scipy最优化

scipy最优化

学习scipy进行优化函数：

参考：Optimization and root finding (scipy.optimize) — SciPy v1.9.3 Manual

scipy最优化的功能：

1、SciPy优化提供了最小化(或最大化)目标函数的函数，可以是有约束的；

2、非线性问题的求解器(支持局部和全局优化算法)；

3、线性规划；

4、约束最小二乘和非线性最小二乘；

5、寻根；

6、曲线拟合；

标量函数优化

minimize_scalar(fun[, bracket, bounds, …])	Minimization of scalar function of one variable.一元标量函数的最小化。

The minimize_scalar function supports the following methods:

• minimize_scalar(method=’brent’)
• minimize_scalar(method=’bounded’)
• minimize_scalar(method=’golden’)

局部多变量优化

minimize(fun, x0[, args, method, jac, hess, …])	Minimization of scalar function of one or more variables.一个或多个变量的标量函数的最小化

The minimize function supports the following methods:

• minimize(method=’Nelder-Mead’)
• minimize(method=’Powell’)
• minimize(method=’CG’)
• minimize(method=’BFGS’)
• minimize(method=’Newton-CG’)
• minimize(method=’L-BFGS-B’)
• minimize(method=’TNC’)
• minimize(method=’COBYLA’)
• minimize(method=’SLSQP’)
• minimize(method=’trust-constr’)
• minimize(method=’dogleg’)
• minimize(method=’trust-ncg’)
• minimize(method=’trust-krylov’)
• minimize(method=’trust-exact’)

Constraints are passed to minimize function as a single object or as a list of objects from the following classes:

NonlinearConstraint(fun, lb, ub[, jac, …])	Nonlinear constraint on the variables.
LinearConstraint(A[, lb, ub, keep_feasible])	Linear constraint on the variables.

Simple bound constraints are handled separately and there is a special class for them:（有一个特殊的类是单独处理简单约束的）

Bounds[lb, ub, keep_feasible]	Bounds constraint on the variables.变量的边界约束。

Quasi-Newton strategies implementing HessianUpdateStrategy interface can be used to approximate the Hessian in minimize function (available only for the ‘trust-constr’ method). Available quasi-Newton methods implementing this interface are:（实现HessianUpdateStrategy接口的拟牛顿策略可以用来近似Hessian的最小化函数（仅适用于“信任-constr”方法）。实现此接口的可用拟牛顿方法包括:）

BFGS([exception_strategy, min_curvature, …])	Broyden-Fletcher-Goldfarb-Shanno (BFGS) Hessian update strategy.