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First-Order Methods in Optimization
Amir Beck
出版SIAM, 2017-10-02
主題Mathematics / OptimizationScience / Physics / Mathematical & ComputationalMathematics / Linear & Nonlinear ProgrammingTechnology & Engineering / Operations ResearchTechnology & Engineering / Imaging SystemsComputers / Data Science / Data Analytics
ISBN16119749929781611974997
URLhttp://books.google.com.hk/books?id=wrk4DwAAQBAJ&hl=&source=gbs_api
EBookSAMPLE
註釋The primary goal of this book is to provide a self-contained, comprehensive study of the main ?rst-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.