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Introduction to Online Convex Optimization
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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 Non-exclusive license
  • Hardcover: 248 pages
  • eBook: PDF (Manuscript, 260 pages)
  • Language: English
  • ISBN-10: 0262046989
  • ISBN-13: 978-0262046985
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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 Israeli-American 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.
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