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..
Accelerated Mirror Descent in Continuous and Discrete Time
Authors: Walid Krichene, Alexandre Bayen, Peter L. Bartlett
NeurIPS 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We test these methods on numerical examples in Section 5 and comment on their performance. The results are given in Figure 1. |
| Researcher Affiliation | Academia | Walid Krichene UC Berkeley EMAIL Alexandre M. Bayen UC Berkeley EMAIL Peter L. Bartlett UC Berkeley and QUT EMAIL |
| Pseudocode | Yes | Algorithm 1 Accelerated mirror descent with distance generating function ฯ , regularizer R, step size s, and parameter r 3 and Algorithm 2 Accelerated mirror descent with restart |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code, nor does it include links to a code repository. |
| Open Datasets | No | The paper describes generating data for two different objective functions: a simple quadratic f(x) = x x , Q(x x ) , for a random positive semi-de๏ฌnite matrix Q, and a log-sum-exp function... where each entry in ai Rn and bi R is iid normal. No publicly available dataset is mentioned or linked. |
| Dataset Splits | No | The paper does not discuss dataset splits (training, validation, or test) as it focuses on optimizing generated objective functions rather than using pre-existing datasets with fixed splits. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions or specific solver versions). |
| Experiment Setup | Yes | We test the accelerated mirror descent method in Algorithm 1, on simplex-constrained problems in Rn, n = 100... (c) Effect of the parameter r. r = 3 r = 10 r = 30 r = 90. The restart conditions are the following: (i) gradient restart: x(k+1) x(k), f(x(k)) > 0, and (ii) speed restart: x(k+1) x(k) < x(k) x(k 1) . |