Question

我正在为另一个项目测试scipy.optimize，并在设置最小化使用SLSQP方法的初始条件时发现了一些异常行为。我假设这是因为顺序最小二乘算法在临界点（start_pos1）初始化时立即返回而不检查二阶导数条件。我仔细查看了文档并没有看到任何内容 - 有人能说出发生了什么吗？

from scipy import optimize
import numpy as np


def simplex(x):
    return 1 - sum(x)

def sphere_min(x):
    return sum(x[i]**2 for i in range(len(x)))**.5

def sphere_max(x):
    return -sphere_min(x)


if __name__ == "__main__":
    dimensions = 3
    start_pos1 = np.ones(dimensions) * 1.0/dimensions
    start_pos2 = [2*float(i+1)/(dimensions*(dimensions+1)) for i in range(dimensions)]
    raw_start_pos = np.random.uniform(0, 1, dimensions)
    start_pos3 = raw_start_pos/raw_start_pos.sum()

    bnds = tuple((0, 1) for i in range(dimensions))
    cons = ({'type': 'eq', 'fun': simplex})

    for start_pos in (start_pos1, start_pos2, start_pos3):
        print 'Start Position: {}'.format(start_pos)
        res = optimize.minimize(sphere_min, start_pos, method='SLSQP', bounds=bnds, constraints=cons)
        print 'Min: x = {}, value = {}'.format(res.x, res.fun)
        res = optimize.minimize(sphere_max, start_pos, method='SLSQP', bounds=bnds, constraints=cons)
        print 'Max: x = {}, value = {}'.format(res.x, -res.fun)
        print '\n==============\n'

基于初始条件的异常约束scipy.optimize.minimze行为

0 个答案: