000			01734nam a22002057a 4500
005			20250507155123.0
008			250507b2022 |||||||| |||| 00| 0 eng d
020			_a9781316518984
082			_a005.7 WRI-S
100			_aWright, Stephen J.
245			_aOptimization for data analysis / _cStephen J. Wright and Benjamin Recht
260			_aIndia _bCambridge University Press _c2022
300			_a227p.
500			_aOptimization techniques are at the core of data science, including data analysis and machine learning. An understanding of basic optimization techniques and their fundamental properties provides important grounding for students, researchers, and practitioners in these areas. This text covers the fundamentals of optimization algorithms in a compact, self-contained way, focusing on the techniques most relevant to data science. An introductory chapter demonstrates that many standard problems in data science can be formulated as optimization problems. Next, many fundamental methods in optimization are described and analyzed, including: gradient and accelerated gradient methods for unconstrained optimization of smooth (especially convex) functions; the stochastic gradient method, a workhorse algorithm in machine learning; the coordinate descent approach; several key algorithms for constrained optimization problems; algorithms for minimizing nonsmooth functions arising in data science; foundations of the analysis of nonsmooth functions and optimization duality; and the back-propagation approach, relevant to neural networks.
650			_aBig data
650			_aMathematical Optimization
650			_aQuantitative Research
650			_aArtificial Intelligence
650			_aMathematics - General
999			_c93486 _d93486