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 Title: Introduction to Online Convex Optimization
 Author(s) Elad Hazan
 Publisher: The MIT Press; 2nd edition (September 6, 2022);
 License(s): arXiv.org Nonexclusive license
 Hardcover: 248 pages
 eBook: PDF (Manuscript, 260 pages)
 Language: English
 ISBN10: 0262046989
 ISBN13: 9780262046985
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Book Description
This book portrays optimization as a process. 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.
This book 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, This book is a widely used graduate level textbook.
About the Authors Elad Hazan is an IsraeliAmerican computer scientist, academic, author and researcher. He is Professor of Computer Science at Princeton University and cofounder and director of Google AI Princeton. An innovator in the design and analysis of algorithms for basic problems in machine learning and optimization, he is coinventor of the AdaGrad optimization algorithm for deep learning, the first adaptive gradient method.
 Operations Research (OR), Mathematical Optimization, Approximation, etc.
 Machine Learning
 Game Theory
 Applied Mathematics

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