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from openopt import NLP |
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from numpy import cos, arange, ones, asarray, abs, zeros |
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N = 30 |
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M = 5 |
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ff = lambda x: ((x-M)**2).sum() |
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p = NLP(ff, cos(arange(N))) |
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|
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def df(x): |
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r = 2*(x-M) |
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r[0] += 15 #incorrect derivative |
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r[8] += 80 #incorrect derivative |
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return r |
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p.df = df |
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|
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p.c = lambda x: [2* x[0] **4-32, x[1]**2+x[2]**2 - 8] |
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|
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def dc(x): |
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r = zeros((2, p.n)) |
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r[0,0] = 2 * 4 * x[0]**3 |
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r[1,1] = 2 * x[1] |
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r[1,2] = 2 * x[2] + 15 #incorrect derivative |
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return r |
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p.dc = dc |
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|
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p.h = lambda x: (1e1*(x[-1]-1)**4, (x[-2]-1.5)**4) |
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|
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def dh(x): |
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r = zeros((2, p.n)) |
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r[0,-1] = 1e1*4*(x[-1]-1)**3 |
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r[1,-2] = 4*(x[-2]-1.5)**3 + 15 #incorrect derivative |
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return r |
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p.dh = dh |
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|
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p.checkdf() |
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p.checkdc() |
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p.checkdh() |
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""" |
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you can use p.checkdF(x) for other point than x0 (F is f, c or h) |
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p.checkdc(myX) |
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or |
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p.checkdc(x=myX) |
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values with difference greater than |
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maxViolation (default 1e-5) |
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will be shown |
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p.checkdh(maxViolation=1e-4) |
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p.checkdh(myX, maxViolation=1e-4) |
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p.checkdh(x=myX, maxViolation=1e-4) |
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|
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################################################################################# |
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Typical output (unfortunately, in terminal or other IDEs the blank space used in strings separation can have other lengths): |
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Note that RD (relative difference) is defined as |
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int(ceil(log10(abs(Diff) / maxViolation + 1e-150))) |
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where |
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Diff = 1 - (info_user+1e-8)/(info_numerical + 1e-8) |
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|
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OpenOpt checks user-supplied gradient df (shape: (30,) ) |
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according to: |
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prob.diffInt = [ 1.00000000e-07] |
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|1 - info_user/info_numerical| <= prob.maxViolation = 0.01 |
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df num user-supplied numerical RD |
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0 +7.000e+00 -8.000e+00 3 |
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8 -2.291e+00 -1.029e+01 2 |
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max(abs(df_user - df_numerical)) = 14.9999995251 |
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(is registered in df number 0) |
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======================== |
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OpenOpt checks user-supplied gradient dc (shape: (2, 30) ) |
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according to: |
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prob.diffInt = [ 1.00000000e-07] |
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|1 - info_user/info_numerical| <= prob.maxViolation = 0.01 |
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dc num i,j:dc[i]/dx[j] user-supplied numerical RD |
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32 1 / 2 +1.417e+01 -8.323e-01 4 |
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max(abs(dc_user - dc_numerical)) = 14.9999999032 |
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(is registered in dc number 32) |
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======================== |
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OpenOpt checks user-supplied gradient dh (shape: (2, 30) ) |
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according to: |
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prob.diffInt = [ 1.00000000e-07] |
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|1 - info_user/info_numerical| <= prob.maxViolation = 0.01 |
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dh num i,j:dh[i]/dx[j] user-supplied numerical RD |
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58 1 / 28 -4.474e+01 -5.974e+01 2 |
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max(abs(dh_user - dh_numerical)) = 14.9999962441 |
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(is registered in dh number 58) |
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======================== |
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|
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""" |
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