Research map / Optimization / Nonconvex and smooth optimization
A Linearly Convergent Proximal Gradient Algorithm for Decentralized Optimization
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use the intermediate variable z_i rather than w_i in the dual update | P2D2 · Covtype.binary, CIFAR-10, and MNIST | relative squared error | Helped |
| Use P2D2 for decentralized composite optimization with a common nonsmooth regularizer | P2D2 · Strongly convex aggregate cost with a common convex regularizer | convergence rate | Helped |
| Set the regularizer to zero (ablation) | P2D2 · Smooth decentralized strongly convex optimization | convergence rate bound | Helped |
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