Benchmarking
Cross-source consensus on Benchmarking from 1 sources and 5 claims.
1 sources · 5 claims
Uses
Preparation
Risks & contraindications
Evidence quality
Highlighted claims
- The benchmark-suite experiments include Random QP, Portfolio Optimization, Lasso, SVM, and Control QP families. — Learning Over-Relaxation Policies for ADMM with Convergence Guarantees
- The learned policies are trained separately for each problem family rather than as a single universal policy. — Learning Over-Relaxation Policies for ADMM with Convergence Guarantees
- The empirical evaluation focuses on convex quadratic programs in OSQP form and one MPC benchmark, leaving broader structured convex problems for future work. — Learning Over-Relaxation Policies for ADMM with Convergence Guarantees
- Reported wall-clock timings depend on the implementation and hardware used for benchmarking. — Learning Over-Relaxation Policies for ADMM with Convergence Guarantees
- Training and testing use separate problem sizes, with larger sizes used for testing. — Learning Over-Relaxation Policies for ADMM with Convergence Guarantees