Articoli correlati a Alternating Direction Method of Multipliers for Machine...

Alternating Direction Method of Multipliers for Machine Learning - Brossura

 
9789811698422: Alternating Direction Method of Multipliers for Machine Learning

Sinossi

Machine learning heavily relies on optimization algorithms to solve its learning models. Constrained problems constitute a major type of optimization problem, and the alternating direction method of multipliers (ADMM) is a commonly used algorithm to solve constrained problems, especially linearly constrained ones. Written by experts in machine learning and optimization, this is the first book providing a state-of-the-art review on ADMM under various scenarios, including deterministic and convex optimization, nonconvex optimization, stochastic optimization, and distributed optimization. Offering a rich blend of ideas, theories and proofs, the book is up-to-date and self-contained. It is an excellent reference book for users who are seeking a relatively universal algorithm for constrained problems. Graduate students or researchers can read it to grasp the frontiers of ADMM in machine learning in a short period of time.

Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.

Informazioni sull?autore

Zhouchen Lin is a leading expert in the fields of machine learning and optimization. He is currently a professor with the Key Laboratory of Machine Perception (Ministry of Education), School of Artificial Intelligence, Peking University. Prof. Lin served as an area chair many times for prestigious conferences, including CVPR, ICCV, NIPS/NeurIPS, ICML, ICLR, IJCAI and AAAI. He is a Program Co-Chair of ICPR 2022 and a Senior Area Chair of ICML 2022. Prof. Lin is an associate editor of the International Journal of Computer Vision and the Optimization Methods and Software. He is a Fellow of CSIG, IAPR and IEEE.

Huan Li received a doctoral degree in machine learning from Peking University in 2019. He is currently an assistant researcher at the School of Artificial Intelligence, Nankai University. His research interests include optimization and machine learning.

Cong Fang received a doctoral degree in machine learning from Peking University in 2019. He is currently anassistant professor at the School of Artificial Intelligence, Peking University. His research interests include optimization and machine learning.

Dalla quarta di copertina

Machine learning heavily relies on optimization algorithms to solve its learning models. Constrained problems constitute a major type of optimization problem, and the alternating direction method of multipliers (ADMM) is a commonly used algorithm to solve constrained problems, especially linearly constrained ones. Written by experts in machine learning and optimization, this is the first book providing a state-of-the-art review on ADMM under various scenarios, including deterministic and convex optimization, nonconvex optimization, stochastic optimization, and distributed optimization. Offering a rich blend of ideas, theories and proofs, the book is up-to-date and self-contained. It is an excellent reference book for users who are seeking a relatively universal algorithm for constrained problems. Graduate students or researchers can read it to grasp the frontiers of ADMM in machine learning in a short period of time.

Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.

  • EditoreSpringer
  • Data di pubblicazione2023
  • ISBN 10 9811698422
  • ISBN 13 9789811698422
  • RilegaturaCopertina flessibile
  • LinguaInglese
  • Numero edizione1
  • Numero di pagine288
  • Contatto del produttorenon disponibile

EUR 9,70 per la spedizione da Germania a Italia

Destinazione, tempi e costi

Altre edizioni note dello stesso titolo

9789811698392: Alternating Direction Method of Multipliers for Machine Learning

Edizione in evidenza

ISBN 10:  9811698392 ISBN 13:  9789811698392
Casa editrice: Springer-Nature New York Inc, 2022
Rilegato

Risultati della ricerca per Alternating Direction Method of Multipliers for Machine...

Immagini fornite dal venditore

Lin, Zhouchen|Li, Huan|Fang, Cong
ISBN 10: 9811698422 ISBN 13: 9789811698422
Nuovo Brossura
Print on Demand

Da: moluna, Greven, Germania

Valutazione del venditore 5 su 5 stelle 5 stelle, Maggiori informazioni sulle valutazioni dei venditori

Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Machine learning heavily relies on optimization algorithms to solve its learning models. Constrained problems constitute a major type of optimization problem, and the alternating direction method of multipliers (ADMM) is a commonly used algorithm to solv. Codice articolo 877457631

Contatta il venditore

Compra nuovo

EUR 127,40
Convertire valuta
Spese di spedizione: EUR 9,70
Da: Germania a: Italia
Destinazione, tempi e costi

Quantità: Più di 20 disponibili

Aggiungi al carrello

Foto dell'editore

Lin, Zhouchen; Li, Huan; Fang, Cong
Editore: Springer, 2023
ISBN 10: 9811698422 ISBN 13: 9789811698422
Nuovo Brossura

Da: Ria Christie Collections, Uxbridge, Regno Unito

Valutazione del venditore 5 su 5 stelle 5 stelle, Maggiori informazioni sulle valutazioni dei venditori

Condizione: New. In. Codice articolo ria9789811698422_new

Contatta il venditore

Compra nuovo

EUR 143,78
Convertire valuta
Spese di spedizione: EUR 10,60
Da: Regno Unito a: Italia
Destinazione, tempi e costi

Quantità: Più di 20 disponibili

Aggiungi al carrello

Immagini fornite dal venditore

Zhouchen Lin
ISBN 10: 9811698422 ISBN 13: 9789811698422
Nuovo Taschenbuch
Print on Demand

Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania

Valutazione del venditore 5 su 5 stelle 5 stelle, Maggiori informazioni sulle valutazioni dei venditori

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Machine learning heavily relies on optimization algorithms to solve its learning models. Constrained problems constitute a major type of optimization problem, and the alternating direction method of multipliers (ADMM) is a commonly used algorithm to solve constrained problems, especially linearly constrained ones. Written by experts in machine learning and optimization, this is the first book providing a state-of-the-art review on ADMM under various scenarios, including deterministic and convex optimization, nonconvex optimization, stochastic optimization, and distributed optimization. Offering a rich blend of ideas, theories and proofs, the book is up-to-date and self-contained. It is an excellent reference book for users who are seeking a relatively universal algorithm for constrained problems. Graduate students or researchers can read it to grasp the frontiers of ADMM in machine learning in a short period of time. 288 pp. Englisch. Codice articolo 9789811698422

Contatta il venditore

Compra nuovo

EUR 149,79
Convertire valuta
Spese di spedizione: EUR 11,00
Da: Germania a: Italia
Destinazione, tempi e costi

Quantità: 2 disponibili

Aggiungi al carrello

Immagini fornite dal venditore

Zhouchen Lin
ISBN 10: 9811698422 ISBN 13: 9789811698422
Nuovo Taschenbuch
Print on Demand

Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania

Valutazione del venditore 5 su 5 stelle 5 stelle, Maggiori informazioni sulle valutazioni dei venditori

Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Machine learning heavily relies on optimization algorithms to solve its learning models. Constrained problems constitute a major type of optimization problem, and the alternating direction method of multipliers (ADMM) is a commonly used algorithm to solve constrained problems, especially linearly constrained ones. Written by experts in machine learning and optimization, this is the first book providing a state-of-the-art review on ADMM under various scenarios, including deterministic and convex optimization, nonconvex optimization, stochastic optimization, and distributed optimization. Offering a rich blend of ideas, theories and proofs, the book is up-to-date and self-contained. It is an excellent reference book for users who are seeking a relatively universal algorithm for constrained problems. Graduate students or researchers can read it to grasp the frontiers of ADMM in machine learning in a short period of time.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 288 pp. Englisch. Codice articolo 9789811698422

Contatta il venditore

Compra nuovo

EUR 149,79
Convertire valuta
Spese di spedizione: EUR 15,00
Da: Germania a: Italia
Destinazione, tempi e costi

Quantità: 1 disponibili

Aggiungi al carrello

Immagini fornite dal venditore

Zhouchen Lin
ISBN 10: 9811698422 ISBN 13: 9789811698422
Nuovo Taschenbuch

Da: AHA-BUCH GmbH, Einbeck, Germania

Valutazione del venditore 5 su 5 stelle 5 stelle, Maggiori informazioni sulle valutazioni dei venditori

Taschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - Machine learning heavily relies on optimization algorithms to solve its learning models. Constrained problems constitute a major type of optimization problem, and the alternating direction method of multipliers (ADMM) is a commonly used algorithm to solve constrained problems, especially linearly constrained ones. Written by experts in machine learning and optimization, this is the first book providing a state-of-the-art review on ADMM under various scenarios, including deterministic and convex optimization, nonconvex optimization, stochastic optimization, and distributed optimization. Offering a rich blend of ideas, theories and proofs, the book is up-to-date and self-contained. It is an excellent reference book for users who are seeking a relatively universal algorithm for constrained problems. Graduate students or researchers can read it to grasp the frontiers of ADMM in machine learning in a short period of time. Codice articolo 9789811698422

Contatta il venditore

Compra nuovo

EUR 156,28
Convertire valuta
Spese di spedizione: EUR 14,99
Da: Germania a: Italia
Destinazione, tempi e costi

Quantità: 1 disponibili

Aggiungi al carrello

Foto dell'editore

Lin, Zhouchen; Li, Huan; Fang, Cong
Editore: Springer, 2023
ISBN 10: 9811698422 ISBN 13: 9789811698422
Nuovo Brossura

Da: California Books, Miami, FL, U.S.A.

Valutazione del venditore 5 su 5 stelle 5 stelle, Maggiori informazioni sulle valutazioni dei venditori

Condizione: New. Codice articolo I-9789811698422

Contatta il venditore

Compra nuovo

EUR 170,04
Convertire valuta
Spese di spedizione: EUR 7,82
Da: U.S.A. a: Italia
Destinazione, tempi e costi

Quantità: Più di 20 disponibili

Aggiungi al carrello

Foto dell'editore

Lin, Zhouchen/ Li, Huan/ Fang, Cong
Editore: Springer, 2023
ISBN 10: 9811698422 ISBN 13: 9789811698422
Nuovo Paperback

Da: Revaluation Books, Exeter, Regno Unito

Valutazione del venditore 5 su 5 stelle 5 stelle, Maggiori informazioni sulle valutazioni dei venditori

Paperback. Condizione: Brand New. 286 pages. 9.25x6.10x0.60 inches. In Stock. Codice articolo x-9811698422

Contatta il venditore

Compra nuovo

EUR 222,16
Convertire valuta
Spese di spedizione: EUR 11,79
Da: Regno Unito a: Italia
Destinazione, tempi e costi

Quantità: 2 disponibili

Aggiungi al carrello