{"product_id":"alternating-direction-method-of-multipliers-for-machine-learning-9789811698422","title":"Alternating Direction Method of Multipliers for Machine Learning","description":"\u003cp\u003e\u003c\/p\u003e\u003cblockquote\u003e\n\u003cbr\u003eMachine learning heavily relies on optimization algorithms, and the alternating direction method of multipliers (ADMM) is a commonly used algorithm to solve constrained problems. This book provides a state-of-the-art review on ADMM under various scenarios, including deterministic and convex optimization, nonconvex optimization, stochastic optimization, and distributed optimization, and is an excellent reference book for users seeking a universal algorithm for constrained problems. \u003c\/blockquote\u003e\u003cp\u003e\u003cstrong\u003eFormat\u003c\/strong\u003e: Paperback \/ softback\u003cbr\u003e\u003cstrong\u003eLength\u003c\/strong\u003e: 263 pages\u003cbr\u003e\u003cstrong\u003ePublication date\u003c\/strong\u003e: 17 June 2023\u003cbr\u003e\u003cstrong\u003ePublisher\u003c\/strong\u003e: Springer Verlag, Singapore\u003cbr\u003e\u003c\/p\u003e \u003cp\u003e\u003cbr\u003eMachine 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.\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003cstrong\u003eDimension\u003c\/strong\u003e: 235 x 155 (mm)\u003cbr\u003e\u003cstrong\u003eISBN-13\u003c\/strong\u003e: 9789811698422\u003cbr\u003e \u003cstrong\u003eEdition number\u003c\/strong\u003e: 1st ed. 2022\u003c\/p\u003e","brand":"Zhouchen Lin,Huan Li,Cong Fang","offers":[{"title":"Paperback \/ softback","offer_id":45811777372410,"sku":"9789811698422","price":99.95,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0522\/4297\/2845\/files\/1714155536633_book.jpg?v=1714332265","url":"https:\/\/shulphink.com\/products\/alternating-direction-method-of-multipliers-for-machine-learning-9789811698422","provider":"Shulph Ink","version":"1.0","type":"link"}