Research map / Optimization / Nonconvex and smooth optimization
Fast Frank–Wolfe Algorithms with Adaptive Bregman Step-Size for Weakly Convex Functions
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Closest papers by meaning
- Frank-Wolfe Beyond 1/t Convergence (NeurIPS 2026 · similarity 0.91)
- Revisiting Frank-Wolfe for Structured Nonconvex Optimization (NeurIPS 2025 · similarity 0.91)
- Revisiting Projection-Free Optimization for Strongly Convex Constraint Sets (AAAI 2019 · similarity 0.91)
- Spectral Frank-Wolfe Algorithm: Strict Complementarity and Linear Convergence (ICML 2020 · similarity 0.90)
- Efficient Quadratic Corrections for Frank-Wolfe Algorithms (NeurIPS 2025 · similarity 0.89)
- Sarah Frank-Wolfe: Methods for Constrained Optimization with Best Rates and Practical Features (ICML 2024 · similarity 0.88)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.