Stochastic Optimization under Distributional Drift
Authors: Joshua Cutler, Dmitriy Drusvyatskiy, Zaid Harchaoui
JMLR 2023 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical experiments illustrate our results. |
| Researcher Affiliation | Academia | Joshua Cutler EMAIL Department of Mathematics University of Washington Seattle, WA 98195-4322, USA Dmitriy Drusvyatskiy EMAIL Department of Mathematics University of Washington Seattle, WA 98195-4322, USA Zaid Harchaoui EMAIL Department of Statistics University of Washington Seattle, WA 98195-4322, USA |
| Pseudocode | Yes | Algorithm 1 Online Proximal Stochastic Gradient PSG(x0, {ηt}, T) ... Algorithm 2 Averaged Online Proximal Stochastic Gradient PSG(x0, µ, {ηt}, T) ... Algorithm 3 Decision-Dependent PSG D-PSG(x0, {ηt}, T) ... Algorithm 4 Averaged Decision-Dependent PSG D-PSG(x0, µ, γ, {ηt}, T) |
| Open Source Code | Yes | Code is available online at https://github.com/joshuacutler/Time Drift Experiments. |
| Open Datasets | No | We investigate the empirical behavior of our finite-time bounds on numerical examples with synthetic data. |
| Dataset Splits | No | To estimate the expected values and confidence intervals of xt x t 2 and ϕt(ˆxt) ϕ t , we run 100 trials with horizon T = 100. |
| Hardware Specification | No | The paper describes numerical experiments and provides parameter values, but it does not specify any particular hardware used for running these experiments. |
| Software Dependencies | No | Code is available online at https://github.com/joshuacutler/Time Drift Experiments. However, the paper does not specify software dependencies with version numbers. |
| Experiment Setup | Yes | In our simulations, we set d = 50, n = 100, and Σt = (σ2/n L)In for all t, where In denotes the n n identity matrix. We initialize x0 and x 0 using standard Gaussian entries and generate A via singular value decomposition with Haar-distributed orthogonal matrices. In Figures 1 and 2, we use default parameter values µ = L = 1, σ = 10, = 1, and the corresponding asymptotically optimal step size η = η . |