000			02013nam a22002177a 4500
008			240509b 2023|||||||| |||| 00| 0 eng d
020			_a9781944660529 (PB)
041			_aeng
080			_a517 _bCAR
100			_aCarlier, Guillaume
245			_aClassical and Modern Optimization
260			_aNew Jersey _bWorld Scientific _c2023
300			_axiii, 371p.
505			_a 1. Topological and functional analytic preliminaries 2. Differential calculus 3. Convexity 4. Optimality conditions for differentiable optimization 5. Problems depending on a parameter 6. Convex duality and applications 7. Iterative methods for convex minimization 8. When optimization and data meet 9. An invitation to the calculus of variations
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			_aConvexity
650			_aCalculus of Variations
690			_aMathematics
942			_cBK
999			_c60262 _d60262