Research map / Learning theory and algorithms / Submodular and game algorithms
Decomposable Submodular Function Minimization: Discrete and Continuous
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use level-1 IBFS instead of coordinate descent | DSFM on image-segmentation instances with small cliques · Small cliques | running time | Mixed |
| Use level-1 IBFS instead of gradient methods | DSFM with larger cliques or generic level-0 methods · Large cliques | running time | Hurt |
| Use coordinate descent with an approximate Fujishige-Wolfe level-0 solver | DSFM with large region potentials · Large cliques | ability to obtain an approximate solution | Helped |
| Warm-start Fujishige-Wolfe from the current solution and run 10 iterations (ablation) | Fujishige-Wolfe level-0 solver with coordinate descent · Large cliques with region potentials | running time | Helped |
| Use a generic Fujishige-Wolfe level-0 solver instead of the potential-specific quadratic minimization algorithm (ablation) | RCDM and ACDM · Large cliques with region potentials | running time: 4.29954 -> 4.4422 | Hurt |
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