Quadratization of Symmetric Pseudo-Boolean Functions
Martin Anthony, Endre Boros, Yves Crama, Aritanan Gruber

TL;DR
This paper investigates the minimal number of auxiliary variables needed to quadratize symmetric pseudo-Boolean functions, enabling their minimization through quadratic optimization techniques.
Contribution
It provides a precise characterization of the auxiliary variable count required for quadratizations of symmetric pseudo-Boolean functions.
Findings
Determines the exact number of auxiliary variables needed for symmetric functions.
Links quadratization complexity to the symmetry property of functions.
Facilitates more efficient minimization of symmetric pseudo-Boolean functions.
Abstract
A pseudo-Boolean function is a real-valued function of binary variables; that is, a mapping from to . For a pseudo-Boolean function on , we say that is a quadratization of if is a quadratic polynomial depending on and on auxiliary binary variables such that for all . By means of quadratizations, minimization of is reduced to minimization (over its extended set of variables) of the quadratic function . This is of some practical interest because minimization of quadratic functions has been thoroughly studied for the last few decades, and much progress has been made in solving such problems exactly or heuristically. A related paper \cite{ABCG} initiated a systematic study of the minimum number…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsCoding theory and cryptography · Machine Learning and Algorithms · Algorithms and Data Compression
