Visualizing Feasible Regions for Optimization Problems on High-Dimensional Permutations using Dimensionality Reduction Methods

Grebennik, I; Chorna, O.; Urniaieva, I

Публікація:
Visualizing Feasible Regions for Optimization Problems on High-Dimensional Permutations using Dimensionality Reduction Methods

dc.contributor.author	Grebennik, I
dc.contributor.author	Chorna, O.
dc.contributor.author	Urniaieva, I
dc.date.accessioned	2024-06-26T12:59:30Z
dc.date.available	2024-06-26T12:59:30Z
dc.date.issued	2023
dc.description.abstract	This paper presents an investigation on the usage of modern dimensionality reduction methods for classic combinatorial optimization problems. We propose the use of t-Distributed Stochastic Neighbor Embedding (t-SNE) method to visualize feasible regions on high-dimensional permutations, aiming to avoid the consequences of combinatorial explosion. The results of the study indicate that the proposed approach can provide valuable insights and improve the understanding of the solution space of high-dimensional permutations for the application of local search approaches.
dc.identifier.citation	Grebennik I. Urniaieva Visualizing Feasible Regions for Optimization Problems on High-Dimensional Permutations using Dimensionality Reduction Methods / I. Grebennik, O. Chorna, I. Urniaieva // 2023 13th International Conference on Advanced Computer Information Technologies (ACIT), Wrocław, Poland, 2023. - pp. 126-130.
dc.identifier.doi	https://doi.org/10.1109/ACIT58437.2023.10275497
dc.identifier.uri	https://openarchive.nure.ua/handle/document/27198
dc.language.iso	en
dc.subject	permutations
dc.subject	dimensionality reduction
dc.subject	combinatorial optimization
dc.subject	permutohedron
dc.subject	t-SNE method
dc.subject	adjacency
dc.title	Visualizing Feasible Regions for Optimization Problems on High-Dimensional Permutations using Dimensionality Reduction Methods
dc.type	Conference proceedings
dspace.entity.type	Publication