minimize(method=’trust-exact’)

torchmin.trustregion._minimize_trust_exact(fun, x0, **trust_region_options)[source]

Minimization of scalar function of one or more variables using a nearly exact trust-region algorithm.

Parameters

  • fun (callable) – Scalar objective function to minimize.

  • x0 (Tensor) – Initialization point.

  • initial_tr_radius (float) – Initial trust-region radius.

  • max_tr_radius (float) – Maximum value of the trust-region radius. No steps that are longer than this value will be proposed.

  • eta (float) – Trust region related acceptance stringency for proposed steps.

  • gtol (float) – Gradient norm must be less than gtol before successful termination.

Returns

result – Result of the optimization routine.

Return type

OptimizeResult

Notes

This trust-region solver was based on 1, 2 and 3, which implement similar algorithms. The algorithm is basically that of 1 but ideas from 2 and 3 were also used.

References

1(1,2)

A.R. Conn, N.I. Gould, and P.L. Toint, “Trust region methods”, Siam, pp. 169-200, 2000.

2(1,2)

J. Nocedal and S. Wright, “Numerical optimization”, Springer Science & Business Media. pp. 83-91, 2006.

3(1,2)

J.J. More and D.C. Sorensen, “Computing a trust region step”, SIAM Journal on Scientific and Statistical Computing, vol. 4(3), pp. 553-572, 1983.