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著名的Sparco工具包。是matlab下解决线性约束，二阶锥约束和半定约束等优化问题的常用工具。-Sparco famous Kit. Are resolved under matlab linear constraints, second-order cone constraint and semidefinite constrained optimization problems, such as commonly used instrument.
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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)
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此算法采用matlab7.0环境下编写的代码，用于求解线性规划问题中非约束条件下的最优解问题。-This algorithm uses matlab7.0 environment code written for solving linear programming problems under the conditions of the Central African constrained optimal solution problem.
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GENOCOP (GEnetic algorithm for Numerical Optimization for COnstrained Problems). This system is optimizing any function with any number of linear constraints (equalities and inequalities).
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数学建模与数学实验（第3版）
第1讲 数学建模简介
第2讲 MATLAB入门
第3讲 MATLAB作图
第4讲 线性规
第5讲 无约束优化划
第6讲 非线性规划
第7讲 微分方
第8讲 最短路问题程
第9讲 行遍性问题-Mathematical Modeling and Mathematical Experiments (3rd edition) Section 1 Introduction to Mathematical Modeling 2 speaker
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4 files are included in the package:
(1) fs01.m - the 1st-order local linear approximation (LLA) very similar to algorithm by Farmer and Sidorowich (1987). Uses constrained linear least squares (lsqlin) and
(2) ts1.m - to reconstruct the vector
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求最小值的方程原文件及运行命令文件，可以方便快捷的求出非线性有约束的最小值-Seeking the minimum value of the equation of the original documents and run the command file, you can find convenient and efficient non-linear constrained minimum
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使用先进的人工鱼群算法求解线性约束规划，此代码为原创，带注释，易读。-The use of advanced artificial fish school algorithm for solving linear constrained programming, the code for the original, with annotations, easy to read.
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使用HOPfield神经网络求解参数线性约束规划，此代码为原创，可直接运行。-Use HOPfield Neural networks for solving parametric linear constrained programming, the code for the original, can be directly run.
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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 Progra
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线性语速广义旁瓣相消器（LC-GSC）的仿真和线性约束最小方差波束形成（LCMV）比较-Linear Speed generalized side lobe canceller (LC-GSC) of the simulation and linear constrained minimum variance beamforming (LCMV) compared
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用基本遗传算法求解一维无约束优化问题
用顺序选择遗传算法求解一维无约束优化问题
用动态线性标定适应值的遗传算法求解一维无约束优化问题
用大变异遗传算法求解一维无约束优化问题
用自适应遗传算法求解一维无约束优化问题
用双切点遗传优化求解一维无约束优化问题
用多变异位自适应遗传优化求解一维无约束优化问题
-The basic genetic algorithm with one-dimensional sequence of unconstrained optimizat
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constrained linear least square implementation
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基于hopfield神经网络解决线性约束优化问题，效果不错，运算精度高
-Based on the hopfield neural network to solve linear constrained optimization problem, the effect is good, operation precision is high
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这是一个多目标多参数无约束线性优化程序，其中包括三个具体的应用实例，对于解决非线性优化问题十分好用-This is a multiple target multi parameter non linear constrained optimization program, which includes three specific examples of application, to solve the nonlinear optimization problem is very easy t
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基于线性约束最小方差（LCMV）准则、ASC旁瓣相消——MSE准则的波束合成，运行效果极好，放心下载。-Based on the linear constrained minimum variance (LCMV) criterion, ASC Sidelobe Canceller- MSE criterion beamformer, run very effective, secure download.
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ADMM方法对于求解线性约束凸优化问题是有效的。在本文中，我们提出了一个邻近线性算法，同伦算法习惯于求解每个迭代点的子问题。在合适的条件下，这个全局收敛和的收敛速度是所提出算法被证明的最糟糕的情况。一个初始的数值结果表明提出方法的有效性。-ADMM method for solving linear constrained convex optimization problem is effective. In this paper, we propose a near linear algo
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使用传统的几种算法来形成自适应波束，包括最大信噪比准则（MSNR），线性约束最小方差（LCMV）和最小均方误差准则（MMSE）等。-The traditional algorithms are used to form adaptive beams, including maximum signal-to-noise ratio (MSNR), linear constrained minimum variance (LCMV), and minimum mean square error cr
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标准的粒子群算法程序代码，相对简洁，求解二维的带约束优化问题(Newton method for solving linear constrained problems)
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Locality-constrained Linear Coding (LLC) in place of the VQ coding in traditional SPM. LLC utilizes the locality constraints to project each descr iptor into its local-coordinate system, and the projected coordinates are integrated by max pooling to
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