Error estimates for iterative algorithms for minimizing regularized quadratic subproblems
| Autor: | Nicholas I. M. Gould, Valeria Simoncini |
|---|---|
| Přispěvatelé: | Gould N.I.M., Simoncini V. |
| Rok vydání: | 2019 |
| Předmět: |
021103 operations research
Control and Optimization Applied Mathematics 0211 other engineering and technologies error estimate 010103 numerical & computational mathematics 02 engineering and technology Krylov subspace regularized quadratic suubproblem 01 natural sciences Trust-region subproblem Quadratic equation error estimates Applied mathematics Quadratic programming 0101 mathematics Software Mathematics |
| Zdroj: | Optimization methods & software 35 (2019): 304–328. doi:10.1080/10556788.2019.1670177 info:cnr-pdr/source/autori:N.I. Gould and V. Simoncini/titolo:Error estimates for iterative algorithms for minimizing regularized quadratic subproblems/doi:10.1080%2F10556788.2019.1670177/rivista:Optimization methods & software (Print)/anno:2019/pagina_da:304/pagina_a:328/intervallo_pagine:304–328/volume:35 |
| ISSN: | 1029-4937 1055-6788 |
| DOI: | 10.1080/10556788.2019.1670177 |
| Popis: | We derive bounds for the objective errors and gradient residuals when finding approximations to the solution of common regularized quadratic optimization problems within evolving Krylov spaces. These provide upper bounds on the number of iterations required to achieve a given stated accuracy. We illustrate the quality of our bounds on given test examples. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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