000			a
999			_c30874 _d30874
008			220627b xxu||||| |||| 00| 0 eng d
020			_a9781316518984
082			_a005.7 _bWRI
100			_aWright, Stephen J.
245			_aOptimization for data analysis
260			_bCambridge University Press, _c2022 _aCambridge :
300			_ax, 227 p. ; _bill., _c24 cm
365			_b37.99 _cGBP _d100.50
504			_aIncludes bibliographical references and index
520			_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			_aMathematical optimization
650			_aQuantitative research
650			_aBig data
700			_aRecht, Benjamin
942			_2ddc _cBK