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In this repository, we deal with the compressed row/column (CSR/CSC) format representation of sparse matrices and its manipulation. Compressed sparse matrix formats store the sparse matrixes in a much more efficient way with reduced space requirements.

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vineeths96/Sparse-Matrix

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Sparse Matrix

Compressed Sparse Row (CSR) and Compressed Sparse Column (CSC)
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View Problem Statement

tags : compressed matrices, sparse matrix, csr, csc, coo,

About The Project

The program deals with the compressed row, compressed column, and coordinate format of sparse matrix representation and its manipulation. Compressed sparse matrix formats stores the sparse matrices in much more efficient way with reduced space requirements. The theory in detail has been provided at SparseDataStructs file .The entire program is written in three files - client.c, header.c, and implement.c. Comments have been added frequently to help in understanding the logic behind implementation. The program has five functions pertaining one to each question and two additional functions to sort and input data to matrices. Refer Problem statement file for detailed information.

Built With

This project was built with

  • C
  • Ubuntu 18.04.1
  • gcc version 7.4.0

Getting Started

Clone the repository into a local machine using

git clone https://github.com/vineeths96/Sparse-Matrix

Instructions to run

Open the terminal, make the program and run it.

make
./a.out

Enter the values from the file (or custom values) as requested during execution of the program.

Testcases

The testcase.txt contains the 5 test cases. Comments have been added to guide through the test case file to identify where to input data.

License

Distributed under the MIT License. See LICENSE for more information.

Contact

Vineeth S - vs96codes@gmail.com

Project Link: https://github.com/vineeths96/Sparse-Matrix

About

In this repository, we deal with the compressed row/column (CSR/CSC) format representation of sparse matrices and its manipulation. Compressed sparse matrix formats store the sparse matrixes in a much more efficient way with reduced space requirements.

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