Research map / Optimization / Nonconvex and smooth optimization
Frank-Wolfe with Subsampling Oracle
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use a subsampled linear minimization oracle (RFW) instead of the full oracle | Frank-Wolfe · Synthetic Lasso | computed gradient coefficients to reach a target precision | Helped |
| Use a subsampled linear minimization oracle (RFW) instead of the full oracle | Frank-Wolfe · Synthetic Lasso | iterations to converge | Hurt |
| Use a subsampled linear minimization oracle (RAFW) instead of the full oracle | Away-step Frank-Wolfe · Synthetic Lasso | iterations to converge | Helped |
| Use a subsampled linear minimization oracle (RAFW) instead of the full oracle | Away-step Frank-Wolfe · Synthetic Lasso | computed gradient coefficients to reach a target precision | Helped |
| Use a subsampled linear minimization oracle (RFW) instead of the full oracle | Frank-Wolfe · Latent group lasso least-squares regression | FW dual gap versus wall-clock time | Helped |
| Use a subsampled linear minimization oracle (RFW) instead of the full oracle | Frank-Wolfe · Latent group lasso least-squares regression | FW dual gap versus computed gradient coefficients | Helped |
Closest papers by meaning
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- Efficient Quadratic Corrections for Frank-Wolfe Algorithms (NeurIPS 2025 · similarity 0.88)
- Revisiting Frank-Wolfe for Structured Nonconvex Optimization (NeurIPS 2025 · similarity 0.87)
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