A barrier-smoothed first-order method achieves stationarity rates of tilde O(K to the -2/3) deterministic and tilde O(K to the -2/5) stochastic for linearly constrained bilevel optimization.
International Conference on Machine Learning , pages=
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
math.OC 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Optimistic bilevel optimization with manifold lower-level minimizers is differentiable if the optimistic selection is unique, yielding a pseudoinverse hyper-gradient and a convergent HG-MS algorithm whose rate depends on intrinsic manifold dimension.
citing papers explorer
-
A Barrier-Metric First-Order Method for Linearly Constrained Bilevel Optimization
A barrier-smoothed first-order method achieves stationarity rates of tilde O(K to the -2/3) deterministic and tilde O(K to the -2/5) stochastic for linearly constrained bilevel optimization.
-
Select-then-differentiate: Solving Bilevel Optimization with Manifold Lower-level Solution Sets
Optimistic bilevel optimization with manifold lower-level minimizers is differentiable if the optimistic selection is unique, yielding a pseudoinverse hyper-gradient and a convergent HG-MS algorithm whose rate depends on intrinsic manifold dimension.