Global Non-convex Optimization with Discretized Diffusions
Authors: Murat A. Erdogdu, Lester Mackey, Ohad Shamir
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Figure 1: The left plot shows the landscape of the non-convex, sublinear growth function f(x) = c log(1 + 1 2). The middle and right plots compare the optimization error of gradient descent, the Langevin algorithm, and the discretized diffusion designed in Section 5.1. |
| Researcher Affiliation | Collaboration | Murat A. Erdogdu 1,2 erdogdu@cs.toronto.edu 1University of Toronto 2Vector Institute Lester Mackey 3 lmackey@ microsoft.com 3Microsoft Research Ohad Shamir 4 ohad.shamir@weizmann.ac.il 4Weizmann Institute of Science |
| Pseudocode | No | The paper describes mathematical equations and procedures but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement about releasing source code or a link to a code repository for the described methodology. |
| Open Datasets | No | The paper describes theoretical functions and general learning problems (e.g., 'regularized loss minimization') but does not specify the use of any publicly available datasets with access information or formal citations. |
| Dataset Splits | No | The paper does not provide specific details on training, validation, or test dataset splits (e.g., percentages, sample counts, or references to predefined splits). |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., CPU, GPU models, or cloud instance types) used for running experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers. |
| Experiment Setup | Yes | Here, d = 2, c = 10, the inverse temperature γ = 1, the step size = 0.1, and each algorithm is run from the initial point (90, 110). |