Introduces HOME-DC smoothing for DC functions, derives an inexact first-order oracle, and proposes convergent inexact descent methods with preliminary numerical support on sparse clustering.
Annals of Operations Research133(1–4), 23–46 (2005)
3 Pith papers cite this work. Polarity classification is still indexing.
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Proposes sBDCA with preconditioning for the LTS estimator, claiming up to 3.25 times faster runtime and up to 90% lower objective values than Fast-LTS on synthetic and real data.
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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Difference-of-Convex Optimization via Inexact Smoothing Descent Methods: Difference of High-Order Moreau Envelopes
Introduces HOME-DC smoothing for DC functions, derives an inexact first-order oracle, and proposes convergent inexact descent methods with preliminary numerical support on sparse clustering.
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Faster than Fast-LTS: Robust Regression and Outlier Detection with DC Programming
Proposes sBDCA with preconditioning for the LTS estimator, claiming up to 3.25 times faster runtime and up to 90% lower objective values than Fast-LTS on synthetic and real data.
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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.