A bidirectional greedy heuristic for the subspace selection problem

Authors:
Dag Haugland
Affiliations:
Department of Informatics, University of Bergen, Bergen, Norway
Venue:
SLS'07 Proceedings of the 2007 international conference on Engineering stochastic local search algorithms: designing, implementing and analyzing effective heuristics
Year:
2007

Citing 6
Cited 0

Sparse Approximate Solutions to Linear Systems

SIAM Journal on Computing
Matrix computations (3rd ed.)

Matrix computations (3rd ed.)
Variable neighborhood search

Computers and Operations Research
Atomic Decomposition by Basis Pursuit

SIAM Journal on Scientific Computing
On the Optimality of the Backward Greedy Algorithm for the Subset Selection Problem

SIAM Journal on Matrix Analysis and Applications
Computers and Intractability: A Guide to the Theory of NP-Completeness

Computers and Intractability: A Guide to the Theory of NP-Completeness

Quantified Score

Hi-index	0.00

Visualization

Abstract

The Subspace Selection Problem (SSP) amounts to selecting t out of n given vectors of dimension m, such that they span a subspace in which a given target b ∈ Rm has a closest possible approximation. This model has numerous applications in e.g. signal compression and statistical regression. It is well known that the problem is NP-hard. Based on elements from a forward and a backward greedy method, we develop a randomized search heuristic, which in some sense resembles variable neighborhood search, for SSP. Through numerical experiments we demonstrate that this approach has good promise, as it produces good results at modest computational cost.