Jacobisvd

The jacobisvd is a Python package used for computing the singular value decomposition (SVD) of a given matrix using the Jacobi method. SVD is a matrix factorization technique widely used in linear algebra and data analysis.

Installation

You can install the jacobisvd package using pip:

pip install jacobisvd

Features

• Computes the SVD of a matrix using the Jacobi method.
• Efficiently handles large matrices.
• Supports both real and complex matrix inputs.
• Provides options for specifying the desired number of singular values/vectors to compute.

Usage

To use the jacobisvd package in your Python code, you need to import it:

import jacobisvd

Computing SVD

To compute the SVD of a given matrix, use the compute_svd function. Here's an example:

import jacobisvd

matrix = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
singular_values, left_singular_vectors, right_singular_vectors = jacobisvd.compute_svd(matrix)

print("Singular Values:", singular_values)
print("Left Singular Vectors:\n", left_singular_vectors)
print("Right Singular Vectors:\n", right_singular_vectors)

Specifying Number of Singular Values/Vectors

By default, the compute_svd function computes all the singular values and vectors of the given matrix. However, you can specify the number of singular values/vectors to compute using the num_singular_values parameter. Example:

import jacobisvd

matrix = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
singular_values, left_singular_vectors, right_singular_vectors = jacobisvd.compute_svd(matrix, num_singular_values=2)

print("Singular Values:", singular_values)
print("Left Singular Vectors:\n", left_singular_vectors)
print("Right Singular Vectors:\n", right_singular_vectors)

Conclusion

The jacobisvd package provides a convenient way to compute the SVD of a matrix using the Jacobi method. It offers flexibility in specifying the desired number of singular values/vectors. With its efficient implementation, it is well-suited for handling large matrices. Give it a try in your next linear algebra or data analysis project!