Approximating and Relaxing Optimization Problems

Gonzalo E. Constante-Flores and Antonio J. Conejo
Additional contact information
Gonzalo E. Constante-Flores: Purdue University
Antonio J. Conejo: The Ohio State University

Chapter Chapter 3 in Optimization via Relaxation and Decomposition, 2025, pp 45-72 from Springer

Abstract: Abstract This chapter describes how to simplify optimization problems by approximation and relaxation. Approximations include linearizations, convexifications, and problem-dependent simplifications. Relaxations are approximations that enlarge, not shrink, the feasibility region. We also explain how to generate an upper bound and a lower bound of the optimal value of the objective function of an optimization problem. Since approximations and relaxations are very much problem-dependent, we use a complex nonlinear and nonconvex but specific optimization problem to illustrate approximations via linearization, convexification, and others.

Keywords: Approximating optimization problems; Linearization; Convexification (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

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

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:isochp:978-3-031-87405-5_3

Ordering information: This item can be ordered from
http://www.springer.com/9783031874055

DOI: 10.1007/978-3-031-87405-5_3

Access Statistics for this chapter

More chapters in International Series in Operations Research & Management Science from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().