Search results
Results from the WOW.Com Content Network
In linear algebra, the singular value decomposition (SVD) is a factorization of a real or complex matrix into a rotation, followed by a rescaling followed by another rotation. It generalizes the eigendecomposition of a square normal matrix with an orthonormal eigenbasis to any m × n {\displaystyle m\times n} matrix.
LAPACK ("Linear Algebra Package") is a standard software library for numerical linear algebra.It provides routines for solving systems of linear equations and linear least squares, eigenvalue problems, and singular value decomposition.
Free Boost Software License High-performance C++ linear algebra library based on Generic programming: NAG Numerical Library: The Numerical Algorithms Group: C, Fortran 1971 many components Non-free Proprietary General purpose numerical analysis library. NMath: CenterSpace Software: C# 2003 7.1 / 12.2019 Non-free Proprietary
The Python package NumPy provides a pseudoinverse calculation through its functions matrix.I and linalg.pinv; its pinv uses the SVD-based algorithm. SciPy adds a function scipy.linalg.pinv that uses a least-squares solver. The MASS package for R provides a calculation of the Moore–Penrose inverse through the ginv function. [24] The ginv ...
In multilinear algebra, the higher-order singular value decomposition (HOSVD) of a tensor is a specific orthogonal Tucker decomposition. It may be regarded as one type of generalization of the matrix singular value decomposition. It has applications in computer vision, computer graphics, machine learning, scientific computing, and signal ...
K-SVD is an algorithm that performs SVD at its core to update the atoms of the dictionary one by one and basically is a generalization of K-means. It enforces that each element of the input data x i {\displaystyle x_{i}} is encoded by a linear combination of not more than T 0 {\displaystyle T_{0}} elements in a way identical to the MOD approach:
The SVD decomposes M into three simple transformations: a rotation V *, a scaling Σ along the rotated coordinate axes and a second rotation U. Σ is a (square, in this example) diagonal matrix containing in its diagonal the singular values of M , which represent the lengths σ 1 and σ 2 of the semi-axes of the ellipse.
In linear algebra, the generalized singular value decomposition (GSVD) is the name of two different techniques based on the singular value decomposition (SVD).The two versions differ because one version decomposes two matrices (somewhat like the higher-order or tensor SVD) and the other version uses a set of constraints imposed on the left and right singular vectors of a single-matrix SVD.