List: Numerical linear algebra

by likeorhate

•  Gaussian elimination

  In linear algebra, Gaussian elimination is an algorithm for solving systems of linear equations, finding the rank of a matrix, and calculating the inverse of an invertible square matrix. Gaussian elimination is named after German mathematician and scientist Carl Friedrich Gauss. Elementary row operations are used to reduce a matrix to row echelon form. Gauss–Jordan elimination, an extension of this algorithm, reduces the matrix further to reduced row echelon form.

  

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•  MATLAB

  MATLAB is a numerical computing environment and fourth-generation programming language. Developed by The MathWorks, MATLAB allows matrix manipulations, plotting of functions and data, implementation of algorithms, creation of user interfaces, and interfacing with programs written in other languages, including C, C++, and Fortran. Although MATLAB is numeric only, an optional toolbox uses the MuPAD symbolic engine, allowing access to computer algebra capabilities.

  

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•  Basis function

  In mathematics, a basis function is an element of a particular basis for a function space. Every function in the function space can be represented as a linear combination of basis functions, just as every vector in a vector space can be represented as a linear combination of basis vectors.

  

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•  Kernel (matrix)

  In linear algebra, the kernel or null space (also nullspace) of a matrix A is the set of all vectors x for which Ax = 0. The kernel of a matrix with n columns is a linear subspace of n-dimensional Euclidean space. If viewed as a linear transformation, the null space of a matrix is precisely the kernel of the mapping (i.e. the set of vectors that map to zero).

  

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•  Hilbert matrix

  In linear algebra, a Hilbert matrix is a square matrix with entries being the unit fractions For example, this is the 5 × 5 Hilbert matrix: The Hilbert matrix can be regarded as derived from the integral that is, as a Gramian matrix for powers of x. It arises in the least squares approximation of arbitrary functions by polynomials. The Hilbert matrices are canonical examples of ill-conditioned matrices, making them notoriously difficult to use in numerical computation.

  

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•  Row echelon form

  In linear algebra a matrix is in row echelon form if All nonzero rows (rows with at least one nonzero element) are above any rows of all zeroes, and The leading coefficient (the first nonzero number from the left, also called the pivot) of a nonzero row is always strictly to the right of the leading coefficient of the row above it.

  

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•  Moore-Penrose pseudoinverse

  

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•  Sparse matrix

  In the subfield of numerical analysis, a sparse matrix is a matrix populated primarily with zeros. The term itself was coined by Harry M. Markowitz. Conceptually, sparsity corresponds to systems which are loosely coupled. Consider a line of balls connected by springs from one to the next; this is a sparse system. By contrast, if the same line of balls had springs connecting each ball to all other balls, the system would be represented by a dense matrix.

  

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•  Vandermonde matrix

  In linear algebra, a Vandermonde matrix, named after Alexandre-Théophile Vandermonde, is a matrix with the terms of a geometric progression in each row, i.e. , an m × n matrix or for all indices i and j. (Some authors use the transpose of the above matrix. ) The determinant of a square Vandermonde matrix (where m=n) can be expressed as: This is called the Vandermonde determinant or Vandermonde polynomial.

  

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•  Triangular matrix

  In the mathematical discipline of linear algebra, a triangular matrix is a special kind of m x n matrix where the entries either below or above the main diagonal are zero. A matrix which is conjugate to a triangular matrix is called triangularizable. Because matrix equations with triangular matrices are easier to solve they are very important in numerical analysis.

  

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•  LINPACK

  LINPACK is a software library for performing numerical linear algebra on digital computers. It was written in Fortran by Jack Dongarra, Jim Bunch, Cleve Moler, and Gilbert Stewart, and was intended for use on supercomputers in the 1970s and early 1980s. It has been largely superseded by LAPACK, which will run more efficiently on modern architectures. LINPACK makes use of the BLAS (Basic Linear Algebra Subprograms) libraries for performing basic vector and matrix operations.

  

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•  Householder transformation

  In linear algebra, a Householder transformation (also known as Householder reflection or elementary reflector) is a linear transformation that describes a reflection about a plane or hyperplane containing the origin. Householder transformations are widely used in numerical linear algebra, to perform QR decompositions and in the first step of the QR algorithm. The Householder transformation was introduced in 1958 by Alston Scott Householder.

  

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•  Rayleigh quotient iteration

  Rayleigh quotient iteration is an eigenvalue algorithm which extends the idea of the inverse iteration by using the Rayleigh quotient to obtain increasingly accurate eigenvalue estimates. Rayleigh quotient iteration is an iterative method, that is, it must be repeated until it converges to an answer (this is true of all eigenvalue algorithms). Fortunately, very rapid convergence is guaranteed and no more than a few iterations are needed in practice.

  

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•  Eigenvalue algorithm

  In linear algebra, one of the most important problems is designing efficient and stable algorithms for finding the eigenvalues of a matrix. These eigenvalue algorithms may also find eigenvectors.

  

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•  Inverse iteration

  In numerical analysis, inverse iteration is an iterative eigenvalue algorithm. It allows to find an approximate eigenvector when an approximation to corresponding eigenvalue is already known. Conceptually it is very close to the power method. The technique is also known as the Inverse power method.

  

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•  QR algorithm

  In numerical linear algebra, the QR algorithm is an eigenvalue algorithm: that is, a procedure to calculate the eigenvalues and eigenvectors of a matrix. The QR transformation was developed in 1961 by John G.F. Francis (England) and by Vera N. Kublanovskaya (USSR), working independently. The basic idea is to perform a QR decomposition, writing the matrix as a product of an orthogonal matrix and an upper triangular matrix, multiply the factors in the other order, and iterate.

  

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•  Givens rotation

  In numerical linear algebra, a Givens rotation is a rotation in the plane spanned by two coordinates axes. Givens rotations are named after Wallace Givens who introduced them to numerical analysts in the 1950s while he was working at Argonne National Laboratory.

  

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•  Strassen algorithm

  In the mathematical discipline of linear algebra, the Strassen algorithm, named after Volker Strassen, is an algorithm used for matrix multiplication. It is asymptotically faster than the standard matrix multiplication algorithm, but slower than the fastest known algorithm, and is useful in practice for large matrices.

  

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•  Circulant matrix

  In linear algebra, a circulant matrix is a special kind of Toeplitz matrix where each row vector is rotated one element to the right relative to the preceding row vector. In numerical analysis circulant matrices are important because they are diagonalized by a discrete Fourier transform, and hence linear equations that contain them may be quickly solved using a fast Fourier transform.

  

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•  LAPACK

  LAPACK (Linear Algebra PACKage) is a 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. It also includes routines to implement the associated matrix factorizations such as LU, QR, Cholesky and Schur decomposition. LAPACK was originally written in FORTRAN 77 and is now written in Fortran 90.

  

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•  Minimum degree algorithm

  In numerical analysis the minimum degree algorithm is an algorithm used to permute the rows and columns of a symmetric sparse matrix before applying the Cholesky decomposition, to reduce the number of non-zeros in the Cholesky factor. This results in reduced storage requirements and means that the Cholesky factor, or sometimes an incomplete Cholesky factor used as a preconditioner (for example in the preconditioned conjugate gradient algorithm) can be applied with fewer arithmetic operations.

  

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•  Basic Linear Algebra Subprograms

  Basic Linear Algebra Subprograms (BLAS) is a de facto application programming interface standard for publishing libraries to perform basic linear algebra operations such as vector and matrix multiplication. They were first published in 1979, and are used to build larger packages such as LAPACK. Heavily used in high-performance computing, highly optimized implementations of the BLAS interface have been developed by hardware vendors such as Intel and AMD, as well as by other authors, e.g.

  

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•  General Matrix Multiply

  The General Matrix Multiply (GEMM) is a subroutine in the Basic Linear Algebra Subprograms (BLAS) which performs matrix multiplication, that is the multiplication of two matrices. This includes: SGEMM for single precision, DGEMM for double-precision, CGEMM for complex single precision, and ZGEMM for complex double precision. GEMM is often tuned by High Performance Computing vendors to run as fast as possible, because it is the building block for so many other routines.

  

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•  Divide-and-conquer eigenvalue algorithm

  Divide-and-conquer eigenvalue algorithms are a class of eigenvalue algorithms for Hermitian or real symmetric matrices that have recently (circa 1990s) become competitive in terms of stability and efficiency with more traditional algorithms such as the QR algorithm. The basic concept behind these algorithms is of course the famous divide-and-conquer approach from computer science.

  

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•  Arnoldi iteration

  In numerical linear algebra, the Arnoldi iteration is an eigenvalue algorithm and an important example of iterative methods. Arnoldi finds the eigenvalues of general matrices; an analogous method for Hermitian matrices is the Lanczos iteration. The Arnoldi iteration was invented by W. E. Arnoldi in 1951. The term iterative method, used to describe Arnoldi, can perhaps be somewhat confusing. Note that all general eigenvalue algorithms must be iterative.

  

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