000			02077nam a22003017a 4500
005			20260717181017.0
008			260717b |||||||| |||| 00| 0 eng d
020			_a9780262046985
041			_aeng
082			_a519.76 _bT22 HAZ
100			_aHazan, Elad _927030
245	1		_aIntroduction to online convex optimization / _cElad Hazan
250			_a2nd.
260			_aEngland : _bThe MIT Press Combridge, _c2022.
300			_axix, 222p.
520			_aIn 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.
650		4	_aConvex programming _927031
650		4	_aMathematical optimization _923504
650		4	_aMachine learning _922944
650		4	_aOnline algorithms _927032
650		4	_aGradient methods (Mathematics) _927033
650		4	_aGame theory _923230
650		4	_aStatistical learning _927034
650		4	_aArtificial intelligence _xMathematics _927035
650		4	_aAlgorithms
650		4	_aOperations research _926288
942			_cBK
999			_c33522 _d33522