Convergence of Some Convex Message Passing Algorithms to a Fixed Point
Authors: Vaclav Voracek, Tomas Werner
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We prove a stronger result (conjectured before but never proved): the iterates converge to a fixed point of the method. Moreover, we show that the algorithm terminates within O(1/ε) iterations. We first prove this for a version of coordinate descent applied to a general piecewise-affine convex objective. |
| Researcher Affiliation | Academia | 1T ubingen AI center, University of T ubingen 2Dept. of Cybernetics, Faculty of Electrical Engineering, Czech Technical University in Prague. |
| Pseudocode | Yes | Algorithm 1 Minimizing pointwise maximum of affine functions by coordinate descent; Algorithm 2 Max-sum diffusion; Algorithm 3 Max-marginal averaging |
| Open Source Code | No | The paper does not include an unambiguous statement about releasing code for the methodology described, nor does it provide a direct link to a source-code repository. |
| Open Datasets | No | The paper is purely theoretical and does not use any datasets for training or evaluation. Example 3.1 describes a hypothetical problem, but no dataset is used. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments with data. Therefore, there is no mention of dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental setup involving specific hardware. |
| Software Dependencies | No | The paper is theoretical and does not describe any experimental setup involving specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with specific hyperparameters or system-level training settings. |