设计一个python程序通过鲍威尔算法进行一个6参数的非线性优化

好的，下面是一个实现鲍威尔算法的简单Python程序：

import numpy as np
from scipy.optimize import minimize

def powell(func, x0, args=(), xtol=1e-8, ftol=1e-8, maxiter=None, maxfun=None,
           full_output=False, disp=False, retall=False, callback=None):
    """Minimize a function using modified Powell's method.
    Optimize the function, `func`, whose gradient is not available, using a
    modification of Powell's conjugate direction method. The algorithm uses
    the technique of backtracking along the search direction to ensure
    convergence.
    """
    x0 = np.asarray(x0).flatten()
    fcalls, func = wrap_function(func, args)
    x = x0
    if retall:
        allvecs = [x0]
    N = len(x0)
    rank = N
    if maxiter is None:
        maxiter = N * 200
    if maxfun is None:
        maxfun = N * 200
    if rank == 0:
        return optimize.OptimizeResult(fun=func(x0), x=x0, nit=0,
                                        nfev=fcalls[0], success=True)

    R = np.eye(N, dtype=int)
    for k in range(rank):
        d = R[k]
        x1 = x + d
        fx = func(x1)
        fx2 = fx
        loop_again = True
        i = 0
        while loop_again and i < maxiter:
            if fx2 > fx:
                t = 2 * (fx2 - 2 * fx + func(x - d))
                t = t * (fx2 - fx - t) / (2 * t ** 2)
                if t < 0:
                    t = min(2 * (fx2 - fx) / t, 0.1 * np.abs(x1 - x))
                else:
                    t = 0.1 * np.abs(x1 - x)
                x1 = x + t * d
                fx2 = func(x1)
                if fx > fx2:
                    x = x1
                    fx = fx2
                i += 1
            else:
                loop_again = False
        d = x1 - x
        x = x1
        if retall:
            allvecs.append(x)
        if callback is not None:
            callback(x)
        if k < rank - 1:
            for j in range(k, N):
                R[j, k] = np.dot(R[j], d)
            for j in range(k + 1, N):
                R[k, j] = 0.
    return optimize.OptimizeResult(fun=fx, x=x, nit=i, nfev=fcalls[0], success=True)

这个程序实现了一个