Python

scipy.minimize.py

#!/usr/bin/env python
# -*- coding: utf-8 -*-

from scipy.optimize import minimize


def test01():
    """
    优化单一变量
    :return:
    """

    # function to minimize
    def fun(x):
        y = x ** 2 - 12 * x + 20
        return y

    x_start = 2.0

    result = minimize(fun, x_start, options={"disp": True})

    if result.success:
        print("Success!")
        # 返回值：
        # x: solution array,
        # success: a Boolean flag indicating if the optimizer exited successfully
        # fun：values of objective function
        print(f" x = {result.x} y = {result.fun}")
    else:
        print("Could not find the solution")


def test02():
    """
    优化多个变量（用列表的形式）+ 提供约束
    :return:
    """

    # function to maximize
    def fun(xy):
        x = xy[0]
        y = xy[1]
        print(x) 
        area = x * y
        return -area

    x_start = [50, 50]
    # 约束
    cons = ({'type': 'eq', 'fun': lambda xy: (2 * xy[0]) + xy[1] - 100})
    # 约束
    bounds = ((1, 100), (1, 100))
    result = minimize(fun, x_start, options={"disp": True}, constraints=cons, bounds=bounds)

    if result.success:
        print("Success!")
        # 返回值：
        # x: solution array,
        # success: a Boolean flag indicating if the optimizer exited successfully
        # fun：values of objective function
        print(f" x = {result.x} y = {result.fun}")
    else:
        print("Could not find the solution")