Research map / Optimization / Nonconvex and smooth optimization
Stochastic Optimization for Non-convex Inf-Projection Problems
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use the inf-projection formulation with St-SPG instead of the min-max formulation with BMD | Variance regularization with logistic loss · a9a, RCV1, covtype, URL | training and testing error versus CPU time | Helped |
| Use the inf-projection formulation with St-SPG instead of the min-max formulation with BMD-eff | Variance regularization with logistic loss · a9a, RCV1, covtype, URL | training and testing error versus CPU time | Helped |
| Use the inf-projection formulation with MSPG instead of the min-max formulation with PGSMD | Variance regularization with truncated logistic loss · a9a, RCV1, covtype, URL | training and testing error versus CPU time | Helped |
| Use the inf-projection formulation with MSPG instead of the min-max formulation with PGSMD-eff | Variance regularization with truncated logistic loss · a9a, RCV1, covtype, URL | training and testing error versus CPU time | Helped |
| Use the inf-projection formulation rather than the min-max formulation | Variance-based regularization · a9a, RCV1, covtype, URL | training and testing error | Mixed |
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