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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A solver-agnostic condensing reformulation for linear-quadratic optimization with polyhedral and geometric constraints that preserves augmented-Lagrangian convergence while improving computational speed.
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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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A condensing approach for linear-quadratic optimization with geometric constraints
A solver-agnostic condensing reformulation for linear-quadratic optimization with polyhedral and geometric constraints that preserves augmented-Lagrangian convergence while improving computational speed.