Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Stochastic Mirror Descent in Variationally Coherent Optimization Problems
Authors: Zhengyuan Zhou, Panayotis Mertikopoulos, Nicholas Bambos, Stephen Boyd, Peter W. Glynn
NeurIPS 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | simulation results are also presented. See Figure 2 for a simulation example. Figure 3: SMD run on the objective function of Fig. 2 |
| Researcher Affiliation | Academia | Zhengyuan Zhou Stanford University EMAIL Panayotis Mertikopoulos Univ. Grenoble Alpes, CNRS, Inria, LIG EMAIL Nicholas Bambos Stanford University EMAIL Stephen Boyd Stanford University EMAIL Peter Glynn Stanford University EMAIL |
| Pseudocode | Yes | Algorithm 1 Stochastic mirror descent (SMD) Algorithm 2 Mirror descent (MD) |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository. |
| Open Datasets | No | The paper describes simulations on a synthetic objective function (g(r, θ) from Figure 2) but does not use or provide access information for any publicly available or open dataset. |
| Dataset Splits | No | The paper does not specify any dataset split information (e.g., percentages or counts for training, validation, or test sets). |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for running its simulations. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., programming languages, libraries, or solvers). |
| Experiment Setup | Yes | Figure 3: SMD run on the objective function of Fig. 2 with Îłn n 1/2 and Gaussian random noise with standard deviation about 150% the mean value of the gradient. |