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..
Time-Reversed Dissipation Induces Duality Between Minimizing Gradient Norm and Function Value
Authors: Jaeyeon Kim, Asuman Ozdaglar, Chanwoo Park, Ernest Ryu
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we present H-duality, which represents a surprising one-to-one correspondence between methods efficiently minimizing function values and methods efficiently minimizing gradient magnitude. In continuous-time formulations, H-duality corresponds to reversing the time dependence of the dissipation/friction term. To the best of our knowledge, H-duality is different from Lagrange/Fenchel duality and is distinct from any previously known duality or symmetry relations. Using H-duality, we obtain a clearer understanding of the symmetry between Nesterov’s method and OGM-G, derive a new class of methods efficiently reducing gradient magnitudes of smooth convex functions, and find a new composite minimization method that is simpler and faster than FISTA-G. |
| Researcher Affiliation | Academia | Jaeyeon Kim Seoul National University EMAIL Asuman Ozdaglar MIT EECS EMAIL Chanwoo Park MIT EECS EMAIL Ernest K. Ryu Seoul National University EMAIL |
| Pseudocode | No | The paper presents algorithms like (OGM), (OGM-G), and (SFG) using mathematical equations and variable definitions, but these are integrated into the text and not formatted as distinct pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any statements about releasing open-source code or links to a code repository. |
| Open Datasets | No | The paper is theoretical and does not describe experiments involving datasets for training. |
| Dataset Splits | No | The paper is theoretical and does not describe experiments involving dataset splits for validation. |
| Hardware Specification | No | The paper is theoretical and does not mention any hardware specifications used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup details such as hyperparameters or training configurations. |