Singular Values

Understanding Singular Values in Linear Algebra

Singular values are a fundamental concept in linear algebra with wide-reaching applications in various fields such as signal processing, statistics, and machine learning. They are closely related to the singular value decomposition (SVD) of a matrix, which is a powerful tool for analyzing the structure of matrices and solving complex numerical problems.

What are Singular Values?

Singular values are non-negative real numbers that provide important insights into the properties of a matrix. They are derived from the singular value decomposition of a matrix, which is a factorization method that generalizes the eigendecomposition of a square matrix to any \(m \times n\) matrix.

Given a matrix \(A\) of size \(m \times n\), the singular value decomposition of \(A\) is given by:

\[ A = U \Sigma V^T \]


  • \(U\) is an \(m \times m\) orthogonal matrix whose columns are the left singular vectors of \(A\).

  • \(\Sigma\) is an \(m \times n\) diagonal matrix whose non-negative diagonal entries are the singular values of \(A\).
  • \(V^T\) is the transpose of an \(n \times n\) orthogonal matrix whose columns are the right singular vectors of \(A\).

The singular values of \(A\) are the entries of \(\Sigma\), and they are typically denoted by \(\sigma_1, \sigma_2, \ldots, \sigma_p\), where \(p\) is the minimum of \(m\) and \(n\). These values are arranged in descending order, so that \(\sigma_1 \geq \sigma_2 \geq \ldots \geq \sigma_p \geq 0\).

Properties of Singular Values

Singular values have several important properties that make them useful in various applications:

  • The number of non-zero singular values of \(A\) is equal to the rank of \(A\).
  • The square of the singular values of \(A\) are the eigenvalues of both \(A^TA\) and \(AA^T\).

  • The largest singular value, \(\sigma_1\), gives the maximum norm of \(A\) when regarded as a linear transformation.

  • If all singular values are equal to one, then \(A\) is an orthogonal matrix.
  • The product of all non-zero singular values gives the absolute value of the determinant of \(A\), if \(A\) is a square matrix.

Applications of Singular Values

Singular values are used in various applications, including:

  • Data Compression: Singular value decomposition can be used to approximate a matrix with a lower rank matrix, which is the basis for data compression techniques.
  • Principal Component Analysis (PCA):

    PCA is a statistical procedure that uses SVD to identify the principal components of a dataset, which are directions of maximum variance.

  • Image Processing: SVD is used in image compression and noise reduction by retaining only the most significant singular values.
  • Numerical Solutions: SVD can be used to solve linear systems, especially when the system is ill-conditioned or does not have a unique solution.

Computing Singular Values

Computing the singular values of a matrix is a non-trivial task that typically involves numerical methods such as the QR algorithm or the Jacobi method. Most modern computational software and libraries provide built-in functions to perform SVD and directly obtain the singular values.


Singular values are a powerful tool in linear algebra that provide deep insights into the structure and properties of matrices. They are the backbone of many techniques in data analysis, numerical computing, and beyond. Understanding singular values and their computation is essential for anyone working in fields that require advanced matrix analysis and manipulation.

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