Complexities of Weak Proximal Oracle Methods for Composite Convex Optimization

arXiv:2609.24423 · math.OC, cs.LG · Submitted 2026-09-21 · Read on arXiv

math.OC, cs.LG

Submitted: 2026-09-21

Updated: 2026-09-21

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Objective Function — 5× in this paper · explained in 2 episodes
• Trace — 5× in this paper · explained in 2 episodes
• Ambient Dimension — 2× in this paper · explained in 1 episode
• Accuracy — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Inverse Problem — 1× in this paper · explained in 2 episodes

Sources

• Projection-Free Non-Smooth Convex Programming
• $k$FW: A Frank-Wolfe style algorithm with stronger subproblem oracles
• First-Order Sparse Convex Optimization: Better Rates with Sparse Updates
• Weak Proximal Newton Oracles for Composite Convex Optimization
• The Complexity of Large-scale Convex Programming under a Linear Optimization Oracle

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