02077nam a22003017a 4500 20260717181017.0 260717b |||||||| |||| 00| 0 eng d 9780262046985 eng 519.76 T22 HAZ Hazan, Elad 27030 Introduction to online convex optimization / Elad Hazan 2nd. England : The MIT Press Combridge, 2022. xix, 222p. In many practical applications, the environment is so complex that it is not feasible to lay out a comprehensive theoretical model and use classical algorithmic theory and/or mathematical optimization. Introduction to Online Convex Optimization presents a robust machine learning approach that contains elements of mathematical optimization, game theory, and learning theory: an optimization method that learns from experience as more aspects of the problem are observed. This view of optimization as a process has led to some spectacular successes in modeling and systems that have become part of our daily lives. Based on the “Theoretical Machine Learning” course taught by the author at Princeton University, the second edition of this widely used graduate level text features thoroughly updated material throughout; new chapters on boosting, adaptive regret, and approachability; expanded exposition on optimization; examples of applications, including prediction from expert advice, portfolio selection, matrix completion and recommendation systems, and SVM training, offered throughout; and exercises that guide students in completing parts of proofs. Convex programming 27031 Mathematical optimization 23504 Machine learning 22944 Online algorithms 27032 Gradient methods (Mathematics) 27033 Game theory 23230 Statistical learning 27034 Artificial intelligence Mathematics 27035 Algorithms Operations research 26288 BK 33522 33522 0 0 0 0 CLIB CLIB IITRPR 2026-07-17 Intercontinental Book Agency 5718.60 1 1 519.76 T22 HAZ 23123 2026-09-22 2026-07-25 16:12:36 2026-07-25 2026-07-17 BK