Greedy minimization of l1-norm with high empirical success
2015 (English)Conference paper, Poster (Refereed)
We develop a greedy algorithm for the basis-pursuit problem. Thealgorithm is empirically found to provide the same solution as convex optimization based solvers. The method uses only a subset ofthe optimization variables in each iteration and iterates until an optimality condition is satisfied. In simulations, the algorithm converges faster than standard methods when the number of measurements is small and the number of variables large.
Place, publisher, year, edition, pages
Convex optimization, basis-pursuit, greedy algorithms.
Research subject Electrical Engineering
IdentifiersURN: urn:nbn:se:kth:diva-163700ScopusID: 2-s2.0-84946038019OAI: oai:DiVA.org:kth-163700DiVA: diva2:801952
40th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2015
Presented at the 40th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2015 in Brisbane, Australia. QC 201504152015-04-102015-04-102015-04-15Bibliographically approved