Towards auction algorithms for large dense assignment problems

Authors:
Libor Buš;Pavel Tvrdík
Affiliations:
Department of Computer Science and Engineering, Czech Technical University, Prague, Czech Republic;Department of Computer Science and Engineering, Czech Technical University, Prague, Czech Republic
Venue:
Computational Optimization and Applications
Year:
2009

Citing 7
Cited 1

A shortest augmenting path algorithm for dense and sparse linear assignment problems

Computing
A parallel shortest augmenting path algorithm for the assignment problem

Journal of the ACM (JACM)
Linear network optimization: algorithms and codes

Linear network optimization: algorithms and codes
Using interior-point methods for fast parallel algorithms for bipartite matching and related problems

SIAM Journal on Computing
An efficient cost scaling algorithm for the assignment problem

Mathematical Programming: Series A and B
On Algorithms For Permuting Large Entries to the Diagonal of a Sparse Matrix

SIAM Journal on Matrix Analysis and Applications
Shape Matching and Object Recognition Using Shape Contexts

IEEE Transactions on Pattern Analysis and Machine Intelligence

An auction-based weighted matching implementation on massively parallel architectures

Parallel Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we focus on the problem of solving large-scale instances of the linear sum assignment problem by auction algorithms. We introduce a modified auction algorithm, called look-back auction algorithm, which extends the forward auction algorithm by the ability of reusing information from previous bids. We show that it is able to reuse information from the previous bids with high efficiency for all tested types of input instances. We discuss then the design and implementation of a suite of sequential and distributed memory auction algorithms on a Linux cluster with the evaluation on several types of input instances of the linear sum assignment problem. Our results show that the look-back auction algorithm solves sequentially nearly all types of dense instances faster than other evaluated algorithms and it is more stable than the forward-reverse auction algorithm for sparse instances. Our distributed memory auction algorithms are fully memory scalable.