A unified zeroth-order proximal Newton framework for composite optimization establishes iteration and oracle complexity bounds for epsilon-optimality in nonconvex and strongly convex cases, proves local R-superlinear convergence, and shows BFGS is more compatible with finite-difference estimators.
Adaptive first-and zeroth-order methods for weakly convex stochastic optimization problems
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Introduces a novel search direction enabling sublinear stochastic bilevel regret guarantees for first- and zeroth-order online bilevel optimization algorithms without relying on window smoothing.
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A Unified Zeroth-Order Proximal Newton-Type Framework for Composite Optimization
A unified zeroth-order proximal Newton framework for composite optimization establishes iteration and oracle complexity bounds for epsilon-optimality in nonconvex and strongly convex cases, proves local R-superlinear convergence, and shows BFGS is more compatible with finite-difference estimators.