PROgrams-of-materials

电容层析成像Tikhonov算法的演示实例，带有详细的说明(An example of a demonstration of the Tikhonov algorithm for capacitance tomography, with a detailed description)

我们需要成百上千的分类器来解决现实世界的分类吗 我们评估179分类17种分类器（判别分析，贝叶斯，神经网络，支持向量机，决策树，基于规则的分类器，升压、装袋、堆放、随机森林和其他合奏，广义线性模型，线性，偏最小二乘法和主成分回归，logistic回归、多项式回归、多元自适应回归样条等方法），实现在WEKA，R（有或没有插入包），C和Matlab，包括所有目前可用的相关分类。(Do-we-Need-Hundreds-of-Classifiers-to-Solve-Real-World-ClassificationProblems)

说明：  Dsimplex.m 对偶单纯形法求解线性规划问题(Dual simplex method for solving linear programming problems)

对格朗日、分段线性、三次样条插值方法的运用。(Grange, piecewise linear, cubic spline interpolation method to use.)

程序用Fortran95标准，给出了双共轭梯度法求解超大型稀疏矩阵的子程序。还给出了一个例子，例子的数据用索引存储了维数将近10万的对称稀疏正定矩阵。(Using Fortran95, a method of Biconjugate gradient for super linear sysem is provied. In addition, a example with the storage type of row-column index is also provide for test these subroutines. )

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)

基于ABAQUS的hashin和Tong-Norrius准则的umat子程序(Umat subroutine based on the Hashin and Tong-Norrius guidelines of ABAQUS)

Maximum Flow Graph (Boykov Kolmogorov Algorithm)

根据LMD的原始定义自己编写的LMD代码( local mean decomposition matlab code according to it s original paper)

poisson生成器，可以生成泊松分布的随机数，需要的就下载吧(poisson sasadas)