Matrix

利用龙格库塔法（等步长，变步长）计算椭圆方程，双曲线方程，抛物线方程等。对各类微分方程进行数值计算。(The use of Runge-Kutta method (such as step, variable step size) calculated elliptic equations, hyperbolic equations, such as parabolic equation. Various types of differential equations for numerical calculation.)

说明：  无约束优化中的共轭梯度算法程序，解压缩后就可以用了(Unconstrained optimization of the conjugate gradient algorithm procedure can be extracted after the)

计算流体力学经典程序Roe格式，每步有说明，适合初学者(Computational Fluid Dynamics classical procedures Roe scheme, each step is described, suitable for beginners)

最小二乘法含有非线性线性拟合附有理论和实际的例子说明比较具有实际应用哟(PLS)

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 (Conjugate Gradient) is between the steepest descent method between the method and Newton' s method, it takes only a first derivative information, but to overcome the steepest descent method convergence slow shortcomings, but also to avoid the Newton method needs to be stored Hesse and disadvantages of computing inverse matrix and the conjugate gradient method is not only one of the most useful methods to solve large linear equations, solution of large-scale nonlinear optimization is one of the most effective algorithm. In various optimization algorithm, conjugate gradient method is a very important one. The advantage is that a small amount of memory required, with step convergence, high stability, and does not require any external parameters.)

Vortex flow around a square cylinder function method of numerical simulation

说明：  基于AOA的室外雷达定位算法仿真，基于TDOA的室外雷达定位算法仿真，基于AOA doa的室外雷达定位算法仿真，三维(Simulation of outdoor radar location algorithm based on AOA, simulation of outdoor radar location algorithm based on TDOA, simulation of outdoor radar location algorithm based on AOA TDOA, 3d)

采用dsp产生lorenz信号，以用于混沌保密通信，程序在ccs下调试并通过(using dsp have lorenz signal to be used in chaotic secure communications, Under procedures in ccs through debugging and)

说明：  复数函数与积分变换 Fourier变换 Laplace逆变换(Complex number function and integral transformation Fourier transformation Laplace inverse transformation)

说明：  ADAM （Adaptive Moment Estimation）是另外一种自适应学习率算法，它结合动量梯度 下降法，在不同参数方向上采用不同学习率，保留前几次迭代的梯度，能够很好 的适应于稀疏数据。(ADAM (Adaptive Moment Estimation) is another adaptive learning rate algorithm, which combines momentum gradient. The descent method, which uses different learning rates in different parameter directions and retains the gradients of previous iterations, is very good. It is suitable for sparse data.)