DCA corresponds to Euler discretization of a Bregman gradient flow, with a damped version providing monotone descent, global linear rates under metric DC-PL, and local exponential convergence near nondegenerate minima.
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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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Continuous-Time Dynamics of the Difference-of-Convex Algorithm
DCA corresponds to Euler discretization of a Bregman gradient flow, with a damped version providing monotone descent, global linear rates under metric DC-PL, and local exponential convergence near nondegenerate minima.
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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.