國立虎尾科技大學 |

Optimization for data analysis /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Optimization for data analysis // Stephen J. Wright, Benjamin Recht.
Author:	Wright, Stephen J.,
other author:	Recht, Benjamin,
Description:	1 online resource (x, 227 pages) :digital, PDF file(s). :
Notes:	Title from publisher's bibliographic system (viewed on 01 Apr 2022).
Subject:	Big data. -
Online resource:	https://doi.org/10.1017/9781009004282
ISBN:	9781009004282 (ebook)

Optimization for data analysis /
Wright, Stephen J.,1960-

Optimization for data analysis /Stephen J. Wright, Benjamin Recht. - 1 online resource (x, 227 pages) :digital, PDF file(s).

Title from publisher's bibliographic system (viewed on 01 Apr 2022).

Optimization 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.

ISBN: 9781009004282 (ebook)Subjects--Topical Terms:

981821
Big data.

LC Class. No.: QA76.9.B45 / W75 2022

Dewey Class. No.: 005.7

Optimization for data analysis /
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520 $a Optimization 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.
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