Convex Optimization with Computational Errors

Alexander J. Zaslavski ()
Additional contact information
Alexander J. Zaslavski: Israel Institute of Technology

in Springer Optimization and Its Applications from Springer, currently edited by Pardalos, Panos, Thai, My T. and Du, Ding-Zhu

Date: 2020
ISBN: 978-3-030-37822-6
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Chapters in this book:

Ch Chapter 1 Introduction

Alexander Zaslavski

Ch Chapter 10 Minimization of Quasiconvex Functions

Alexander Zaslavski

Ch Chapter 11 Minimization of Sharp Weakly Convex Functions

Alexander Zaslavski

Ch Chapter 12 A Projected Subgradient Method for Nonsmooth Problems

Alexander Zaslavski

Ch Chapter 2 Subgradient Projection Algorithm

Alexander Zaslavski

Ch Chapter 3 The Mirror Descent Algorithm

Alexander Zaslavski

Ch Chapter 4 Gradient Algorithm with a Smooth Objective Function

Alexander Zaslavski

Ch Chapter 5 An Extension of the Gradient Algorithm

Alexander Zaslavski

Ch Chapter 6 Continuous Subgradient Method

Alexander Zaslavski

Ch Chapter 7 An Optimization Problems with a Composite Objective Function

Alexander Zaslavski

Ch Chapter 8 A Zero-Sum Game with Two Players

Alexander Zaslavski

Ch Chapter 9 PDA-Based Method for Convex Optimization

Alexander Zaslavski

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:spopap:978-3-030-37822-6

Ordering information: This item can be ordered from
http://www.springer.com/9783030378226

DOI: 10.1007/978-3-030-37822-6

Access Statistics for this book

More books in Springer Optimization and Its Applications from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().