Last-Iterate Convergence for Generalized Frank-Wolfe in Monotone Variational Inequalities
Authors: Zaiwei Chen, Eric Mazumdar
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we conduct numerical simulations to empirically verify the performance of our proposed algorithms. |
| Researcher Affiliation | Academia | Zaiwei Chen Purdue IE West Lafayette, IN 47907 chen5252@purdue.edu Eric Mazumdar Caltech CMS Pasadena, CA 91125 mazumdar@caltech.edu |
| Pseudocode | Yes | Algorithm 1 Smoothed Fictitious Play (from Player 1 s perspective); Algorithm 2 Generalized Frank-Wolfe for Monotone Variational Inequalities; Algorithm 3 Stochastic Frank-Wolfe for Monotone Variational Inequalities |
| Open Source Code | No | This paper is a theoretical work, and the numerical simulations are conducted on synthetic examples to demonstrate the effectiveness of the proposed algorithm. |
| Open Datasets | No | The paper uses synthetic game setups (Rock-Paper-Scissors, Burglar-Policeman, Randomly Generated Matrix Game) for numerical simulations, but does not provide concrete access information or formal citations for these as publicly available datasets. |
| Dataset Splits | No | The numerical simulations describe game setups and parameter choices but do not specify explicit training, validation, or test splits for the data. |
| Hardware Specification | No | The paper states that it is theoretical work with numerical simulations on synthetic examples, and thus does not provide specific hardware details (like GPU/CPU models or memory) used for running experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies used in the numerical simulations. |
| Experiment Setup | Yes | In the stochastic setting, we choose τ = 0.1, α = 0.1, and β = 0.01 in Algorithm 3. |