Work

Invariant Graph Propagation in Constraint-Based Local Search

Year: 2025

Type: article

Abstract: In constraint-based local search, an assignment to the search variables is improved upon by an iterative procedure that replaces the current assignment with a similar assignment. The latter is selected by a heuristic that assesses the qualities of a subset of all similar assignments, where the quality of such assignments is determined via a process called invariant graph propagation. Since, typically, many similar assignments are considered in every iteration, invariant graph propagation must be as efficient as possible. Since invariant graph propagation is independent of the selection heuristic, any comparison between different invariant graph propagation styles under different selection heuristics can be misleading. In this paper, we describe and compare both theoretically and empirically the throughput of several invariant graph propagation styles, and give criteria when one style or another is to be used.

Source: Journal of Artificial Intelligence Research

DOI: 10.1613/jair.1.17482

Authors: Frej Knutar Lewander, Pierre Flener, Justin Pearson

Institution: Uppsala University

Language: English

Topic: Constraint Satisfaction and Optimization

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

Funder: Vetenskapsrådet

Awards: G712881263, Data-Independent Analysis of Combinatorial Models