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571.00 ₪
FIRST-ORDER METHODS IN OPTIMIZATION
571.00 ₪
ISBN13
9781611974980
יצא לאור ב
New York
זמן אספקה
21 ימי עסקים
עמודים
484
פורמט
Paperback / softback
תאריך יציאה לאור
1 במרץ 2017
שם סדרה
MOS-SIAM Series on Optimization
Provides a self-contained, comprehensive study of the main first-order methods that are frequently used in solving large-scale problems. 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.
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.
עמודים | 484 |
---|---|
פורמט | Paperback / softback |
ISBN10 | 1611974984 |
יצא לאור ב | New York |
תאריך יציאה לאור | 1 במרץ 2017 |
תוכן עניינים | Preface; Chapter 1: Vector Spaces; Chapter 2: Extended Real-Value Functions; Chapter 3: Subgradients; Chapter 4: Conjugate Functions; Chapter 5: Smoothness and Strong Convexity; Chapter 6: The Proximal Operator; Chapter 7: Spectral Functions; Chapter 8: Primal and Dual Projected Subgradient Methods; Chapter 9: Mirror Descent; Chapter 10: The Proximal Gradient Method; Chapter 11: The Block Proximal Gradient Method; Chapter 12: Dual-Based Proximal Gradient Methods; Chapter 13: The Generalized Conditional Gradient Method; Chapter 14: Alternating Minimization; Chapter 15: ADMM; Appendix A: Strong Duality and Optimality Conditions; Appendix B: Tables; Appendix C: Symbols and Notation; Appendix D: Bibliographic Notes; Bibliography; Index. |
זמן אספקה | 21 ימי עסקים |
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