Year: 2019
Type: article
Abstract: Integer Linear Programming (ILP) is among the most successful and general paradigms for solving computationally intractable optimization problems in computer science. ILP is NP-complete, and until recently we have lacked a systematic study of the complexity of ILP through the lens of variable-constraint interactions. This changed drastically in recent years thanks to a series of results that together lay out a detailed complexity landscape for the problem centered around the structure of graphical representations of instances. The aim of this survey is to summarize these recent developments, put them into context and a unified format, and make them more approachable for experts from many diverse backgrounds.
Source: Algorithms
DOI: 10.3390/a12120248
Authors: Robert Ganian, Sebastian Ordyniak
Institutions: TU Wien, University of Sheffield
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $948
Funder: Austrian Science Fund
OpenAlex ID: W2989854141