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) between the steepest descent between law and Newton'

本代码为《最优化理论与方法》书籍中的共轭梯度法算法的代码，并举了两个书上的作业题的例子，本代码中的目标函数和约束等初值可由需要进行改变，来得到所期望的计算-The code for the " optimization theory and method" books conjugate gradient method algorithm code, citing two books on the example of the job title, the code in th

最优化问题中的最速下降法，共轭梯度法，黄金分割法代码，为applied optimization with matlab programming书中源代码-Steepest descent method, conjugate gradient method, golden section method code

最优化算法程序，包含费线性最小二乘、共轭梯度法、牛顿法-Linear least squares optimization algorithm procedures, including fees, conjugate gradient method, Newton method and so on

Simulated annealing (SA) is a probabilistic technique for approximating the global optimum of a given function. Specifically, it is a metaheuristic to approximate global optimization in a large search space. It is often used when the search space is