sanciyangtiao

进行PQ分解，计算电力系统潮流的有效方法，应用广泛(PQ decomposition is an effective method for calculating power flow in power system.)

用C＋＋实现的高斯混合模型的算法类，方差矩阵是对角矩阵(C++ Gaussian mixture model algorithm category, variance matrix is diagonal matrix)

涡格法计算机翼的涡强，对不同剖面形状，拱度等进行了比较分析。并且与书籍上对比(Vortex lattice method the computer wing of the vortex is strong, the shape of different profiles, such as camber, a comparative analysis.)

用三元组表为结构，实现稀疏矩阵的加法并输出。(Achieve sparse matrix addition)

优化设计算法：用powell法求解目标函数的最优解和最优值，已经验证算法的正确性，在visual c++6.0下开发(Optimization algorithm: Solving the objective function with powell optimal solution and optimal value, has verified the correctness of the algorithm, the visual c++6.0 under development)

电磁计算 几何光学 计算电磁学 几何光学法 程序(computational electromagnetic GO)

EDFA的掺铒光纤长度模拟计算，附3篇相关论文，330mw的980nm泵泵浦，3db的泵浦吸收，12m最佳长度已经的到验证，可作为EDFA设计参考 (EDFA erbium-doped fiber length simulation of three related papers, 330mw of 980nm pump pump pump 3db absorption 12m optimum length has been to verify, can be used as the EDFA design reference)

用幂法反幂法求解方阵的最大最小特征值及其特征向量，C语言程序，基本的数值计算算法(power method and inverse power method)

湍流模型re计算流体力学软件udf 可以用于修改UDF文件(turbulence model FLUENT)

说明：  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。 在各种优化算法中，共轭梯度法是非常重要的一种。其优点是所需存储量小，具有步收敛性，稳定性高，而且不需要任何外来参数。(Conjugate gradient method Gradient) is a method between the steepest descent method and Newton's method. It only uses the first derivative information, but overcomes the disadvantage of slow convergence of steepest descent method, and avoids the disadvantage of storing and calculating Hesse matrix and solving inverse of Newton's method. Conjugate gradient method is not only one of the most useful methods to solve large-scale linear equations, but also the most effective method to solve large-scale nonlinear optimization One of the algorithms of. Among all kinds of optimization algorithms, conjugate gradient method is very important. It has the advantages of small storage, step convergence, high stability and no external parameters.)