Two adaptive kernel selection techniques for Kernelized Diffusion Maps are developed, backed by proofs of Lipschitz dependence on kernel weights, spectral projector continuity under gap conditions, residual control, and exponential consistency of the selector.
SIAM Review53(3), 464–501 (2011)
3 Pith papers cite this work. Polarity classification is still indexing.
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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.
Proposes and tests a t-test based method to limit simulations per iteration in local search for the stochastic parallel machine scheduling and stochastic electric vehicle scheduling problems.
citing papers explorer
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Adaptive Kernel Selection for Kernelized Diffusion Maps
Two adaptive kernel selection techniques for Kernelized Diffusion Maps are developed, backed by proofs of Lipschitz dependence on kernel weights, spectral projector continuity under gap conditions, residual control, and exponential consistency of the selector.
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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.
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Simulation Strategies for an Efficient Local Search to solve Stochastic Scheduling Problems
Proposes and tests a t-test based method to limit simulations per iteration in local search for the stochastic parallel machine scheduling and stochastic electric vehicle scheduling problems.