A proximal limited-memory quasi-Newton scheme is developed for nonsmooth nonconvex optimization, with global convergence proven under mild assumptions and rates under the Kurdyka-Lojasiewicz property.
Nonmonotone spectral projected gradient methods on convex sets
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Momentum-augmented projected gradient methods achieve faster convergence with theoretical guarantees for nonconvex constrained optimization.
Proposes two spectral conjugate gradient projection methods for monotone nonlinear equations, proving global convergence under monotonicity alone for the first variant without Lipschitz continuity.
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Proximal Limited-Memory Quasi-Newton Methods for Nonsmooth Nonconvex Optimization
A proximal limited-memory quasi-Newton scheme is developed for nonsmooth nonconvex optimization, with global convergence proven under mild assumptions and rates under the Kurdyka-Lojasiewicz property.
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Projected Gradient Methods with Momentum
Momentum-augmented projected gradient methods achieve faster convergence with theoretical guarantees for nonconvex constrained optimization.
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Spectral conjugate gradient projection methods for large-scale monotone equations without Lipschitz continuity
Proposes two spectral conjugate gradient projection methods for monotone nonlinear equations, proving global convergence under monotonicity alone for the first variant without Lipschitz continuity.