Why do I feel so? I feel because the way to calculate the maximum subarray can be taken by: for(i=0;i<N;i++) { DP[i][A[i]]=true; for(j= -ve maximum ;j<= +ve maximum ;j++) if(DP[i-1][j]) DP[i][j+A[i]]=true; } The recurrence being if it is possible to form j with a subarray ending at i-1 elements i can form j+A[i] using the i th element and also form A[i] alone by starting a

问题 I have an object and I want to dynamically call a method on it. Having typechecking would be nice but that maybe impossible. But I can't even get it to compile at all currently: const key: string = 'someMethod' const func = this[key] func(msgIn) gives me this error... Element implicitly has an 'any' type because expression of type 'any' can't be used to index type 'TixBot'. I tried some other type options without success. const key: any = cmd.func const func: any = this[key] Apart from @ts

问题 While taking the Algorithms: Design and Analysis II class, one of the questions asks about the maximum-weight independent set problem for a path graph. shown below is a (blurry) screenshot of the problem statement, and the corresponding lecture videos are on YouTube: https://www.youtube.com/watch?v=0awkct8SkxA https://www.youtube.com/watch?v=pLOkbHGRsv0 https://www.youtube.com/watch?v=Im_zjFkZDCY This problem can be elegantly solved by dynamic programming, with literally one line of code. a[i