Abstract:
We propose a global optimization approach to solve ℓ0-norm penalized least-squares problems, using a dedicated branch-and-bound methodology. A specific tree search strate...Show MoreMetadata
Abstract:
We propose a global optimization approach to solve ℓ0-norm penalized least-squares problems, using a dedicated branch-and-bound methodology. A specific tree search strategy is built, with branching rules inspired from greedy exploration techniques. We show that the subproblem involved at each node can be evaluated via ℓ1-norm-based optimization problems with box constraints, for which an active-set algorithm is built. Our method is able to solve exactly moderate-size, yet difficult, sparse approximation problems, without resorting to mixed-integer programming (MIP) optimization. In particular, it outperforms the generic MIP solver CPLEX.
Published in: ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Date of Conference: 04-08 May 2020
Date Added to IEEE Xplore: 09 April 2020
ISBN Information: