Research map / Optimization / Nonconvex and smooth optimization
Constrained Langevin Algorithms with L-mixing External Random Variables
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use a projected Langevin algorithm with polyhedral constraints and L-mixing data | Constrained non-convex Langevin sampling · Constrained problems with bounded polyhedral domains and IID data | 1-Wasserstein deviation: $O(T^{-1/4} (\log T)^{1/2})$ -> $O(T^{-1/2} \log T)$ | Helped |
| Handle constraints using the polyhedral setting | Constrained Langevin algorithm with L-mixing data · Polyhedral constraints with L-mixing data | 1-Wasserstein deviation: $O(T^{-1/2} \log T)$; unconstrained bound: $O(T^{-1/2} (\log T)^{1/2})$ | Mixed |
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