Convex Optimization in Normed Spaces

Juan Peypouquet

ISBN 10: 3319137093 / ISBN 13: 9783319137094

Editore: Springer International Publishing AG, CH, 2015

Riguardo questo articolo

This work is intended to serve as a guide for graduate students and researchers who wish to get acquainted with the main theoretical and practical tools for the numerical minimization of convex functions on Hilbert spaces. Therefore, it contains the main tools that are necessary to conduct independent research on the topic. It is also a concise, easy-to-follow and self-contained textbook, which may be useful for any researcher working on related fields, as well as teachers giving graduate-level courses on the topic. It will contain a thorough revision of the extant literature including both classical and state-of-the-art references.

Codice articolo LU-9783319137094

Dati bibliografici

• Titolo: Convex Optimization in Normed Spaces
• Editore: Springer International Publishing AG, CH
• Data di pubblicazione: 2015
• Lingua: Inglese
• ISBN 10: 3319137093
• ISBN 13: 9783319137094
• Rilegatura: Paperback
• Condizione: New

Descrizione articolo

Riassunto

This work is intended to serve as a guide for graduate students and researchers who wish to get acquainted with the main theoretical and practical tools for the numerical minimization of convex functions on Hilbert spaces. Therefore, it contains the main tools that are necessary to conduct independent research on the topic. It is also a concise, easy-to-follow and self-contained textbook, which may be useful for any researcher working on related fields, as well as teachers giving graduate-level courses on the topic. It will contain a thorough revision of the extant literature including both classical and state-of-the-art references.

Informazioni sull?autore

Juan Peypouquet is an Associate Professor at the Mathematics Department of the Universidad Tecnica Federico Santa Maria. His main research interest is the study of the asymptotic behavior of dynamical systems in a broad sense, along with their applications in variational analysis and optimization.