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Approximate Steepest Coordinate Descent
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use ASCD instead of UCD for coordinate selection | Coordinate descent · Synthetic least squares and Lasso; RCV1-binary training | objective convergence | Helped |
| Initialize ASCD with arbitrary gradient estimates instead of the true gradient (ablation) | ASCD · Synthetic data and RCV1-binary training | objective convergence | No clear effect |
| Use the crude g4 gradient oracle instead of more precise, more expensive oracles (ablation) | ASCD · RCV1-binary training | objective convergence | No clear effect |
| Use heuristic a-ASCD active-set selection instead of the standard ASCD active set (ablation) | ASCD · Synthetic data and RCV1-binary training | objective convergence | No clear effect |
Closest papers by meaning
- Even Faster Accelerated Coordinate Descent Using Non-Uniform Sampling (ICML 2016 · similarity 0.89)
- When Cyclic Coordinate Descent Outperforms Randomized Coordinate Descent (NeurIPS 2017 · similarity 0.89)
- Accelerated Stochastic Greedy Coordinate Descent by Soft Thresholding Projection onto Simplex (NeurIPS 2017 · similarity 0.88)
- Accelerating Greedy Coordinate Descent Methods (ICML 2018 · similarity 0.88)
- Stochastic Spectral and Conjugate Descent Methods (NeurIPS 2018 · similarity 0.87)
- Provable Benefit of Random Permutations over Uniform Sampling in Stochastic Coordinate Descent (ICML 2025 · similarity 0.86)
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