000			a
999			_c31779 _d31779
008			230416b xxu||||| |||| 00| 0 eng d
020			_a9781944660529
082			_a519.6 _bCAR
100			_aCarlier, Guillaume
245			_aClassical and modern optimization
260			_bWorld Scientific, _c2022 _aNew Jersey :
300			_axiii, 371 p.; _bill., _c23 cm
365			_b1395.00 _cINR _d01
490			_aAdvanced textbooks in mathematics
504			_aIncludes bibliographical references and index.
520			_aThe quest for the optimal is ubiquitous in nature and human behavior. The field of mathematical optimization has a long history and remains active today, particularly in the development of machine learning. Classical and Modern Optimization presents a self-contained overview of classical and modern ideas and methods in approaching optimization problems. The approach is rich and flexible enough to address smooth and non-smooth, convex and non-convex, finite or infinite-dimensional, static or dynamic situations. The first chapters of the book are devoted to the classical toolbox: topology and functional analysis, differential calculus, convex analysis and necessary conditions for differentiable constrained optimization. The remaining chapters are dedicated to more specialized topics and applications. Valuable to a wide audience, including students in mathematics, engineers, data scientists or economists, Classical and Modern Optimization contains more than 200 exercises to assist with self-study or for anyone teaching a third- or fourth-year optimization class.
650			_aMathematical optimization
650			_aFunctional Analysis
650			_aBanach space
650			_a Baire's theorem
650			_aDunford-Pettis theorem
650			_aEnvelope t5heorem
650			_aFenchel-Rockafellar theorem
650			_aGreen formula
650			_aHopf-Lax formula
650			_aInverse function theorem
650			_aKrein-Milman theorem
650			_aLax-Oleinik formula
650			_aMinkowski Farkes theorem
650			_aNewton's law
650			_aRademacher's theorem
650			_aStrong linear programming (LP) duality theorem
942			_2ddc _cBK