Globally Convergent Parallel MAP LP Relaxation Solver using the Frank-Wolfe Algorithm
Authors: Alexander Schwing, Tamir Hazan, Marc Pollefeys, Raquel Urtasun
ICML 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our method proves superior when compared to existing algorithms on a set of spin-glass models and protein design tasks. and 4. Experimental Evaluation We compare our approach to a wide variety of state-of-the-art baselines using spin-glass models of size 10 10 with variable state-space size and energy functions arising from a protein design task. |
| Researcher Affiliation | Academia | Alexander G. Schwing ASCHWING@CS.TORONTO.EDU University of Toronto, 10 King s College Rd., Toronto, Canada Tamir Hazan TAMIR@CS.HAIFA.AC.IL University of Haifa, Haifa, Israel Marc Pollefeys MARC.POLLEFEYS@INF.ETHZ.CH ETH Zurich, Universit atstrasse 6, Zurich, Switzerland Raquel Urtasun URTASUN@CS.TORONTO.EDU University of Toronto, 10 King s College Rd., Toronto, Canada |
| Pseudocode | Yes | Figure 2. Frank-Wolfe algorithm for finding the solution of the program given in Eq. (4). and Figure 4. Our efficient, parallel and provably convergent MAP LP Relaxation Solver. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described, nor does it explicitly state that code will be released or is available. |
| Open Datasets | Yes | To this end, we make use of the eight problems from the probabilistic inference challenge1. (1http://www.cs.huji.ac.il/project/PASCAL/index.php) |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning. |
| Hardware Specification | No | The paper mentions 'single core' and '16 cores' for parallelization but does not provide specific hardware details such as exact GPU/CPU models, processor types, or memory amounts. |
| Software Dependencies | No | The paper mentions using baselines from the 'STAIR library by Gould et al. (2011)' but does not provide specific version numbers for this or any other software dependencies. |
| Experiment Setup | Yes | All algorithms are restricted to at most 5,000 iterations and all baselines utilize a single core. ... We start from ϵ = 0.01 and successively decrease its value if the model is sufficiently close to |R|ϵ optimality, i.e., if ϵ is larger than f(b)/1000. ... In the following we set the number of it-erations to 400 ... we increase the number of the maximally possible iterations to 50,000 and the Frank-Wolfe iterations to 2,000 |