GS2

有效集法利用数学规划的对偶理论,将所求双层规划转化为一个下层只有一个无约束凸二次子规划的双层规划问题.然后根据两个双层规划的最优解和最优目标值之间的关系,提出一种简单有效的算法来解决非增值型凸二次双层规划问题.(Quadratic Bilevel Programming 　)

用Python语言写的多目标进化算法库（2017年最新版本），包含常见的多目标进化算法以及当前最新文献中的最新算法，能够实现不同算法之间的性能比较（包含常见的GD, IGD, HV等评价指标自动生成）。可以供进化计算、多目标优化、单目标优化算法的学习参考。Python语言版本简单易懂，实用性强。(Library of multi-objective evolutionary algorithm written in Python language (the latest version in 2017), including multi-objective evolutionary algorithm and the most common new literature in the new algorithm, can realize the performance between different methods (including the common GD, IGD, HV and other evaluation index generation). It can be used as a reference for evolutionary computation, multi-objective optimization and single objective optimization. Python language version is simple and easy to understand, practical.)

说明：  最速下降法就是梯度下降法,可以用于求解最小二乘问题(线性和非线性都可以)。可以说是求解机器算法的最古老、最经典的模型，另一种常用的方法是最小二乘法。(The steepest descent method is the gradient descent method, which can be used to solve the least squares problem (both linear and non-linear). It can be said that it is the oldest and most classical model for solving machine algorithm. Another commonly used method is the least square method.)

这个是计算声传播的各种模型 包括简正波kraken波数积分scooter等模型 有很好的学习价值 (a system program for acoustic propagation calculation)

这是一段导线找形的命令流，可用于ansys有限元分析(This is a piece of wire to find the form of order flow, can be used for finite element analysis ansys)

说明：  MATLAB实现自定义的拉格朗日插值和牛顿插值，均包括主调函数和被调用函数，程序简单易懂。(MATLAB realizes the self defined Lagrange interpolation and Newton interpolation, both including the main function and the called function, the program is simple and easy to understand.)

xfdtd中用于计算光子晶体PBG的算例(example used for PBG calculation in xfdtd)

说明：  自己编的，实现l-bfgs解无约束优化问题(Own, and the realization of l-bfgs Unconstrained optimization problems)

ANSYS车桥耦合瞬态分析代码,实现车辆移动荷载加载(ANSYS-vehicle bridge coupled)

based classification (SRC) has been widely used for face recognition (FR). SRC first codes a testing sample as a sparse linear combination of all the training samples, and then classifies the testing sample by evaluating which class leads to the minimum representation error. While the importance of sparsity is much emphasized in SRC and many related works, the use of collaborative representation (CR) in SRC is ignored by most literature.