Average case performance of heuristics for multi-dimensional assignment problems
Alan Frieze, Gregory Sorkin
TL;DR
This paper investigates the average case performance of heuristics for multi-dimensional assignment problems, introducing new algorithms for specific problem variants and analyzing their efficiency in probabilistic settings.
Contribution
It presents novel algorithms for 3-dimensional assignment problems and analyzes their average case performance in a probabilistic framework.
Findings
Efficient algorithm for 3D planar assignment based on alternating-path trees
Matching-based algorithm for 3D axial assignment
Improved understanding of heuristic performance in probabilistic models
Abstract
We consider multi-dimensional assignment problems in a probabilistic setting. Our main results are: (i) A new efficient algorithm for the 3-dimensional planar problem, based on enumerating and selecting from a set of "alternating-path trees"; (ii) A new efficient matching-based algorithm for the 3-dimensional axial problem.
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Taxonomy
TopicsAdvanced Graph Theory Research · Complexity and Algorithms in Graphs · Optimization and Search Problems