超出范围时scipy.optimize.minimize(method ='SLSQP')内存问题 [英] scipy.optimize.minimize(method=‘SLSQP’) memory issues when outside the bounds

问题描述

我正在使用scipy.optimize.minimize(method=‘SLSQP’)，功能和约束都已插入scipy.interpolate.LinearNDInterpolator.起始值为边界内的随机数.

I am working with scipy.optimize.minimize(method=‘SLSQP’), the function and constraints are interpolated with scipy.interpolate.LinearNDInterpolator. The start values are random numbers inside the bounds.

我正在与之合作:

1. scipy 0.13.3
2. cython 0.20.1

优化有时会运行并给出合理的结果，但有时优化器会开始请求高达20GB的大量内存，然后我的计算机停止工作.这总是发生在边界之外的值.

The optimizations sometimes runs and gives a reasonable results but sometimes the optimizer begins to request huge amounts of memory up to 20GB then my computer stops working. This always occurs with values outside the boundary.

scipy.interpolate.LinearNDInterpolator是否不能与scipy.optimize.minimize(method=‘SLSQP’)一起使用?在边界之外，我没有模拟数据，因此插值给出了fill_Value = 0或fill_value = 1e10.

Is it possible that the scipy.interpolate.LinearNDInterpolator cannot be used with scipy.optimize.minimize(method=‘SLSQP’)? Outside the bounds I have no simulation data so the interpolation gives a fill_Value=0 or fill_value=1e10.

当我使用scipy.optimize.fmin_slsqp时会发生相同的行为

Same behavior occurs when I am working with scipy.optimize.fmin_slsqp

不幸的是，我的代码很大，但是有了这个数据集，我总是遇到内存问题:

Unfortunately my code is very large but with this data set I always get memory issues:

#########################################
###Memory Leak scipy.optimize.minimize###
#########################################
import numpy as np
from scipy.optimize import minimize
from scipy.interpolate import LinearNDInterpolator
def objfun(x):
    print x
    return x[1]

points = np.array([[  0.00000000e+00,   0.00000000e+00],[  0.00000000e+00,   1.00334000e+00],[  0.00000000e+00,   2.00669000e+00],[  7.07700000e+02,   0.00000000e+00],[  7.07700000e+02,   1.00334000e+00],[  7.07700000e+02,   2.00669000e+00],[  1.56890000e+03,   0.00000000e+00],[  1.56890000e+03,   1.00334000e+00],[  1.56890000e+03,   2.00669000e+00],[  2.50080000e+03,   0.00000000e+00],[  2.50080000e+03,   1.00334000e+00],[  2.50080000e+03,   2.00669000e+00],[  3.47090000e+03,   0.00000000e+00],[  3.47090000e+03,   1.00334000e+00],[  3.47090000e+03,   2.00669000e+00],[  4.46380000e+03,   0.00000000e+00],[  4.46380000e+03,   1.00334000e+00],[  4.46380000e+03,   2.00669000e+00],[  5.47130000e+03,   0.00000000e+00],[  5.47130000e+03,   1.00334000e+00],[  5.47130000e+03,   2.00669000e+00],[  6.48890000e+03,   0.00000000e+00],[  6.48890000e+03,   1.00334000e+00],[  6.48890000e+03,   2.00669000e+00],[  7.51360000e+03,   0.00000000e+00],[  7.51360000e+03,   1.00334000e+00],[  7.51360000e+03,   2.00669000e+00],[  8.54350000e+03,   0.00000000e+00],[  8.54350000e+03,   1.00334000e+00],[  8.54350000e+03,   2.00669000e+00],[  9.57740000e+03,   0.00000000e+00],[  9.57740000e+03,   1.00334000e+00],[  9.57740000e+03,   2.00669000e+00],[  1.06143000e+04,   0.00000000e+00],[  1.06143000e+04,   1.00334000e+00],[  1.06143000e+04,   2.00669000e+00],[  1.16535000e+04,   0.00000000e+00],[  1.16535000e+04,   1.00334000e+00],[  1.16535000e+04,   2.00669000e+00],[  1.26945000e+04,   0.00000000e+00],[  1.26945000e+04,   1.00334000e+00],[  1.26945000e+04,   2.00669000e+00],[  1.37369000e+04,   0.00000000e+00],[  1.37369000e+04,   1.00334000e+00],[  1.37369000e+04,   2.00669000e+00],[  1.47804000e+04,   0.00000000e+00],[  1.47804000e+04,   1.00334000e+00],[  1.47804000e+04,   2.00669000e+00],[  1.58248000e+04,   0.00000000e+00],[  1.58248000e+04,   1.00334000e+00],[  1.58248000e+04,   2.00669000e+00],[  1.68698000e+04,   0.00000000e+00],[  1.68698000e+04,   1.00334000e+00],[  1.68698000e+04,   2.00669000e+00],[  1.79153000e+04,   0.00000000e+00],[  1.79153000e+04,   1.00334000e+00],[  1.79153000e+04,   2.00669000e+00],[  1.89612000e+04,   0.00000000e+00],[  1.89612000e+04,   1.00334000e+00],[  1.89612000e+04,   2.00669000e+00],[  2.00074000e+04,   0.00000000e+00],[  2.00074000e+04,   1.00334000e+00],[  2.00074000e+04,   2.00669000e+00]])
values = np.array([  0.00000000e+00,   0.00000000e+00,   0.00000000e+00,4.29730000e+01,   5.72947500e-01,  -5.35464000e-01,9.11676000e+01,   1.31063500e+00,  -1.05937500e+00,1.38660750e+02,   2.11484000e+00,  -1.50850500e+00,1.84497000e+02,   2.96052000e+00,  -1.88466000e+00,2.28622000e+02,   3.83846000e+00,  -2.19702000e+00,2.71163000e+02,   4.74426500e+00,  -2.45397000e+00,3.12274500e+02,   5.67547500e+00,  -2.66222500e+00,3.52102000e+02,   6.63058000e+00,  -2.82711000e+00,3.90774000e+02,   7.60858000e+00,  -2.95286000e+00,4.28399500e+02,   8.60879000e+00,  -3.04289000e+00,4.65074500e+02,   9.63071000e+00,  -3.10001500e+00,5.00881500e+02,   1.06739850e+01,  -3.12655500e+00,5.35893000e+02,   1.17383500e+01,  -3.12444000e+00,5.70166500e+02,   1.28235000e+01,  -3.09540500e+00,6.03760000e+02,   1.39293500e+01,  -3.04082500e+00,6.36721500e+02,   1.50557500e+01,  -2.96194500e+00,6.69093500e+02,   1.62026000e+01,  -2.85982000e+00,7.00915000e+02,   1.73698000e+01,  -2.73539500e+00,7.32222000e+02,   1.85573500e+01,  -2.58950000e+00,7.63042500e+02,   1.97651000e+01,  -2.42286000e+00])

S22_Adh1Ad_inter = LinearNDInterpolator(points,values,1e10)
def Fsigcon(x):
    rf1_int = x[1]
    rf_eval=[]
    x_eval=[]
    interval = np.linspace(0,x[0],x[0]/0.01)
    if interval.size == 0:
        interval=np.array([x[0]])
    for xcoord in interval:
        rf_eval.append(rf1_int)
        x_eval.append(xcoord)
    val_interp = S22_Adh1Ad_inter(rf_eval,x_eval) #'nearest' #'linear' #'cubic'
    out = (val_interp.min()-39.45)
    return out 

points = np.array([[  0.00000000e+00,   0.00000000e+00],[  0.00000000e+00,   1.99997000e-01],[  0.00000000e+00,   4.00002000e-01],[  7.07700000e+02,   1.39999000e-01],[  7.07700000e+02,   3.39996000e-01],[  1.56890000e+03,   8.00020000e-02],[  1.56890000e+03,   2.79999000e-01],[  2.50080000e+03,   1.99970000e-02],[  2.50080000e+03,   2.20001000e-01],[  2.50080000e+03,   1.90000200e+00],[  3.47090000e+03,   1.60004000e-01],[  3.47090000e+03,   3.60001000e-01],[  4.46380000e+03,   9.99980000e-02],[  4.46380000e+03,   3.00003000e-01],[  5.47130000e+03,   4.00010000e-02],[  5.47130000e+03,   2.39998000e-01],[  5.47130000e+03,   3.00000000e+00],[  6.48890000e+03,   1.80000000e-01],[  6.48890000e+03,   3.79997000e-01],[  7.51360000e+03,   1.20003000e-01],[  7.51360000e+03,   3.20000000e-01],[  8.54350000e+03,   5.99980000e-02],[  8.54350000e+03,   2.60002000e-01],[  9.57740000e+03,   0.00000000e+00],[  9.57740000e+03,   1.99997000e-01],[  9.57740000e+03,   4.00002000e-01],[  1.06143000e+04,   1.39999000e-01],[  1.06143000e+04,   3.39996000e-01],[  1.16535000e+04,   8.00020000e-02],[  1.16535000e+04,   2.79999000e-01],[  1.26945000e+04,   1.99970000e-02],[  1.26945000e+04,   2.20001000e-01],[  1.26945000e+04,   1.90000200e+00],[  1.37369000e+04,   1.60004000e-01],[  1.37369000e+04,   3.60001000e-01],[  1.47804000e+04,   9.99980000e-02],[  1.47804000e+04,   3.00003000e-01],[  1.58248000e+04,   4.00010000e-02],[  1.58248000e+04,   2.39998000e-01],[  1.58248000e+04,   3.00000000e+00],[  1.68698000e+04,   1.80000000e-01],[  1.68698000e+04,   3.79997000e-01],[  1.79153000e+04,   1.20003000e-01],[  1.79153000e+04,   3.20000000e-01],[  1.89612000e+04,   5.99980000e-02],[  1.89612000e+04,   2.60002000e-01],[  2.00074000e+04,   0.00000000e+00],[  2.00074000e+04,   1.99997000e-01],[  2.00074000e+04,   4.00002000e-01]])

values = np.array([ 0.      ,  0.      ,  0.      ,  0.010168,  0.010055,  0.046252,0.045731,  0.092687,  0.107056,  0.11196 ,  0.19232 ,  0.190859,0.29924 ,  0.295611,  0.401297,  0.42018 ,  0.450553,  0.564416,0.561699,  0.727387,  0.719631,  0.883825,  0.894486,  0.      ,1.087256,  1.084631,  1.298136,  1.287209,  1.507127,  1.505308,1.424393,  1.740491,  1.839568,  1.993769,  1.981605,  2.251336,2.238475,  2.330676,  2.511822,  2.723058,  2.803453,  2.792818,3.104855,  3.08533 ,  3.29549 ,  3.393902,  0.      ,  3.721085,3.714504])

G_Adh1Ad_inter = LinearNDInterpolator(points,values,0)
def Gcon(x):
    val_interp = G_Adh1Ad_inter(x[1],x[0])
    out = (val_interp.min()-0.33)
    return out

cons = (
    {'type': 'ineq',
    'fun' : Fsigcon},
    {'type': 'ineq',
    'fun' : Gcon}
    )
amin = 0.0
amax = 3.0
bounds=[(amin,amax),(0.0, 20007.400000000001)]
a_start=  1.5343936873636999
rf1_start= 6824.9659188661817
res_int = minimize(objfun, [a_start,rf1_start],method='SLSQP',jac=None,bounds=bounds,constraints=cons,tol =1e-4,options={'iprint':2,  'disp': True , 'maxiter':1e2})

推荐答案

sudo -H pip install --upgrade scipy
sudo -H pip install --upgrade numpy

彻底解决了问题

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