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Model-Based Derivative-Free Methods for Convex-Constrained Optimization

We present a model-based derivative-free method for optimization subject to general convex constraints, which we assume are unrelaxable and accessed only through a projection operator that is cheap to evaluate. We prove global convergence and a worst-case complexity of iterations and objective evaluations for nonconvex functions, matching results for the unconstrained case. We introduce new, weaker requirements on model accuracy compared to existing methods. As a result, sufficiently accurate interpolation models can be constructed only using feasible points. We develop a comprehensive theory of interpolation set management in this regime for linear and composite linear models. We implement our approach for nonlinear least-squares problems and demonstrate strong practical performance compared to general-purpose solvers.

Ketersediaan

Informasi Detail

Judul Seri

SIAM JOURNAL ON OPTIMIZATION; Vol.32 No.4 October-December 2022

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 2552-2579

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

DERIVATIVE-FREE OPTIMIZATION
TRUST-REGION METHODS
WORST-CASE COMPLEXITY
CONVEX CONSTRAINTS

Info Detail Spesifik

https://doi.org/10.1137/21M1460971

Pernyataan Tanggungjawab

Matthew Hough, Lindon Roberts

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