我从scipy中的优化函数开始。
我试图通过复制Find optimal vector that minimizes function解决方案来创建代码
我有一个数组，其中包含多个列中的序列。我需要将每个列乘以一个权重，这样这些列的最后一行的总和乘以权重就得到一个给定的数字（约束）。
这个序列的和乘以权重得到一个新的序列，我在这里提取了最大下降，我想最小化这个mdd。
我尽我所能地写了我的代码（2个月的Python和3个小时的scipy），但无法解决用于解决问题的函数上的错误信息。
这里是我的代码和任何帮助将不胜感激：

import numpy as np
from scipy.optimize import fmin_slsqp

# based on: https://stackoverflow.com/questions/41145643/find-optimal-vector-that-minimizes-function

# the number of columns (and so of weights) can vary; it should be generic, regardless the number of columns

def mdd(serie):  # finding the max-draw-down of a series (put aside not to create add'l problems)
    min = np.nanargmax(np.fmax.accumulate(serie) - serie)
    max = np.nanargmax((serie)[:min])
    return serie[np.nanargmax((serie)[:min])] - serie[min]  # max-draw-down

# defining the input data

# mat is an array of 5 columns containing series of independent data

mat = np.array([[1, 0, 0, 1, 1],[2, 0, 5, 3, 4],[3, 2, 4, 3, 7],[4, 1, 3, 3.1, -6],[5, 0, 2, 5, -7],[6, -1, 4, 1, -8]]).astype('float32')
w = np.ndarray(shape=(5)).astype('float32')  # 1D vector for the weights to be used for the columns multiplication
w0 = np.array([1/5, 1/5, 1/5, 1/5, 1/5]).astype('float32') # initial weights (all similar as a starting point)
fixed_value = 4.32  # as a result of constraint nb 1

# testing the operations that are going to be used in the minimization

series = np.sum(mat * w0, axis=1)

# objective:

# minimize the mdd of the series by modifying the weights (w)

def test(w, mat):
    series = np.sum(mat * w, axis=1)
    return mdd(series)

# constraints:

def cons1(last, w, fixed_value):  # fixed_value = 4.32
    # the sum of the weigths multiplied by the last value of each column must be equal to this fixed_value
    return np.sum(mat[-1, :] * w) - fixed_value

def cons2(w):  # the sum of the weights must be equal to 1
    return np.sum(w) - 1

# solution:

# looking for the optimal set of weights (w) values that minimize the mdd with the two contraints and bounds being respected

# all w values must be between 0 and 1

result = fmin_slsqp(test, w0, f_eqcons=[cons1, cons2], bounds=[(0.0, 1.0)]*len(w), args=(mat, fixed_value, w0), full_output=True)
weights, fW, its, imode, smode = result
print(weights)