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License: GNU General Public License v3.0
Python trust-region subproblem solvers for nonlinear optimization
License: GNU General Public License v3.0
Allow only Hessian-vector products (i.e. H is a function s -> H*s). Having problems interfacing in Fortran (f2py allows callbacks, but it doesn't seem to work properly here for some reason).
It is my understanding that the TRS is solvable even in the case of convex maximization (i.e. a problem that would be unbounded if unconstrained). However, with the code
import trustregion
import numpy as np
n = 5
g = np.zeros(n)
H = -np.eye(n)
delta = 1.0
x = trustregion.solve(g, H, delta)
we should get a nonzero solution, having a negative objective value. However, I get a solution of 0.
Using python 3.8, installed via pip.
If input Hessian is not symmetric, allow option to replace with 0.5*(H + H.T)
Allow elliptical trust regions (i.e. replace ||s||_2 <= delta with sqrt(sAs) <= delta for A symmetric positive definite).
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