dian004

在visual studio的平台上开发CUDA程序实现并行加速计算矩阵的乘积，同时比较与串行计算的差异(Differences in the development of procedures to achieve parallel CUDA accelerated computing matrices in visual studio platform, but compared with the serial computing)

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)

计算波导中四波混频效率，考虑线性损耗、自由载流子吸收损耗、双光子吸收损耗(FWM efficiency in silicon waveguide)

ANSYS二次开发考虑损伤的材料模型子程序(ANSYS subroutine)

一个个人使用的精简了的c++有限元计算库(Finite element calculation library)

ABAQUS的二次开发，道路厚度与温度对路基的影响，来自廖公云老师的《ABAQUS有限元软件在道路工程中的应用》(ABAQUS secondary development, the road thickness and temperature of the roadbed, from Liao Gongyun teacher "ABAQUS finite element software in road works)

说明：  fftw 快速傅里叶变换， C#算法 直接调用dll(fftw fuliye bianhuan C# suanfa zhijie dianyong dll wenjian zhijie daoru fangzai bin wenjian jia zhong)

说明：  利用GA遗传算法解决欺骗函数最优问题，具体问题描述如下，如有问题请与我联系(The deceptive functions are a family of functions in which there exists low-order building blocks that do not combine to form the higher-order building blocks. Here, a deceptive problem that consists of 25 copies of the order-4 fully deceptive function DF2 is constructed for this paper. DF2 can be described as follows: f(0000)=28 f(0001)=26 f(0010)=24 f(0011)=18 f(0100)=22 f(0101)=6 f(0110)=14 f(0111)=0 f(1000)=20 f(1001)=12 f(1010)=10 f(1011)=2 f(1100)=8 f(1101)=4 f(1110)=6 f(1111)=30 This problem has a maximal function value of 750.)

有限元n维实体材料单元，仅供科研用途，拒绝商业使用(N-dimensional finite element solid material element, for scientific purposes, refusing commercial use)

％ SSOR预处理的共轭梯度法求解方程Ax=b ％ 输入参数说明 ％ A 正定矩阵[n*n] ％ b 右边向量 ％ omega SSOR预处理参数(0--2) ％ Times 迭代次数 ％ errtol 给定误差终止条件 ％ ％输出参数 ％ NewX 方程Ax=b的x近似解 ％ avgerr 求解的当前平均绝对误差( SSOR preconditioning conjugate gradient method for solving equations Ax = b the input parameter descriptions A positive definite matrix [n* n] b right side vector omega SSOR preconditioning parameters (0- 2) Times iterations errtol termination of a given error condition NewX output parameter equation Ax = b of x approximate solution to solve the current avgerr average absolute error)