Description: function [X,fval,exitflag,output,lambda]=quadprog(H,f,A,B,Aeq,Beq,lb,ub,X0,options,varargin)
％ X=QUADPROG(H,f,A,b) 求解二次规划:
％ min 0.5*x *H*x + f *x subject to: A*x <= b
％x=quadprog(H,f, A, b, Aeq, beq)求解二次规划：-function [X, fval, exitflag, output, lambda] = quadprog (H, f, A, B, Aeq, Beq, lb, ub, X0, options, varargin)% X = QUADPROG (H, f, A, b) solving quadratic programming : min 0.5%* H* x* x* x f subject to : A* x Platform: |
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Description: 优化算法loqo的算法源代码。Purpose: solves quadratic programming problem for pattern recognition for support vectors-Optimization algorithm loqo the algorithm source code. Purpose: solves quadratic programming problem for pattern recognition for support vectors Platform: |
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Description: LinProg.NET is a framework for mathematical programming implemented on .NET platform. This framework is supporting linear, integer, boolean and quadratic programming. Framework is written on Microsoft .NET version 2.0. Platform: |
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Description: 通过对经典的lemke互补转轴算法求解含有等式约束的凸二次规划问题的分析，发现所得到的线性互补问题(lcp)可能是退化的．由lemke算法求解(lcp)问题的迭代过程，通过六个命题说明了含有等式约束的凸二次规划问题对应的(lcp)问题退化的原因，并对经典的lemke算法的迭代过程进行修正，提出了一种改进的lemke算法，这种算法能有效地搜索到含等式约束凸二次规划问题的最优解．-Through the classic Lemke complementarity algorithm shaft containing the convex equality constrained quadratic programming problem of the analysis, we found by the linear complementarity problem (lcp) may be degraded. Algorithm by Lemke (lcp) of the iterative process of questions, through the six propositions that contain equality constraints of convex quadratic programming problem corresponding to (lcp) questions the reasons for degradation of the classic Lemke algorithm iterative process amendments put forward an improved Lemke algorithm can effectively containing english convex equality constrained quadratic programming problem of the optimal solution. Platform: |
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Description: Visual Studio 2005 环境下，C++ 实现的求解凸二次规划的算法源代码。-a C++ implementing algorithm for a (convex)
Quadratic Programming problem by means of an active-set dual method.
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Description: Numerical Optimization-Every year optimization algorithms are being called on to handle problems that are much larger and complex than in the past. Accordingly, the book emphasizes large- scale optimization techniques, such as interior-point methods, inexact Newton methods, limited-memory methods, and the role of partially separable functions and automatic differentiation. It treats important topics such as trust-region methods and sequential quadratic programming more thoroughly than existing texts, and includes comprehensive discussion of such “core curriculum” topics as constrained optimization theory, Newton and quasi-Newton methods, nonlinear least squares and nonlinear equations, the simplex method, and penalty and barrier methods for nonlinear programming. Platform: |
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Description: SVM classification
This routine classify the training set with a support vector machine using quadratic programming algorithm (active constraints method) Platform: |
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Description: 这个程序的主要功能是二次规划问题，这个在运筹学中是个非常重要的内容-The main feature of this program is to quadratic programming problems, this in operations research is a very important part of Platform: |
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Description: CO is an applications module written in the GAUSS programming language. It solves the Nonlinear Programming problem, subject to general constraints on the parameters - linear or nonlinear, equality or inequality, using the Sequential Quadratic Programming method in combination with several descent methods selectable by the user - Newton-Raphson, quasi-Newton (BFGS and DFP), and scaled quasi-Newton. There are also several selectable line search methods. A Trust Region method is also available which prevents saddle point solutions. Gradients can be user-provided or numerically calculated. -CO is an applications module written in the GAUSS programming language. It solves the Nonlinear Programming problem, subject to general constraints on the parameters- linear or nonlinear, equality or inequality, using the Sequential Quadratic Programming method in combination with several descent methods selectable by the user- Newton-Raphson, quasi-Newton (BFGS and DFP), and scaled quasi-Newton. There are also several selectable line search methods. A Trust Region method is also available which prevents saddle point solutions. Gradients can be user-provided or numerically calculated. Platform: |
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Description: 这是龚纯《精通MATLAB最优化计算》随书源码(M文件)。基于MATLAB优化工具箱，代码包含的内容有：牛顿法等无约束一维极值问题、单纯形搜索法等无约束多维极值问题、Rosen梯度投影法等约束优化问题、L-M法等非线性最小二乘优化问题、线性规划、整数规划、二次规划、粒子群优化、遗传算法。-This is pure Gong " Mastering MATLAB optimization calculations," with the book source (M file). Based on MATLAB Optimization Toolbox, the content code contains: Newton and other non-binding one-dimensional extremum problems, such as simplex search method for unconstrained multi-dimensional extremum problems, Rosen gradient projection method and other constrained optimization problems, LM non-linear law least squares optimization problem, linear programming, integer programming, quadratic programming, particle swarm optimization, genetic algorithm. Platform: |
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Search - quadratic programming