On the Convergence Rate of Decomposable Submodular Function Minimization
Authors: Robert Nishihara, Stefanie Jegelka, Michael I Jordan
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we show that the algorithm converges linearly, and we provide upper and lower bounds on the rate of convergence. Our proof relies on the geometry of submodular polyhedra and draws on results from spectral graph theory. |
| Researcher Affiliation | Academia | Robert Nishihara, Stefanie Jegelka, Michael I. Jordan Electrical Engineering and Computer Science University of California Berkeley, CA 94720 {rkn,stefje,jordan}@eecs.berkeley.edu |
| Pseudocode | No | The paper describes the Alternating Projections algorithm but does not provide a formally structured pseudocode or algorithm block. |
| Open Source Code | No | The paper does not mention providing open-source code for the described methods. |
| Open Datasets | No | The paper is theoretical and does not involve training models or datasets. |
| Dataset Splits | No | The paper is theoretical and does not mention validation splits or processes related to experimental data. |
| Hardware Specification | No | The paper is theoretical and does not mention specific hardware used for any experiments. |
| Software Dependencies | No | The paper is theoretical and does not list specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or training configurations. |