Introduces inexactly smooth convex functions with interpolation theorems for PEP-based analysis and derives new optimal gradient methods for Hölder smooth convex minimization.
The exact information-based complexity of smooth convex minimization
3 Pith papers cite this work, alongside 4 external citations. Polarity classification is still indexing.
3
Pith papers citing it
4
external citations · OpenAlex
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math.OC 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
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.
Hamiltonian dynamics yield deterministic accelerated convergence for smooth convex optimization by contracting averaged flow trajectories, with discrete implementations matching optimal first-order complexity.
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Inexactly Smooth Performance Estimation and New Optimized Gradient Methods
Introduces inexactly smooth convex functions with interpolation theorems for PEP-based analysis and derives new optimal gradient methods for Hölder smooth convex minimization.
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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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Accelerated Convex Optimization via Hamiltonian Dynamics with Deterministic Integration Time
Hamiltonian dynamics yield deterministic accelerated convergence for smooth convex optimization by contracting averaged flow trajectories, with discrete implementations matching optimal first-order complexity.