000			02913nam a2200349 4500
001			57679
005			20260118111023.0
008			260118s2018 gw |||| o |||| 0|eng
020			_a9783319915784
040			_aDLC _beng _epn _erda _cDLC _dBD-DhIUB
082	0	4	_a519.6 _223 _bN468l
100	1		_aNesterov, Yurii. _eauthor.
245	1	0	_aLectures on Convex Optimization / _cby Yurii Nesterov.
250			_a2nd ed. 2018.
260			_aBelgium: _bSpringer nature , _c2018
300			_a1 online resource (XXIII, 589 pages 1 illustrations)
490	1		_aSpringer Optimization and Its Applications, _x1931-6828 ; _v137
505	0		_aIntroduction -- Part I Black-Box Optimization -- 1 Nonlinear Optimization -- 2 Smooth Convex Optimization -- 3 Nonsmooth Convex Optimization -- 4 Second-Order Methods -- Part II Structural Optimization -- 5 Polynomial-time Interior-Point Methods -- 6 Primal-Dual Model of Objective Function -- 7 Optimization in Relative Scale -- Bibliographical Comments -- Appendix A. Solving some Auxiliary Optimization Problems -- References -- Index.
520			_aThis book provides a comprehensive, modern introduction to convex optimization, a field that is becoming increasingly important in applied mathematics, economics and finance, engineering, and computer science, notably in data science and machine learning. Written by a leading expert in the field, this book includes recent advances in the algorithmic theory of convex optimization, naturally complementing the existing literature. It contains a unified and rigorous presentation of the acceleration techniques for minimization schemes of first- and second-order. It provides readers with a full treatment of the smoothing technique, which has tremendously extended the abilities of gradient-type methods. Several powerful approaches in structural optimization, including optimization in relative scale and polynomial-time interior-point methods, are also discussed in detail. Researchers in theoretical optimization as well as professionals working on optimization problems will find this book very useful. It presents many successful examples of how to develop very fast specialized minimization algorithms. Based on the author's lectures, it can naturally serve as the basis for introductory and advanced courses in convex optimization for students in engineering, economics, computer science and mathematics.
526			_aCSE _lREF _bps
541			_aRisaam
650		0	_aAlgorithms.
650		0	_aMathematical optimization.
650	1	4	_aOptimization.
650	2	4	_aAlgorithm Analysis and Problem Complexity.
650	2	4	_aAlgorithms.
776	0	8	_iPrint version: _tLectures on convex optimization _z9783319915777 _w(DLC) 2018949149
776	0	8	_iPrinted edition: _z9783319915777
776	0	8	_iPrinted edition: _z9783319915791
830		0	_aSpringer Optimization and Its Applications, _v137
942			_2ddc _cBK
999			_c57679 _d57853