Cardinality-constrained structured data-fitting problems
A memory-efficient solution framework is proposed for the cardinality-constrained structured data-fitting problem. Dual-based atom-identification rules reveal the structure of the optimal primal solution from near-optimal dual solutions, which allows for a simple and computationally efficient algori...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Université de Montpellier
2024-05-01
|
| Series: | Open Journal of Mathematical Optimization |
| Subjects: | |
| Online Access: | https://ojmo.centre-mersenne.org/articles/10.5802/ojmo.27/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | A memory-efficient solution framework is proposed for the cardinality-constrained structured data-fitting problem. Dual-based atom-identification rules reveal the structure of the optimal primal solution from near-optimal dual solutions, which allows for a simple and computationally efficient algorithm that translates any feasible dual solution into a primal solution satisfying the cardinality constraint. Rigorous guarantees bound the quality of a near-optimal primal solution given any dual-based method that generates dual iterates converging to an optimal dual solution. Numerical experiments on real-world datasets support the analysis and demonstrate the efficiency of the proposed approach. |
|---|---|
| ISSN: | 2777-5860 |