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Minimize quadratic function subject to linear equality constraints with SciPy

I have a reasonably simple constrained optimization problem but get different answers depending on how I do it. Let's get the import and a pretty print function out of the way first:

import numpy as np
from scipy.optimize import minimize, LinearConstraint, NonlinearConstraint, SR1

def print_res( res, label ):
    print("\n\n ***** ", label, " ***** \n")
    print(res.message)
    print("obj func value at solution", obj_func(res.x))
    print("starting values: ", x0)
    print("ending values:   ", res.x.astype(int) )
    print("% diff", (100.*(res.x-x0)/x0).astype(int) )
    print("target achieved?",target,res.x.sum())
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The sample data …

python numpy mathematical-optimization scipy quadratic-programming

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