Stochastic Mirror Descent in Variationally Coherent Optimization Problems
Authors: Zhengyuan Zhou, Panayotis Mertikopoulos, Nicholas Bambos, Stephen Boyd, Peter W. Glynn
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | 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 zyzhou@stanford.edu Panayotis Mertikopoulos Univ. Grenoble Alpes, CNRS, Inria, LIG panayotis.mertikopoulos@imag.fr Nicholas Bambos Stanford University bambos@stanford.edu Stephen Boyd Stanford University boyd@stanford.edu Peter Glynn Stanford University glynn@stanford.edu |
| 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 γn n 1/2 and Gaussian random noise with standard deviation about 150% the mean value of the gradient. |