人脸图片数据库(PGM)

【实例简介】

用MATLAB 实现，基于栅格地图的蚁群算法路径规划，含蚁群相关文档。

算法入门资源，便于快速掌握和复习。学的久了忘记的也可以用来回顾下……

黑马程序员&传智播客 Python入门教程完整版（懂中文就能学会）免费视频课程配套讲义及代码课程官网:http://yun.itheima.com/course/273.html bilibili:https://www.bilibili.com/video/av14184325/

软件测试是软件工程中非常重要的环节，是软件质量的保证。该课程是培养训练学生软件质量保证能力的重要实践性教学环节，与软件测试技术课程的教学内容紧密配合，同步进行。通过软件测试的实践训练，深刻理解和掌握软件测试和软件测试过程的基本方法和基本技术，熟练掌握黑盒测试、白盒测试的测试用例的设计，同时进一步提高学生对于复杂程序的编写能力，为学生将来从事实际软件测试工作和进一步深入研究打下坚实的理论基础和实践基础。

一个struts1的demo含数据库哦，通过用户登录测试的.

MATLAB仿真的分布式电源接入对于配电网的影响，有疑问可以私信我。

Huffman构造自适应霍夫曼树实现文件的压缩与解压缩。-Adaptive Huffman tree structure for file compression and decompression.文件列表(点击判断是否您需要的文件，如果是垃圾请在下面评价投诉): EncoderHuffman.h .......Encoder.cpp DecoderHuffman.h .......Decoder.cpp Encoder Decoder

C#实现键盘鼠标全局钩子VS2013，可编译运行，带DEMO可全局拦截鼠标单击/双击/滚动事件可全局拦截键盘按键事件可锁定键盘鼠标输入，比如锁屏！该代码无法屏蔽Ctrl +alt +delete 需要实现所有拦截，请参考相关C++实现

alternating direction method of multipliers优化算法讲解OutlineDual decompositionMethod of multipliersAlternating direction method of multipliersCommon patternsExamplConsensus and exchangeConclusionsDual decompositionDual problemp convex equality constrained optimization problemminimizesubject to Ax= 6e Lagrangian: L(a, g)=f(a)+y(Ac-bdual function: g(y)=infx L(, g)e dual problem: maximize g(g)recover x*=argminL(, y*)Dual decompositionDual ascentgradient method for dual problem: y+l=yk +aVg(yky ")=A c-b, where a= argmin L(a, y")b dual ascent method isk+1gminz L(a, yk/-minimization(Axk+I-b)// dual updateworks, with lots of strong assumptionsDual decompositionDual decompositione suppose f is separablef(x)=f1(x1)+…+fN(xN),x=(x1Nthen L is separable in x: L(a, y)=L1(a1, 3)+...+Ln(N, 3)-y bLi(ai, y)=fi(ai)+y Aiaie -minimization in dual ascent splits into N separate minimizationsk+1argmin Li(li, y)Which can be carried out in parallelDual decompositionDual decompositiondual decomposition(Everett, Dantzig, Wolfe, Benders 1960-65k+1argLi(ei, y)N A: k+scatterupdate i in parallel, gather Ai k+solve a large problemby iteratively solving subproblems(in parallel)dual variable update provides coordinationworks, with lots of assumptions; often slowDual decompositionOutlineDual decompositionMethod of multipliersAlternating direction method of multipliersCommon patternsExamplConsensus and exchangeConclusionsMethod of multipliersMethod of multipliersa method to robustify dual ascentb use augmented Lagrangian(Hestenes, Powell 1969),p>0(, y)=f(c)+y(Ax-b)+(p/2)Acmethod of multipliers( Hestenes, Powell; analysis in Bertsekas 1982)k+1argmin Lp(a, yD(A.(note specific dual update step length pMethod of multipliersMethod of multipliers dual update stepoptimality conditions( for differentiableAcx-b=0, Vf(a*)+A(primal and dual feasibility)Since ah+1minimizes Lp(a, y)k+1 kf(x4+1)+A7(y+p(AVxf(at)+adual update yti=y+p(k+1k+1dual feasibleprimal feasibility achieved in limit: A k+I-b>0Method of multipliers