我有一个包含1070个数字（账户余额）的列表，从一个源系统中提取出来，但由于技术问题，其中一些值的符号错误（例如，我得到100而不是-100）
我只知道一件事，即所有值都有正确的符号后，它们的总和将为零（但我不知道多少个数字的符号是错误的）。
我尝试使用下面的算法进行暴力解决方案，但仅当我只组合3个数字时（即对1070个元素中的3个数字进行符号翻转）就已经需要很长时间。
我认为，即使我优化代码，例如，如果我反转1070个元素中的565个元素，则组合数量如此之大（5,740547E+319），以至于任何暴力尝试都会失败。
基本上，我正在尝试反转列表中的一个值，然后检查总和是否为0，然后是2个值、3个值等等。
当然，如果只有6个数字，它运行得很好，如下所示。
有没有人有更聪明的方法来解决这个问题？
import itertools
import numpy as np
test_list = np.array([1,2,3,4,5,9])
index_list = np.arange(0, len(test_list), dtype=np.intp)
sum_list = test_list.sum()
for L in range(1, len(test_list)+1):
print('current list lenght: ' + str(L))
for subset in itertools.combinations(index_list, L):
cur_list = np.array(test_list)
check = sum_list - (2 * cur_list[[subset]].sum())
if check == 0:
print('found:', subset)
这个简单的例子有多个解决方案，实际值更加独特（正负都有），所以不必担心存在多个解决方案。
我的列表如下（已删除零值）：
[511720.02, 39409.62, 14680.43, 731.29, 10387.38, 2256.56, 262681.13, 5897.66, 11060.76, 144271.31, 25914.87, 13015.5, 19316.85, 40208606.69, -5846350.44, -33538929.18, -540682.74, 68089.94, 561696.88, 58050.78, 469706.4, 524.67, 472701.44, 2158456.56, 1158292.03, 100024.37, 18134.69, 337825.89, 10180.76, 2228315.58, 772.53, 8549.91, 43307.27, 39642.75, 72176.45, 323822.22, 962998.36, 135.5, 134388.73, 1473523.1, 275221.49, 34267.25, 549705.04, 247.87, 12958.72, 847714.28, 1781791.26, 3010.0, 1895276.98, 14434.11, 104587.1, 38056.23, 24962.62, 92497.96, 1227702.25, 110346.36, 391.85, 208.41, 8756.85, 782736.47, 3147519.19, 194767.75, 295568.31, 100694.2, 1893.93, 1133698.81, 523.2, 280280.91, 3078.82, 289841.77, 515.2, 175112.67, 24084.99, 35048.85, 49398.15, 41109.64, 1023.53, 782319.87, 28.14, 20.75, 13227.61, 14765.65, 41182.6, 4297.29, 1340.99, 826014.08, 6111.16, 965389.36, 673482.76, 109298.96, 39807.35, 311.79, 437149.28, 1950968.88, 325.21, 2764917.36, 8838.14, 202885.02, 25.49, 999.66, 743027.37, 759.22, 2950.0, 96146.21, 165.2, 21956.69, 1811289.92, 58.0, 6982282.03, 169464.71, 537268.28, 32655.3, 2474912.35, 1066214.61, 87962.0, 81526.67, 49053309.4, 45032405.02, 49548828.59, 48930853.47, 3533339.79, 3343489.4, 61758.14, 52772.61, 1095708.2, 819624.62, 141.17, 8611.75, 88752.23, 14669477.14, 22308370.28, 1215208.59, -183222.0, 99579.07, 621585.75, -26967.75, -787397.0, 18509117.59, 1212170.47, 60214.78, -749.37, 77455.94, 787350.1, 32760.21, 8117.1, 18493.03, 8267483.6, -15627.16, 58625.11, 38622.21, 57.11, 47565.71, 3523.6, 3205.0, 113242.59, 73.76, 782949.24, 5368.09, -4300.0, 2160047.12, -3533.08, 2460435.1, 50990.31, 93463.6, 1487.07, 22470.0, 22470.0, 999344.79, 135237.18, 9281.62, 3430281.72, 7126642.53, -53338.0, 9412.32, 3344946.88, 83075155.43, 17856133.2, 13449.34, 3845.0, 689998.56, 337645.3, 5937356.47, -2491.16, 2199069.77, 167652.42, 3331515.65, 1124710.59, 34075.85, 494639.09, 385540.84, 5891.39, 14378783.44, 7111253.62, 1247695.94, 1.0, 1.0, 138841.22, 0.5, 5979482.63, 1263134.4, 1.0, 5700.4, 12000000.0, 6100000.0, 1850000.0, 1000000.0, 500000.0, 51000.0, 77081.0, 19288853.74, 11600000.0, 4094000.0, 100000.0, 32400.0, 80000.0, 100000.0, 40810000.0, 33500000.0, 2336976.0, 812290.0, 177655.1, 1187432.01, 5224021.89, 4733986.5, 6174765.0, 1646604.82, 535146.3, 695690.19, 2858833.86, 863125.08, 859612.55, 631772.99, 100000.0, -200000.0, -177654.1, 10950.0, 25040.2, 1659670.0, 1143444.46, 3861357.87, 505930.63, 573072.0, 105000.0, 57500.0, -505929.63, -57499.0, 3402777.8, 54270.0, 105000.0, 44717.27, 1725619.5, 928972.0, 382924.78, 110015.09, 500.0, 100000.0, -1779887.33, -105000.0, -44717.27, -1311895.78, 19257544.9, 786109.73, 3885330.13, 9200000.0, 4347805.0, 464486.0, 13300000.0, 500000.0, 900000.0, 4000000.0, 550000.0, 46633144.87, 3657123.02, 2400000.0, 775000.0, 600000.0, 4980000.0, 715092.4, 250000.0, -715092.4, 5407573.99, 6516939.56, 71570143.67, -59186479.4, 1058557.22, -772031.22, 210000.0, 1753591.07, 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