libri scuola books Fumetti ebook dvd top ten sconti 0 Carrello


Torna Indietro

lin zhouchen; li huan; fang cong - alternating direction method of multipliers for machine learning
Zoom

Alternating Direction Method of Multipliers for Machine Learning

; ;




Disponibilità: Normalmente disponibile in 15 giorni


PREZZO
151,98 €
NICEPRICE
144,38 €
SCONTO
5%



Questo prodotto usufruisce delle SPEDIZIONI GRATIS
selezionando l'opzione Corriere Veloce in fase di ordine.


Pagabile anche con Carta della cultura giovani e del merito, 18App Bonus Cultura e Carta del Docente


Facebook Twitter Aggiungi commento


Spese Gratis

Dettagli

Genere:Libro
Lingua: Inglese
Editore:

Springer

Pubblicazione: 06/2023





Trama

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.





Sommario

Chapter 1. Introduction.- Chapter 2. Derivations of ADMM.- Chapter 3. ADMM for Deterministic and Convex Optimization.- Chapter 4. ADMM for Nonconvex Optimization.- Chapter 5.  ADMM for Stochastic Optimization.- Chapter 6. ADMM for Distributed Optimization.- Chapter 7. Practical Issues and Conclusions.




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.





I LIBRI CHE INTERESSANO A CHI HA I TUOI GUSTI

Cybernetic avatar
The pragmatic programmer for machine learning
Biological antenna to the humanoid bot



I LIBRI ACQUISTATI DA CHI HA I TUOI GUSTI

Crisi e insolvenza dopo il correttivo ter - g.u. 27 settembre 2024, n.227
Non c'e' posto per l'amore, qui
Volontariato competente. riconoscere gli apprendimenti nella partecipazione soci
Manuale di meccanica
L'ultimo segreto



Altre Informazioni

ISBN:

9789811698422

Condizione: Nuovo
Dimensioni: 235 x 155 mm
Formato: Brossura
Illustration Notes:XXIII, 263 p. 1 illus.
Pagine Arabe: 263
Pagine Romane: xxiii


Dicono di noi





Per noi la tua privacy è importante


Il sito utilizza cookie ed altri strumenti di tracciamento che raccolgono informazioni dal dispositivo dell’utente. Oltre ai cookie tecnici ed analitici aggregati, strettamente necessari per il funzionamento di questo sito web, previo consenso dell’utente possono essere installati cookie di profilazione e marketing e cookie dei social media. Cliccando su “Accetto tutti i cookie” saranno attivate tutte le categorie di cookie. Per accettare solo deterninate categorie di cookie, cliccare invece su “Impostazioni cookie”. Chiudendo il banner o continuando a navigare saranno installati solo cookie tecnici. Per maggiori dettagli, consultare la Cookie Policy.

Impostazioni cookie
Rifiuta Tutti i cookie
Accetto tutti i cookie
X