Sparse Quadratic Optimisation over the Stiefel Manifold with Application to Permutation Synchronisation
Authors: Florian Bernard, Daniel Cremers, Johan Thunberg
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experimentally compare our proposed approach with various methods for permutation synchronisation and perform an evaluation on both real and synthetic datasets. |
| Researcher Affiliation | Academia | Florian Bernard TU Munich, University of Bonn Daniel Cremers TU Munich Johan Thunberg Halmstad University |
| Pseudocode | Yes | Algorithm 1: Overview of our proposed algorithm. |
| Open Source Code | No | The paper does not contain an explicit statement or link indicating that the source code for the methodology described is publicly available or released by the authors. |
| Open Datasets | Yes | In this experiment we use the CMU house image sequence [1] comprising 111 frames within the experimental protocol of [34]. We reproduce the procedure described in [8] for generating synthetic instances for the synchronisation of partial permutations. |
| Dataset Splits | No | The paper describes generating and sampling problem instances but does not provide explicit details about train/validation/test splits, specific percentages, or cross-validation strategies typically used for model validation. |
| Hardware Specification | Yes | All experiments are run on a Macbook Pro (2.8 GHz quad core i7, 16 GB RAM), where for ϵ = 10 5 we use f(Ut)/f(Ut+1) 1 ϵ as convergence criterion in Algorithm 1, and a step size of αt = h(Ut, I) h T (Ut, I) 1 in (7). |
| Software Dependencies | No | The paper mentions using "the efficient implementation from the authors of [51]" and an "efficient implementation in [10]", but does not specify software dependencies (e.g., programming languages, libraries, frameworks) with version numbers for their own code. |
| Experiment Setup | Yes | All experiments are run on a Macbook Pro (2.8 GHz quad core i7, 16 GB RAM), where for ϵ = 10 5 we use f(Ut)/f(Ut+1) 1 ϵ as convergence criterion in Algorithm 1, and a step size of αt = h(Ut, I) h T (Ut, I) 1 in (7). |