The superiorization method with restarted perturbations for split minimization problems with an application to radiotherapy treatment planning
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Título: | The superiorization method with restarted perturbations for split minimization problems with an application to radiotherapy treatment planning |
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Autor/es: | Aragón Artacho, Francisco Javier | Censor, Yair | Gibali, Aviv | Torregrosa-Belén, David |
Grupo/s de investigación o GITE: | Laboratorio de Optimización (LOPT) |
Centro, Departamento o Servicio: | Universidad de Alicante. Departamento de Matemáticas |
Palabras clave: | Superiorization | Bounded perturbation resilience | Split minimization problem | Subvectors | Intensity-modulated radiation therapy | Restart |
Fecha de publicación: | 4-nov-2022 |
Editor: | Elsevier |
Cita bibliográfica: | Applied Mathematics and Computation. 2023, 440: 127627. https://doi.org/10.1016/j.amc.2022.127627 |
Resumen: | In this paper we study the split minimization problem that consists of two constrained minimization problems in two separate spaces that are connected via a linear operator that maps one space into the other. To handle the data of such a problem we develop a superiorization approach that can reach a feasible point with reduced (not necessarily minimal) objective function values. The superiorization methodology is based on interlacing the iterative steps of two separate and independent iterative processes by perturbing the iterates of one process according to the steps dictated by the other process. We include in our developed method two novel elements. The first one is the permission to restart the perturbations in the superiorized algorithm which results in a significant acceleration and increases the computational efficiency. The second element is the ability to independently superiorize subvectors. This caters to the needs of real-world applications, as demonstrated here for a problem in intensity-modulated radiation therapy treatment planning. |
Patrocinador/es: | The work of Yair Censor was supported by the ISF-NSFC joint research plan Grant Number 2874/19. Francisco Aragón and David Torregrosa were partially supported by the Ministry of Science, Innovation and Universities of Spain and the European Regional Development Fund (ERDF) of the European Commission, Grant PGC2018-097960-B-C22, and the Generalitat Valenciana (AICO/2021/165). David Torregrosa was supported by MINECO and European Social Fund (PRE2019-090751) under the program “Ayudas para contratos predoctorales para la formación de doctores” 2019. |
URI: | http://hdl.handle.net/10045/129106 |
ISSN: | 0096-3003 (Print) | 1873-5649 (Online) |
DOI: | 10.1016/j.amc.2022.127627 |
Idioma: | eng |
Tipo: | info:eu-repo/semantics/article |
Derechos: | This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) |
Revisión científica: | si |
Versión del editor: | https://doi.org/10.1016/j.amc.2022.127627 |
Aparece en las colecciones: | INV - LOPT - Artículos de Revistas |
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Aragon-Artacho_etal_2023_ApplMathComput.pdf | 1,83 MB | Adobe PDF | Abrir Vista previa | |
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