000			01997 a2200241 4500
005			20190530122026.0
008			190530b xxu||||| |||| 00| 0 eng d
020			_a9783319915777
040			_cIIT Kanpur
041			_aeng
082			_a519.3 _bN376l2
100			_aNesterov, Yurii
245			_aLectures on convex optimization _cYurii Nesterov
250			_a2nd ed
260			_bSpringer _c2018 _aSwitzerland
300			_axxiii, 589p
440			_aSpringer optimization and its applications
490			_a / edited by Ding-Zhu Du; v.137
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.
650			_aComputers -- Programming -- Algorithms
650			_aAlgorithms &​ data structures
942			_cBK
999			_c560279 _d560279