dynamic-programming

问题 I can't seem to get my splatting to work in my Invoke-WmiMethod command. I declare the hash table like so: $HKU = 2147483651 $MyParams = @{ 'Class' = 'StdRegProv'; 'Name' = 'EnumKey'; 'ArgumentList' = "$HKU,''"; 'ComputerName' = ''; } # additional code determining ComputerName... # $MyParams['ComputerName'] = $MyComputer; $Vals = Invoke-WmiMethod @MyParams This line gives me the following error: Invoke-WmiMethod : Input string was not in a correct format. At C:\Users\Person\Desktop\tmp.ps1

问题 Given a string consisting of only vowels, find the longest subsequence in the given string such that it consists of all five vowels and is a sequence of one or more a’s, followed by one or more e’s, followed by one or more i’s, followed by one or more o’s and followed by one or more u’s. If there is more than one longest subsequence, print any one. QUESTION: can u pls show how u would add memoization to soln below/show how to solve using dp? I've seen how to solve recursively (below). I'm

问题 My main purpose is to create something like that I currently read 2 dna sequence from my txt file and create 2 strings with 2 sequence (M and N) so I have to create a M+1 and N+1 matrix in order to perform my dynamic programming algorithm. now my problem is this! how can i create this 2d array? My first dimension should created with chars of my first string (part1) and second dimension with my second string (part2) How can i accomplish that and later on print it like in the picture. thank you

问题 I'm struggling a bit with dynamic programming. To be more specific, implementing an algorithm for finding Fibonacci numbers of n. I have a naive algorithm that works: int fib(int n) { if(n <= 1) return n; return fib(n-1) + fib(n-2); } But when i try to do it with memoization the function always returns 0: int fib_mem(int n) { if(lookup_table[n] == NIL) { if(n <= 1) lookup_table[n] = n; else lookup_table[n] = fib_mem(n-1) + fib_mem(n-2); } return lookup_table[n]; } I've defined the lookup

问题 I am interested in solving a variant of the coin exchange problem. Recall the formal definition of the coin exchange problem: Given a value N , if we want to make change for N cents, and we have infinite supply of each of S = {S1, S2, .. , Sm} integral-valued coins, how many ways can we make the change? The order of coins doesn’t matter. For example, for N = 4 and S = {1,2,3} , there are four solutions: {1,1,1,1},{1,1,2},{2,2},{1,3}. So output should be 4. For N = 10 and S = {2, 5, 3, 6} ,

问题 The question says, That given an array of size n, we have to output/partition the array into subsets which sum to N. For E,g, I/p arr{2,4,5,7}, n=4, N(sum) = 7(given) O/p = {2,5}, {7} I saw similar kind of problem/explanation in the url Dynamic Programming3 And I have the following queries in the pdf:- How could we find the subsets which sum to N, as the logic only tells whether the subset exist or not? Also, if we change the question a bit, can we find two subsets which has equal average

问题 What I have learnt is that dynamic programming (DP) is of two kinds: top-down and bottom-up. In top-down , you use recursion along with memoization. In bottom-up , you just fill an array (a table). Also, both these methods use same time complexity. Personally, I find top-down approach more easier and natural to follow. Is it true that a given question of DP can be solved using either of the approaches? Or will I ever face a problem which can only be solved by one of the two methods? 回答1: Well

问题 I'm researching on how to find the minimum number of steps required to convert word1 to word2, and came across the following implementation with the rules: Given two words word1 and word2, find the minimum number of steps required to convert word1 to word2. (each operation is counted as 1 step.) You have the following 3 operations permitted on a word: a) Insert a character b) Delete a character c) Replace a character And the idea of the implementation is: Use distance[i][j] to represent the

问题 I was reading about the matrix chain multiplication in dynamic programming, It has a naive recursive solution which has a exponential run-time. http://www.geeksforgeeks.org/dynamic-programming-set-8-matrix-chain-multiplication/ Although there is dynamic prog. solution(code in the link above) which has a run-time complexity of O(n^3), but if we keep a 2d array to store results for overlapping sub problems, Will it have a same run-time as the dp solution ? public class MatrixChain { public

问题 I don't know if this a well known problem or is reducible to one, but here is what I am struggling with since last few days. Let X_1, X_2, X_3 .. X_n be sets of integers. In each set X_i, we have m (equal for all sets) elements x_i,j : j = 1...m. My goal is to pick a single element from each set, such that the sum of pairwise differences between all the selected elements is least possible. I started off with a slightly different problem of minimizing the sum of serial-differences (that is,