jeimai

遗传算法反演子程序，可用于地球物理非线性反演(The genetic algorithm inversion subroutine, can be used for nonlinear geophysical inversion)

一维 问题，即激波管问题，是一个典型的一维可压缩无黏气体动力学问题，并有 解析解。对它采用二阶精度 两步差分格式进行数值求解。(Use MacCormack difference scheme for solving a two-dimensional shock tube problem)

说明：  ABAQUS 的二次开发,详细注释说明，自学ABAQUS的二次开发(ABAQUS secondary development, detailed notes, self-study ABAQUS secondary development)

点云曲面建模，基于距离函数，运用移动最小二乘法，拟合曲面，提取等值面，用于散乱点云的快速三角网格构建(Point cloud surface modeling, based on the distance function, the use of moving least squares method, fitting surface, isosurface extraction for fast scattered point cloud build triangular mesh)

说明：  扩展有限元的Fortran代码，用于扩展有限元的数值仿真计算。(Extended finite element of the Fortran code for the extended finite element numerical simulation.)

Fortran编写的VOF经典算法，SOLA-VOF(VOF classical algorithms written in Fortran, SOLA-VOF)

共轭梯度法（Conjugate Gradient）是介于最速下降法与牛顿法之间的一个方法，它仅需利用一阶导数信息，但克服了最速下降法收敛慢的缺点，又避免了牛顿法需要存储和计算Hesse矩阵并求逆的缺点，共轭梯度法不仅是解决大型线性方程组最有用的方法之一，也是解大型非线性最优化最有效的算法之一(Conjugate gradient method (Conjugate Gradient) is between the steepest descent method and Newton' s method between a method that takes only a first derivative information, but to overcome the slow convergence of the steepest descent method shortcomings, but also avoid the need to store Newton and computing the inverse Hesse matrix and disadvantages, conjugate gradient method is not only to solve large linear equations of the most useful methods, large-scale nonlinear optimization solution is the most efficient algorithms)

说明：  FDTD的3D源程序,PEC边界,比较详细(FDTD the 3D source, PEC boundary, a more detailed)

The following is an example of how Microsoft Solver Foundation can be used to solve a constraint satisfaction problem (CSP) like generating a typical Sudoku problem. Sudoku http://www.codeproject.com/Articles/419389/Sudoku-using-Microsoft-solver-foundation In this article I do not attempt to explain everything there is to know about constraint satisfaction problems, but I will go over the concepts in the hope that even if you have never heard of CSP you will still get the idea.

约束最优化方法－－最速下降法(也叫梯度法)，是人们用来求多个变量函数极值问题的最早的一种方法。(Constrained optimization methods- steepest descent method (also known as gradient method), is used for multiple variables function Extremum Problems earliest methods.)