We lifted 3 functions out of this paper's own repositories and ran 2 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| Liuyuan999/Penalty_Based_Lagrangian_Bilevel | canonical | 2 of 3 |
| Function | Status | Where it lives |
|---|---|---|
| lag_g | Ran | Liuyuan999/Penalty_Based_Lagrangian_Bilevel/hyperparam_opt/algorithms/blocc.py code served (permissive licence) · get_code("5a9b19d0f6995579") |
| minmax_opt | Ran | Liuyuan999/Penalty_Based_Lagrangian_Bilevel/hyperparam_opt/algorithms/blocc.py code served (permissive licence) · get_code("ec36d4f9516b426e") |
| lag_F | Not yet run | Liuyuan999/Penalty_Based_Lagrangian_Bilevel/hyperparam_opt/algorithms/blocc.py code served (permissive licence) · get_code("48eb8f68d7d16c34") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Interest in bilevel optimization has grown in recent years, partially due to its applications to tackle challenging machine-learning problems. Several exciting recent works have been centered around developing efficient gradient-based algorithms that can solve bilevel optimization problems with provable guarantees. However, the existing literature mainly focuses on bilevel problems either without constraints, or featuring only simple constraints that do not couple variables across the upper and lower levels, excluding a range of complex applications. Our paper studies this challenging but less explored scenario and develops a (fully) first-order algorithm, which we term BLOCC, to tackle BiLevel Optimization problems with Coupled Constraints. We establish rigorous convergence theory for the proposed algorithm and demonstrate its effectiveness on two well-known real-world applications - hyperparameter selection in support vector machine (SVM) and infrastructure planning in transportation networks using the real data from the city of Seville.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2406.10148")
get_code_for_paper("2406.10148")
have("2406.10148")
Connect an agent — have() is free.