Optimizing Euclidean Distance Computation

This paper presents a comparative analysis of seventeen different approaches to optimizing Euclidean distance computations, which is a core mathematical operation that plays a critical role in a wide range of algorithms, particularly in machine learning and data analysis. The Euclidean distance, bei...

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Veröffentlicht in:Mathematics (Basel) Jg. 12; H. 23; S. 3787
1. Verfasser: Mussabayev, Rustam
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 01.12.2024
Schlagworte:
Algorithms
Bioinformatics
Clustering
Comparative analysis
Computational linguistics
computational optimization
Data analysis
Data mining
Data structures
Datasets
Efficiency
Energy conservation
Energy consumption
Euclidean distance
Euclidean geometry
Euclidean space
Geospatial data
Germany
Information management
K-means clustering
K-nearest neighbors (KNNs)
Language processing
Linear algebra
Machine learning
Mathematical optimization
Methods
Natural language interfaces
Optimization
parallelization
Spatial data
Task complexity
vectorization
Germany
ISSN:2227-7390, 2227-7390
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Zusammenfassung:This paper presents a comparative analysis of seventeen different approaches to optimizing Euclidean distance computations, which is a core mathematical operation that plays a critical role in a wide range of algorithms, particularly in machine learning and data analysis. The Euclidean distance, being a computational bottleneck in large-scale optimization problems, requires efficient computation techniques to improve the performance of various distance-dependent algorithms. To address this, several optimization strategies can be employed to accelerate distance computations. From spatial data structures and approximate nearest neighbor algorithms to dimensionality reduction, vectorization, and parallel computing, various approaches exist to accelerate Euclidean distance computation in different contexts. Such approaches are particularly important for speeding up key machine learning algorithms like K-means and K-nearest neighbors (KNNs). By understanding the trade-offs and assessing the effectiveness, complexity, and scalability of various optimization techniques, our findings help practitioners choose the most appropriate methods for improving Euclidean distance computations in specific contexts. These optimizations enable scalable and efficient processing for modern data-driven tasks, directly leading to reduced energy consumption and a minimized environmental impact.
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ISSN:2227-7390
2227-7390
DOI:10.3390/math12233787