{"product_id":"accelerated-optimization-for-machine-learning-first-order-algorithms","title":"Accelerated Optimization for Machine Learning: First-Order Algorithms","description":"\u003cp\u003eOur review of Accelerated Optimization for Machine Learning: First-Order Algorithms finds it best suited for graduate students, researchers and practitioners who need a rigorous, up-to-date reference on accelerated first-order methods. The book's single biggest reason to buy is its comprehensive treatment of acceleration across deterministic and stochastic settings, explained by leading experts and framed with authoritative forewords that signal academic relevance. Readers looking for a concise tutorial will find depth and breadth rather than a quick-start guide.\u003c\/p\u003e\n\u003ch2\u003eKey Features\u003c\/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eComprehensive coverage:\u003c\/strong\u003e Presents a wide range of accelerated first-order algorithms that cover both deterministic and stochastic approaches for varied machine learning problems.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eTheoretical depth:\u003c\/strong\u003e Includes rigorous discussions of convergence and acceleration techniques useful for researchers and advanced students seeking formal understanding.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003ePractical scope:\u003c\/strong\u003e Addresses synchronous and asynchronous algorithms, giving readers insight into real-world implementations and parallel computing contexts.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eProblem variety:\u003c\/strong\u003e Discusses unconstrained and constrained formulations as well as convex and non-convex settings, helping practitioners map methods to specific tasks.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eExpert validation:\u003c\/strong\u003e Forewords by Michael I. Jordan, Zongben Xu and Zhi-Quan Luo lend authority and context for the book's place in the literature.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003ch2\u003eWho It's For\u003c\/h2\u003e\n\u003cp\u003eThis book is tailored to advanced undergraduates, graduate students, academic researchers and machine learning engineers who already have a foundational knowledge of optimization and wish to deepen their understanding of accelerated first-order methods. It is especially valuable for those working on algorithm development, theoretical analysis or high-performance implementations.\u003c\/p\u003e\n\u003cp\u003eReaders seeking an introductory or application-first text with extensive coding examples and tutorials should look elsewhere, since the emphasis here is on theory, method variants and state-of-the-art review rather than step-by-step practical labs or extensive sample code.\u003c\/p\u003e\n\u003ch2\u003ePros \u0026amp; Cons\u003c\/h2\u003e\n\u003cp\u003e\u003cstrong\u003ePros\u003c\/strong\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eThorough survey of accelerated algorithms across deterministic and stochastic domains that supports research and advanced coursework.\u003c\/li\u003e\n\u003cli\u003eClear attention to both synchronous and asynchronous implementations, which aids understanding of parallel and distributed settings.\u003c\/li\u003e\n\u003cli\u003eCoverage of constrained and unconstrained, convex and non-convex problems makes the book broadly applicable across machine learning tasks.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e\u003cstrong\u003eCons\u003c\/strong\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eThe text is dense and leans toward formal analysis, so beginners looking for practical tutorials may find it challenging.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003ch2\u003eSpecifications\u003c\/h2\u003e\n\u003ctable\u003e\n\u003ctr\u003e\n\u003ctd\u003eTitle\u003c\/td\u003e\n\u003ctd\u003eAccelerated Optimization for Machine Learning: First-Order Algorithms\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eAuthors\u003c\/td\u003e\n\u003ctd\u003eZhouchen Lin, Huan Li, Cong Fang\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eScope\u003c\/td\u003e\n\u003ctd\u003eAccelerated first-order optimization methods for machine learning\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eAlgorithm types\u003c\/td\u003e\n\u003ctd\u003eDeterministic, stochastic, synchronous, asynchronous\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eProblem settings\u003c\/td\u003e\n\u003ctd\u003eUnconstrained and constrained; convex and non-convex\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eEndorsements\u003c\/td\u003e\n\u003ctd\u003eForewords by Michael I. Jordan, Zongben Xu, Zhi-Quan Luo\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e\n\u003ch2\u003eOur Verdict\u003c\/h2\u003e\n\u003cp\u003eAccelerated Optimization for Machine Learning is a well-structured, authoritative reference for those who need a deep, rigorous account of accelerated first-order methods. It is good value for researchers and advanced practitioners because it consolidates modern techniques, theoretical analysis and implementation considerations into a single resource.\u003c\/p\u003e\n\u003ch2\u003eFrequently Asked Questions\u003c\/h2\u003e\n\u003cp\u003e\u003cstrong\u003eDoes this book cover stochastic acceleration methods?\u003c\/strong\u003e\u003cbr\u003eYes. The book discusses both deterministic and stochastic accelerated first-order algorithms and compares their properties.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eIs prior optimization knowledge required?\u003c\/strong\u003e\u003cbr\u003eYes. The text assumes familiarity with basic optimization and machine learning concepts and is best suited to advanced students or researchers.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eAre there practical code examples included?\u003c\/strong\u003e\u003cbr\u003eThe description emphasizes theory and method review; readers should not expect extensive tutorial-style code or labs within this volume.\u003c\/p\u003e","brand":"Zhouchen Lin, Huan Li, Cong Fang","offers":[{"title":"Default Title","offer_id":48137244868827,"sku":"9811529124","price":131.59,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0724\/1043\/1707\/files\/618WDSU36sS._SL1254.jpg?v=1768417574","url":"https:\/\/gearmusthave.com\/products\/accelerated-optimization-for-machine-learning-first-order-algorithms","provider":"GearMustHave","version":"1.0","type":"link"}