With an emphasis on timeless essential mathematical background for optimization, this textbook provides a comprehensive and accessible introduction to convex optimization for students in applied mathematics, computer science, and engineering. Authored by two influential researchers, the book covers both convex analysis basics and modern topics such as conic programming, conic representations of convex sets, and cone-constrained convex problems, providing readers with a solid, up-to-date understanding of the field. By excluding modeling and algorithms, the authors are able to discuss the theoretical aspects in greater depth. Over 170 in-depth exercises provide hands-on experience with the theory, while more than 30 'Facts' and their accompanying proofs enhance approachability. Instructors will appreciate the appendices that cover all necessary background and the instructors-only solutions manual provided online. By the end of the book, readers will be well equipped to engage with state-of-the-art developments in optimization and its applications in decision-making and engineering.
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Fatma Kılınç-Karzan is a Professor of Operations Research at Tepper School of Business, Carnegie Mellon University. She was awarded the 2015 INFORMS Optimization Society Prize for Young Researchers, the 2014 INFORMS JFIG Best Paper Award (with S. Yıldız), and an NSF CAREER Award in 2015. Her research focuses on foundational theory and algorithms for convex optimization and structured nonconvex optimization and their applications in optimization under uncertainty, machine learning, and business analytics. She has been an elected member on the councils of the Mathematical Optimization Society and INFORMS Computing Society, and has served on the editorial boards of MOS-SIAM Book Series on Optimization, MathProgA, MathOR, OPRE, SIAM J Opt, IJOC, and OMS.
Arkadi Nemirovski is the John P. Hunter, Jr. Chair and Professor of Industrial and Systems Engineering at Georgia Tech. He has co-authored six optimization textbooks including this one, and he has received many rewards for his contributions to the field, including the 1982 MPS-SIAM Fulkerson Prize (with L. Khachiyan and D. Yudin), the 1991 MPS-SIAM Dantzig Prize (with M. Grotschel), the 2003 INFORMS John von Neumann Theory Prize (with M. Todd), the 2019 SIAM Norbert Wiener Prize (with M. Berger), and the 2023 WLA Prize (with Yu. Nesterov). His research focuses on convex optimization (algorithmic design and complexity analysis, optimization under uncertainty, engineering applications) and nonparametric statistics. He is a member of the National Academy of Engineering, the American Academy of Arts and Sciences, and National Academy of Sciences.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
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Hardcover. Etat : new. Hardcover. With an emphasis on timeless essential mathematical background for optimization, this textbook provides a comprehensive and accessible introduction to convex optimization for students in applied mathematics, computer science, and engineering. Authored by two influential researchers, the book covers both convex analysis basics and modern topics such as conic programming, conic representations of convex sets, and cone-constrained convex problems, providing readers with a solid, up-to-date understanding of the field. By excluding modeling and algorithms, the authors are able to discuss the theoretical aspects in greater depth. Over 170 in-depth exercises provide hands-on experience with the theory, while more than 30 'Facts' and their accompanying proofs enhance approachability. Instructors will appreciate the appendices that cover all necessary background and the instructors-only solutions manual provided online. By the end of the book, readers will be well equipped to engage with state-of-the-art developments in optimization and its applications in decision-making and engineering. This textbook offers a mathematically rigorous introduction to convex optimization, blending classical theory with modern topics. Its elementary treatment based on linear algebra, calculus, and real analysis, focus on mathematical foundations, conversational tone, and over 170 exercises make this one of the most accessible textbooks on the topic. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. N° de réf. du vendeur 9781009510523
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Hardback. Etat : New. With an emphasis on timeless essential mathematical background for optimization, this textbook provides a comprehensive and accessible introduction to convex optimization for students in applied mathematics, computer science, and engineering. Authored by two influential researchers, the book covers both convex analysis basics and modern topics such as conic programming, conic representations of convex sets, and cone-constrained convex problems, providing readers with a solid, up-to-date understanding of the field. By excluding modeling and algorithms, the authors are able to discuss the theoretical aspects in greater depth. Over 170 in-depth exercises provide hands-on experience with the theory, while more than 30 'Facts' and their accompanying proofs enhance approachability. Instructors will appreciate the appendices that cover all necessary background and the instructors-only solutions manual provided online. By the end of the book, readers will be well equipped to engage with state-of-the-art developments in optimization and its applications in decision-making and engineering. N° de réf. du vendeur LU-9781009510523
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