Research map / Optimization / Nonconvex and smooth optimization
Coordinate Descent Methods for Fractional Minimization
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Parametric Coordinate Descent (PCD) instead of DPA, PGSA, QTPA | sparse recovery · 16 dataset/size configurations (e.g., e2006-1000-1024, TDT2-2048-1000) | objective value (lower is better) | Helped |
| Fractional Coordinate Descent (FCD) instead of PGSA / power method | l_p norm eigenvalue problem (p=4, ICA) · 16 dataset/size configurations (e.g., news20-1000-1024, sector-2048-1000) | objective value (lower is better) | Helped |
| Fractional Coordinate Descent vs Parametric Coordinate Descent convergence rate | convex-convex FMPs theory · theoretical rate comparison | convergence rate factor | Helped |
| Proximal term theta (sufficiently large, varpi<=1) in subproblem | CD methods for convex-convex FMPs · theory | convergence guarantee | No clear effect |
Closest papers by meaning
- Coordinate Descent Methods for DC Minimization: Optimality Conditions and Global Convergence (AAAI 2023 · similarity 0.91)
- ADMM for Structured Fractional Minimization (ICLR 2025 · similarity 0.90)
- When Cyclic Coordinate Descent Outperforms Randomized Coordinate Descent (NeurIPS 2017 · similarity 0.87)
- Smooth Primal-Dual Coordinate Descent Algorithms for Nonsmooth Convex Optimization (NeurIPS 2017 · similarity 0.86)
- Random extrapolation for primal-dual coordinate descent (ICML 2020 · similarity 0.86)
- Efficient Continual Finite-Sum Minimization (ICLR 2024 · similarity 0.85)
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