000			01666 a2200205 4500
020			_a9781611974980
040			_cIIT Kanpur
041			_aeng
082			_a519.6 _bB388f
100			_aBeck, Amir
245			_aFirst-order methods in optimization _cAmir Beck
260			_bSIAM _c2017 _aPhiladelphia
300			_axii, 475p
440			_aMOS-SIAM series on optimization
490			_a / edited by Katya Scheinberg; no.25
520			_aThe primary goal of this book is to provide a self-contained, comprehensive study of the main first-order methods that are frequently used in solving large-scale problems. First-order methods exploit information on values and gradients/subgradients (but not Hessians) of the functions composing the model under consideration. With the increase in the number of applications that can be modeled as large or even huge-scale optimization problems, there has been a revived interest in using simple methods that require low iteration cost as well as low memory storage. The author has gathered, reorganized, and synthesized (in a unified manner) many results that are currently scattered throughout the literature, many of which cannot be typically found in optimization books. First-Order Methods in Optimization offers comprehensive study of first-order methods with the theoretical foundations; provides plentiful examples and illustrations; emphasizes rates of convergence and complexity analysis of the main first-order methods used to solve large-scale problems; and covers both variables and functional decomposition methods.
650			_aMathematical optimization
650			_aFirst-order logic
942			_cBK
999			_c561056 _d561056