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.
Python 2001 1.11.1 / 6.2023 Free BSD: Based on Python Xtensor [12] S. Corlay, W. Vollprecht, J. Mabille et al. C++ 2016 0.21.10 / 11.2020 Free 3-clause BSD: Xtensor is a C++ library meant for numerical analysis with multi-dimensional array expressions, broadcasting and lazy computing.
Umple code embedding one or more of Java, Python, C++, PHP or Ruby Pure Umple code describing associations, patterns, state machines, etc. Java, Python, C++, PHP, Ruby, ECcore, Umlet, Yuml, Textuml, JSON, Papyrus XMI, USE, NuXMV, Alloy Velocity apache: Java Passive [2] Tier Templates Java driver code Any text Yii2 Gii: PHP Active Tier
A number of solutions to the problem have appeared in literature, notably Davenport's q-method, [2] QUEST and methods based on the singular value decomposition (SVD). Several methods for solving Wahba's problem are discussed by Markley and Mortari.
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 applied mathematics, k-SVD is a dictionary learning algorithm for creating a dictionary for sparse representations, via a singular value decomposition approach. k-SVD is a generalization of the k-means clustering method, and it works by iteratively alternating between sparse coding the input data based on the current dictionary, and updating the atoms in the dictionary to better fit the data.
The term higher order singular value decomposition (HOSVD) was coined be DeLathauwer, but the algorithm referred to commonly in the literature as the HOSVD and attributed to either Tucker or DeLathauwer was developed by Vasilescu and Terzopoulos. [6] [7] [8] Robust and L1-norm-based variants of HOSVD have also been proposed. [9] [10] [11] [12]
The singular value decomposition of the Hankel matrix provides a means of computing the A, B, and C matrices which define the state-space realization. [4] The Hankel matrix formed from the signal has been found useful for decomposition of non-stationary signals and time-frequency representation.