Research map / Optimization / Nonconvex and smooth optimization
Stochastic Subspace Cubic Newton Method
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use SSCN instead of uniform-sampling coordinate descent | Logistic regression on LIBSVM datasets · LIBSVM | convergence performance | Helped |
| Use SSCN instead of importance-sampling coordinate descent | Logistic regression on LIBSVM datasets · LIBSVM | convergence performance | Mixed |
| Use SSCN instead of accelerated importance-sampling coordinate descent | Logistic regression on LIBSVM datasets · LIBSVM | convergence performance | Mixed |
| Use SSCN instead of coordinate descent | Log-Sum-Exp minimization · Log-Sum-Exp, d = 500 and 1000 | iteration rate | Helped |
| Use a medium sketch size (ablation) | SSCN on Log-Sum-Exp · Log-Sum-Exp, d = 500 and 1000 | computational time | Helped |
Closest papers by meaning
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- Skip the Hessian, Keep the Rates: Globalized Semismooth Newton with Lazy Hessian Updates (NeurIPS 2026 · similarity 0.87)
- Stochastic Newton Proximal Extragradient Method (NeurIPS 2024 · similarity 0.86)
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