| language: en | |
| license: mit | |
| tags: | |
| - operations-research | |
| - decomposition | |
| - method-selection | |
| - enterprise | |
| - aria-ai | |
| library_name: decompbench | |
| # DecompBench Method Selector | |
| Feature-calibrated method selector for large-scale optimization decomposition. | |
| ## Intended Use | |
| Recommends a decomposition strategy (Benders, Dantzig-Wolfe, Column Generation, Lagrangian, Progressive Hedging, ADMM) from instance structure features. | |
| ## Input Features | |
| - `n_master_variables`, `n_sub_variables`, `n_coupling_constraints` | |
| - `n_scenarios`, `block_angularity`, `constraint_density`, `decomposability_score` | |
| ## Artifacts | |
| - `selector_weights.json` — scoring weights | |
| - `config.json` — model metadata | |
| ## Limitations | |
| Calibrated on synthetic instances. Validate on production models before deployment. | |
| ## Space | |
| https://huggingface.co/spaces/alirezaaminzadeh/large-scale-optimization-decomposition-lab | |