000			01509 a2200217 4500
003			OSt
005			20250626100544.0
008			250626b |||||||| |||| 00| 0 eng d
020			_a9781108741774
082			_a515.882 _bV823a
100			_aVishnoi, Nisheeth K.
245			_aAlgorithms for convex optimization _cNisheeth K. Vishnoi
260			_bCambridge University Press _c2021 _aCambridge
300			_axvi, 323p
520			_aIn the last few years, algorithms for convex optimization have revolutionized algorithm design, both for discrete and continuous optimization problems. For problems like maximum flow, maximum matching, and submodular function minimization, the fastest algorithms involve essential methods such as gradient descent, mirror descent, interior point methods, and ellipsoid methods. The goal of this self-contained book is to enable researchers and professionals in computer science, data science, and machine learning to gain an in-depth understanding of these algorithms. The text emphasizes how to derive key algorithms for convex optimization from first principles and how to establish precise running time bounds. This modern text explains the success of these algorithms in problems of discrete optimization, as well as how these methods have significantly pushed the state of the art of convex optimization itself
650			_aMathematical optimization
650			_aConvex functions
650			_aConvex programming
700			_aVishnoi, Nisheeth K.
942			_cBK
999			_c567535 _d567535