ADMM for 0/1 D-optimality and maximum-entropy sampling relaxations
Abstract
Abstract The 0/1 D-optimality problem and the maximum-entropy sampling problem are two well-known NP-hard discrete maximization problems in experimental design. Algorithms for exact optimization (of moderate-sized instances) are based on branch-and-bound. The best upper-bounding methods are based on convex relaxation. We present ADMM (Alternating Direction Method of Multipliers) algorithms for solving these relaxations and experimentally demonstrate their practical value.
Fields of Study & Subjects
Optical Network Technologies
Photonic and Optical Devices
Semiconductor Lasers and Optical Devices
Publication details
- Journal / source
- Computational Optimization and Applications
- Publisher
- Springer Science and Business Media LLC
- Publication date
- 11 August 2026
- Language
- EN