We study the sparsity of the solutions to systems of linear Diophantine equations with and without non-negativity constraints. The sparsity of a solution vector is the number of its nonzero entries, which is referred to as the
This paper studies several solution paths of sparse quadratic minimization problems as a function of the weighing parameter of the bi-objective of estimation loss versus solution sparsity. Three such paths are considered: the “
- Award ID(s):
- 2006762
- NSF-PAR ID:
- 10411443
- Publisher / Repository:
- Springer Science + Business Media
- Date Published:
- Journal Name:
- Mathematical Programming
- Volume:
- 204
- Issue:
- 1-2
- ISSN:
- 0025-5610
- Format(s):
- Medium: X Size: p. 517-566
- Size(s):
- ["p. 517-566"]
- Sponsoring Org:
- National Science Foundation
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