Prox-ITEM achieves the minimax-optimal distance-to-solution rate among span-based first-order methods for smooth strongly convex composite problems, with Prox-TMM as its stationary limit matching TMM rates.
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Presents a stochastic gradient algorithm for non-separable optimization with local convergence guarantees under smoothness assumptions.
Parameter-free first-order methods attain optimal oracle complexity O(ε^{-2/(1+3ρ)}) for convex function-constrained optimization under Hölder smoothness by combining modified Polyak steps, Nesterov momentum, and APL level-set methods.
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An optimal first-order method for smooth and strongly convex composite optimization and its stationary limit
Prox-ITEM achieves the minimax-optimal distance-to-solution rate among span-based first-order methods for smooth strongly convex composite problems, with Prox-TMM as its stationary limit matching TMM rates.
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A stochastic gradient algorithm for non-separable optimization with convergence guarantee
Presents a stochastic gradient algorithm for non-separable optimization with local convergence guarantees under smoothness assumptions.
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Uniformly Optimal and Parameter-free First-order Methods for Convex and Function-constrained Optimization
Parameter-free first-order methods attain optimal oracle complexity O(ε^{-2/(1+3ρ)}) for convex function-constrained optimization under Hölder smoothness by combining modified Polyak steps, Nesterov momentum, and APL level-set methods.