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.
Advances in Neural Information Processing Systems , volume=
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SPACO is a new single-loop stochastic algorithm for stochastic nonconvex-concave minimax problems with nonlinear convex coupled constraints that uses penalty smoothing and provides non-asymptotic complexity bounds plus stationarity analysis.
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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.
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A Single-Loop Stochastic Gradient Algorithm for Minimax Optimization with Nonlinear Coupled Constraints
SPACO is a new single-loop stochastic algorithm for stochastic nonconvex-concave minimax problems with nonlinear convex coupled constraints that uses penalty smoothing and provides non-asymptotic complexity bounds plus stationarity analysis.