Proposes CLARSTA, a random subspace trust-region algorithm for convex-constrained DFO with new projection-based model class, geometry measure, and concentration-of-measure subspace sampling, proving almost-sure convergence and complexity while demonstrating performance on problems up to dimension 10
Randomized subspace derivative-free opti- mization with quadratic models and second-order convergence
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RZCW-PSS augments classical coordinate and swap moves with reservoir-based subspace searches and proves that accumulation points reach Beck-Hallak zero-coordinatewise stationarity almost surely under stated assumptions.
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CLARSTA: A random subspace trust-region algorithm for convex-constrained derivative-free optimization
Proposes CLARSTA, a random subspace trust-region algorithm for convex-constrained DFO with new projection-based model class, geometry measure, and concentration-of-measure subspace sampling, proving almost-sure convergence and complexity while demonstrating performance on problems up to dimension 10
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Reservoir Zero-Coordinatewise Projected Subspace Search for Minimization Over Sparse Symmetric Sets in Machine Learning
RZCW-PSS augments classical coordinate and swap moves with reservoir-based subspace searches and proves that accumulation points reach Beck-Hallak zero-coordinatewise stationarity almost surely under stated assumptions.