Description: 题目:求网络的最小费用最大流-C语言
已经经过调试,没有问题!-topics : finding the minimum cost maximum flow-through C language has been tested and no problems! Platform: |
Size: 20480 |
Author:周琳 |
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Description: 求数组最长递增子序列算法。输入一个数组，输出其最大递增子序列-Array for the longest increasing subsequence algorithm. An array of input and output its maximum incremental sequence Platform: |
Size: 1024 |
Author:sdf |
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Description: 用Java编写的maximum contiguous subsequence sum algorithm 实现, 希望对大家有所帮助-Java prepared using maximum contiguous subsequence sum algorithm to achieve, I hope all of you to help Platform: |
Size: 1024 |
Author:阎贺 |
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Description: 求取最大连续子序列之和与最大连续子序列，输入在input2.dat文件中，输出在result2文件中-Derive the maximum sum consecutive subsequence with the maximum continuous sub-sequence, enter the input2.dat file, the output file in the result2 Platform: |
Size: 5120 |
Author:vida gu |
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Description: 最大子序列之和的实现，依旧是课程设计中的一个简单实现.-Maximum subsequence sum of realization remains in the curriculum design of a simple implementation. Platform: |
Size: 538624 |
Author:hsy |
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Description: java实现，寻找最大连续子序列完整算法，可以找出所有最大连续子序列-java implementation, a complete search algorithm for maximum consecutive subsequence, can find all of the largest sequence of consecutive sub- Platform: |
Size: 6144 |
Author:尹文昊 |
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Description: Finding Maximum Contiguous Subsequence Sum using divide-and-conquer
approach-Given a sequence Q of n numbers (positive and negative), the maximum subsequence of Q is the
contiguous subsequence that has the maximum sum among all contiguous subsequences of Q. Platform: |
Size: 30720 |
Author:yuliqi |
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Description: 这是算法设计与分析课程实验，动态规划的源程序。包含最长上升子序列，最长公共子序列，最大字段和问题，DP背包问题-This is a test algorithm design and analysis programs, dynamic programming source code. Rising sequence containing the longest, longest common subsequence, maximum field and problems, DP knapsack problem Platform: |
Size: 2048 |
Author:林达 |
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Description: 最长公共子序列递归算法，最长限制是10000字符-Longest common subsequence of a recursive algorithm, the maximum limit is 10,000 characters Platform: |
Size: 2048 |
Author:zibin_lee |
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Description: 最大子矩阵
算法复杂性
书上介绍了一些关于最大子列的算法，现将其推广到二维，并分析算法复杂性-Performance Measurement
MAXIMUM SUBMATRIX SUM PROBLEM
In your textbook, several methods for solving the Maximum Subsequence Sum
problem are discussed in Section 2.4.3. Now let us extend this problem to 2-dimensional Platform: |
Size: 2048 |
Author:Wellfly May |
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Description: Java实现最大子序列问题，算法导论实验三-Java implementation of the maximum subsequence problem, algorithms, Introduction to Experimental Platform: |
Size: 1024 |
Author:张伟 |
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Description: 文本比较算法Ⅵ——用线性空间计算最大公共子序列（翻译贴）-Text comparison with linear space algorithm calculating maximum public subsequence (translation is stuck Platform: |
Size: 26624 |
Author:李浩 |
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Description: 最长公共子序列与最大字段和c语言版，具有图例说明结果-The longest common subsequence with maximum field and c language version, with Legend results Platform: |
Size: 173056 |
Author:王子彪 |
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Description: poj 1458
A subsequence of a given sequence is the given sequence with some elements (possible none) left out. Given a sequence X = < x1, x2, ..., xm > another sequence Z = < z1, z2, ..., zk > is a subsequence of X if there exists a strictly increasing sequence < i1, i2, ..., ik > of indices of X such that for all j = 1,2,...,k, xij = zj. For example, Z = < a, b, f, c > is a subsequence of X = < a, b, c, f, b, c > with index sequence < 1, 2, 4, 6 >. Given two sequences X and Y the problem is to find the length of the maximum-length common subsequence of X and Y.-A subsequence of a given sequence is the given sequence with some elements (possible none) left out. Given a sequence X = < x1, x2, ..., xm > another sequence Z = < z1, z2, ..., zk > is a subsequence of X if there exists a strictly increasing sequence < i1, i2, ..., ik > of indices of X such that for all j = 1,2,...,k, xij = zj. For example, Z = < a, b, f, c > is a subsequence of X = < a, b, c, f, b, c > with index sequence < 1, 2, 4, 6 >. Given two sequences X and Y the problem is to find the length of the maximum-length common subsequence of X and Y. Platform: |
Size: 529408 |
Author:cam |
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Search - Maximum Subsequence