First-Order Methods for Linearly Constrained Bilevel Optimization
–Neural Information Processing Systems
Algorithms for bilevel optimization often encounter Hessian computations, which are prohibitive in high dimensions. While recent works offer first-order methods for unconstrained bilevel problems, the constrained setting remains relatively underexplored. We present first-order linearly constrained optimization methods with finite-time hypergradient stationarity guarantees.
Neural Information Processing Systems
May-25-2025, 21:48:20 GMT
- Country:
- North America > United States > Massachusetts (0.14)
- Genre:
- Research Report > Experimental Study (1.00)
- Industry:
- Health & Medicine (0.67)
- Technology: