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 single-loop penalty-based stochastic algorithm for minimax optimization with nonlinear coupled constraints, achieving non-asymptotic complexity bounds and asymptotic convergence to enhanced KKT points.
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