Hazan, Elad  <br/><br/>
Introduction to online convex optimization /  
Elad Hazan 
 - 2nd.  
 - England :   The MIT Press Combridge,   2022.  
 - xix, 222p.  
<br/><br/> 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.<br/><br/>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. 
<br/><br/><label>ISBN: </label>9780262046985 
<br/><br/><label>Subjects--Topical Terms: </label><br/>Convex programming<br/>Mathematical optimization<br/>Machine learning<br/>Online algorithms<br/>Gradient methods (Mathematics)<br/>Game theory<br/>Statistical learning<br/>Artificial intelligence--Mathematics<br/>Algorithms<br/>Operations research
<br/><br/><label>Dewey Class. No.: </label>519.76   / T22 HAZ