The Limit Points of (Optimistic) Gradient Descent in Min-Max Optimization
Authors: Constantinos Daskalakis, Ioannis Panageas
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section we provide two examples/experiments, one 2-dimensional (function f : R2 R, x, y R) and one higher dimensional (f : R10 R, x, y R5). The purpose of these experiments is to get better intuition about our findings. |
| Researcher Affiliation | Academia | Constantinos Daskalakis CSAIL MIT Cambridge, MA 02138 costis@csail.mit.edu Ioannis Panageas ISTD SUTD Singapore, 487371 ioannis@sutd.edu.sg |
| Pseudocode | No | The paper provides mathematical equations for the GDA and OGDA dynamics, but no pseudocode or algorithm blocks are present. |
| Open Source Code | No | The paper does not contain any statement or link indicating that source code for the described methodology is publicly available. |
| Open Datasets | No | The paper constructs specific polynomial functions for its examples and generates random initializations, rather than using or providing concrete access information for a publicly available dataset. |
| Dataset Splits | No | The paper describes using '10000 random initializations' in its experiments, but does not provide specific train/validation/test dataset splits or references to predefined splits, as it generates synthetic initial conditions rather than using a standard dataset. |
| 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 dependency details, such as library names with version numbers, that would be needed to replicate the experiments. |
| Experiment Setup | No | The paper mentions '10000 random initializations' and uses 'α = 0.001' for an illustration, but it does not provide comprehensive specific experimental setup details such as concrete hyperparameter values, optimizer settings, or training configurations. |