Research map / Optimization / Nonconvex and smooth optimization
The power of first-order smooth optimization for black-box non-smooth problems
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Batched accelerated gradient inside the randomized smoothing scheme (batch-parallel zeroth-order method) | non-smooth convex optimization · theory, p=2 | iteration complexity | Helped |
| Use of Shamir-style two-point gradient estimator with batching instead of Duchi et al. style finite differences | non-smooth convex zeroth-order optimization · theory | oracle complexity | Helped |
| Replace batched accelerated gradient with batched consensus-projected method in decentralized setting | non-smooth decentralized saddle-point problems · theory | communication rounds: ~sqrt(d) factor loss | Hurt |
| Smoothing scheme with one-point instead of two-point feedback | stochastic non-smooth optimization · theory | oracle complexity | Hurt |
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