A new unified method for directional stationary points of non-smooth DC programs with both components possibly non-smooth, plus deterministic and randomized convergence results.
Mathe- matical Programming169(1), 5–68 (2018)
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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.
RA-DCA applies randomized vertex screening inside DCA iterations for max-structured DC programs and proves that safeguarded accumulation points are directionally stationary with probability one under regularity, active-set consistency, and random-embedding assumptions.
citing papers explorer
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Finding directional stationary points of DC programs
A new unified method for directional stationary points of non-smooth DC programs with both components possibly non-smooth, plus deterministic and randomized convergence results.
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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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RA-DCA: A Randomized Active-Set DCA for Directional Stationarity in Max-Structured DC Programs
RA-DCA applies randomized vertex screening inside DCA iterations for max-structured DC programs and proves that safeguarded accumulation points are directionally stationary with probability one under regularity, active-set consistency, and random-embedding assumptions.