python - Solving constrained maximization with SciPy -

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function maximize:

x[0] + x[1] + x[2]

constraints:

0.2 * x[0] + 0.4 * x[1] - 0.33 * x[2] <= 25 5 * x[0] + 8.33 * x[2] <= 130 ... x[0] >= 0 x[1] >= 0 x[2] >= 0

my code looks like:

from numpy import * scipy.optimize import minimize   cons = ({'type': 'ineq', 'fun': lambda x:  array([25 - 0.2 * x[0] - 0.4 * x[1] - 0.33 * x[2]])},         {'type': 'ineq', 'fun': lambda x:  array([130 - 5 * x[0] - 8.33 * x[2]])},         {'type': 'ineq', 'fun': lambda x:  array([16 - 0.6 * x[1] - 0.33 * x[2]])},         {'type': 'ineq', 'fun': lambda x:  array([7 - 0.2 * x[0] - 0.1 * x[1] - 0.33 * x[2]])},         {'type': 'ineq', 'fun': lambda x:  array([14 - 0.5 * x[1]])},         {'type': 'ineq', 'fun': lambda x:  array([x[0]])},         {'type': 'ineq', 'fun': lambda x:  array([x[1]])},         {'type': 'ineq', 'fun': lambda x:  array([x[2]])})   f = lambda x: -1 * (x[0] + x[1] + x[2])  res = minimize(f, [0, 0, 0], method='slsqp', constraints=cons, options={'disp': true})  print(res)

unfortunately, result got is:

positive directional derivative linesearch    (exit mode 8)             current function value: -18240083542.4             iterations: 20             function evaluations: 180             gradient evaluations: 16        x: array([  6.05846118e+09,   6.05846118e+09,   6.12316118e+09])      jac: array([ 0.,  0.,  0.,  0.])  message: 'positive directional derivative linesearch'      fun: -18240083542.377449   status: 8     njev: 16     nfev: 180      nit: 20  success: false

i can solve problem in excel solver, guess wrong in python.

bug in scipy, code works under python2, refuses work under python3

issue on github: https://github.com/scipy/scipy/issues/4240


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