SPARSE MATRICES IN DATA SCIENCE: EFFICIENT ALGORITHMS AND APPLICATIONS WITH CASE STUDY

Abstract

Sparse matrices in data structure are an important concept in data structure and algorithms. They provide a good way to store and manipulate large matrices; They are widely used in various fields for large matrices, like scientific computing, machine learning, and image processing. Create multiple fields. Effective algorithms for managing different matrices are important because they have the ability to reduce the budget and increase performance. This article examines a variety of algorithms and similar operations, including stored procedures (such as concatenated and concatenated rows), matrix-vector multiplication, and solutions to return-to-system problems. In addition, this article also examines the use of sparse matrices in optimization and parallel computing. This research shows a significant improvement in detail and insight using the technology matrix. The findings highlight the importance of visual differentiation of matrix algorithms in big data processing, highlighting their important role in the use of data science today. Effective algorithms for processing and incorporating data research demonstrate their implementation and quality.