EconPapers    
Economics at your fingertips  
 

Inapproximability

Ding-Zhu Du (), Ker-I Ko () and Xiaodong Hu ()
Additional contact information
Ding-Zhu Du: University of Texas at Dallas
Ker-I Ko: State University of New York at Stony Brook
Xiaodong Hu: Academy of Mathematics and Systems Science Chinese Academy of Sciences

Chapter 10 in Design and Analysis of Approximation Algorithms, 2012, pp 371-406 from Springer

Abstract: Abstract In this chapter, we turn our attention to a different issue about approximation algorithms. We study how to prove inapproximability results for some NP-hard optimization problems. We are not looking here for a lower bound for the performance ratio of a specific approximation algorithm, but, instead, we try to find a lower bound for the performance ratio of any approximation algorithmfor a given problem.Most results in this study are based on advanced developments in computational complexity theory, which is beyond the scope of this book. Therefore, we limit ourselves to fundamental concepts and results, often with proofs omitted, which are sufficient to establish the inapproximability of many combinatorial optimization problems.

Date: 2012
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:spochp:978-1-4614-1701-9_10

Ordering information: This item can be ordered from
http://www.springer.com/9781461417019

DOI: 10.1007/978-1-4614-1701-9_10

Access Statistics for this chapter

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

 
Page updated 2025-04-01
Handle: RePEc:spr:spochp:978-1-4614-1701-9_10