Research map / Optimization / Nonconvex and smooth optimization
Faster Projection-free Convex Optimization over the Spectrahedron
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.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use rank-one-regularized updates with randomized component selection instead of standard conditional-gradient updates | Conditional gradient optimization over the spectrahedron · Spectrahedron optimization | expected approximation error: O(\beta/t) -> O\left(\min\{\frac{\beta{}}{t} ,\left({\frac{\beta\sqrt{\textrm{rank}(\X^*)}}{\alpha^{1/4}t}}\right)^{4/3} | Helped |
| Use the proposed ROR-CG variant with greedy component selection and line search | ROR-CG · MovieLens100k | objective versus iterations | Helped |
| Use the proposed ROR-CG variant with greedy component selection and line search | ROR-CG · MovieLens1M | objective versus iterations | Helped |
Closest papers by meaning
- Spectral Frank-Wolfe Algorithm: Strict Complementarity and Linear Convergence (ICML 2020 · similarity 0.90)
- Stochastic Frank-Wolfe for Composite Convex Minimization (NeurIPS 2019 · similarity 0.87)
- Revisiting Frank-Wolfe for Structured Nonconvex Optimization (NeurIPS 2025 · similarity 0.87)
- Spectral Preconditioning for Gradient Methods on Graded Non-convex Functions (ICML 2024 · similarity 0.86)
- Single-Loop Stochastic Algorithms for Difference of Max-Structured Weakly Convex Functions (NeurIPS 2024 · similarity 0.85)
- Det-CGD: Compressed Gradient Descent with Matrix Stepsizes for Non-Convex Optimization (ICLR 2024 · similarity 0.85)
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.