Search - partition problem

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[Algorithm] freefem3d_1.0pre10.orig.tar DL : 0: 这是FreeFem2D的姊妹软件，对于学习有限元程序设计的人来说有很好的参考价值，我在Fedora Core9下编译运行通过(安装很容易)，编译后会生成ff3d可执行程序，通过读取描述问题的文件就可以自动剖分／求解等。-This is FreeFem2D s sister software, finite element program for the study and design for those who have a good reference value, I have compiled to run under Fedora Core9 through the (very easy to install), the compiler will generate ff3d executable programs, by reading description of the problem of the document can automatically partition/solving, etc..
Date : 2026-01-02 Size : 4.98mb User : hopeshot
[Algorithm] yuandaima DL : 0: 代码设计：整数的分划问题。如，对于正整数n=6，可以分划为： 6 5+1 4+2, 4+1+1 3+3, 3+2+1, 3+1+1+1 2+2+2, 2+2+1+1, 2+1+1+1+1 1+1+1+1+1+1+1 -Code design: integer partition problem. For example, for positive integer n = 6, you can partition as follows: 6 5+1 4+2, 4+1+1 3+3, 3+2+1, 3+1+1+1 2+2+2, 2+2+1+1, 2+1+1+1+1 1+1+1+1+1+1+1
Date : 2026-01-02 Size : 3kb User : 张晗
[Algorithm] Algorithm-analysis-and-design DL : 0: 计算机实验室内容： 1、分治法，maxmin算法 2、动态规划，矩阵连乘 3、贪心法， 1）背包问题，2）装载问题 4、回溯法，N皇后问题的循环结构算法和递归结构算法。-Computer laboratory content: 1, partition method, maxmin algorithm 2, the dynamic planning, LianCheng matrix 3, greedy method, 1) knapsack problem, 2) the loading problem 4, back in the method, the circulation of the queen s problems N structure algorithm and recursive structure algorithm.
Date : 2026-01-02 Size : 4kb User : 王越瑾
[Algorithm] KMean DL : 0: KMEAN C# In data mining, k-means clustering is a method of cluster analysis which aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean. This results in a partitioning of the data space into Voronoi cells. The problem is computationally difficult (NP-hard), however there are efficient heuristic algorithms that are commonly employed and converge fast to a local optimum. These are usually similar to the expectation-maximization algorithm for mixtures of Gaussian distributions via an iterative refinement approach employed by both algorithms. Additionally, they both use cluster centers to model the data, however k-means clustering tends to find clusters of comparable spatial extent, while the expectation-maximization mechanism allows clusters to have different shapes.
Date : 2026-01-02 Size : 2kb User : Truong