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Adaptive methods
Iterative methods that collect information about the coefficient matrix during the iteration process, and use this to speed up convergence.
Backward error
The size of perturbations of the coefficient matrix and of the right hand side of a linear system , such that the computed iterate is the solution of .
Band matrix
A matrix for which there are nonnegative constants , such that if or . The two constants , are called the left and right halfbandwidth respectively.
Black box
A piece of software that can be used without knowledge of its inner workings; the user supplies the input, and the output is assumed to be correct.
Basic Linear Algebra Subprograms; a set of commonly occurring vector and matrix operations for dense linear algebra. Optimized (usually assembly coded) implementations of the BLAS exist for various computers; these will give a higher performance than implementation in high level programming languages.
Block factorization
See: Block matrix operations.
Block matrix operations
Matrix operations expressed in terms of submatrices.
The occurrence of a zero divisor in an iterative method.
Choleski decomposition
Expressing a symmetric matrix as a product of a lower triangular matrix and its transpose , that is, .
Condition number
See: Spectral condition number.
The fact whether or not, or the rate at which, an iterative method approaches the solution of a linear system. Convergence can be
Dense matrix
Matrix for which the number of zero elements is too small to warrant specialized algorithms to exploit these zeros.
Diagonally dominant matrix
See: Matrix properties
Direct method
An algorithm that produces the solution to a system of linear equations in a number of operations that is determined a priori by the size of the system. In exact arithmetic, a direct method yields the true solution to the system. See: Iterative method.
Distributed memory
See: Parallel computer.
An iterative method is said to diverge if it does not converge in a reasonable number of iterations, or if some measure of the error grows unacceptably. However, growth of the error as such is no sign of divergence: a method with irregular convergence behavior may ultimately converge, even though the error grows during some iterations.
Domain decomposition method
Solution method for linear systems based on a partitioning of the physical domain of the differential equation. Domain decomposition methods typically involve (repeated) independent system solution on the subdomains, and some way of combining data from the subdomains on the separator part of the domain.
Field of values
Given a matrix , the field of values is the set . For symmetric matrices this is the range .
A position that is zero in the original matrix but not in an exact factorization of . In an incomplete factorization, some fill elements are discarded.
Forward error
The difference between a computed iterate and the true solution of a linear system, measured in some vector norm.
See: Band matrix.
Ill-conditioned system
A linear system for which the coefficient matrix has a large condition number. Since in many applications the condition number is proportional to (some power of) the number of unknowns, this should be understood as the constant of proportionality being large.
Incomplete factorization
A factorization obtained by discarding certain elements during the factorization process (`modified' and `relaxed' incomplete factorization compensate in some way for discarded elements). Thus an incomplete factorization of a matrix will in general satisfy ; however, one hopes that the factorization will be close enough to to function as a preconditioner in an iterative method.
Approximation to the solution of a linear system in any iteration of an iterative method.
Iterative method
An algorithm that produces a sequence of approximations to the solution of a linear system of equations; the length of the sequence is not given a priori by the size of the system. Usually, the longer one iterates, the closer one is able to get to the true solution. See: Direct method.
Krylov sequence
For a given matrix and vector , the sequence of vectors , or a finite initial part of this sequence.
Krylov subspace
The subspace spanned by a Krylov sequence.
A mathematical library of linear algebra routine for dense systems solution and eigenvalue calculations.
Lower triangular matrix
Matrix for which if .
A way of writing a matrix as a product of a lower triangular matrix and a unitary matrix , that is, .
factorization / decomposition
Expressing a matrix as a product of a lower triangular matrix and an upper triangular matrix , that is, .
See: Matrix properties.
Matrix norms
See: Norms.
Matrix properties
We call a square matrix
if for all , .
Positive definite
if it satisfies for all nonzero vectors .
Diagonally dominant
if ; the excess amount is called the diagonal dominance of the matrix.
An -matrix
if for , and it is nonsingular with for all , .

Message passing
See: Parallel computer.
Multigrid method
Solution method for linear systems based on restricting and extrapolating solutions between a series of nested grids.
Modified incomplete factorization
See: Incomplete factorization.
Natural ordering
See: Ordering of unknowns.
Nonstationary iterative method
Iterative method that has iteration-dependent coefficients.
Normal equations
For a nonsymmetric or indefinite (but nonsingular) system of equations , either of the related symmetric systems () and (; ). For complex , is replaced with in the above expressions.
A function is called a vector norm if The same properties hold for matrix norms. A matrix norm and a vector norm (both denoted ) are called a mutually consistent pair if for all matrices and vectors

Ordering of unknowns
For linear systems derived from a partial differential equation, each unknown corresponds to a node in the discretization mesh. Different orderings of the unknowns correspond to permutations of the coefficient matrix. The convergence speed of iterative methods may depend on the ordering used, and often the parallel efficiency of a method on a parallel computer is strongly dependent on the ordering used. Some common orderings for rectangular domains are: For matrices from problems on less regular domains, some common orderings are:
Parallel computer
Computer with multiple independent processing units. If the processors have immediate access to the same memory, the memory is said to be shared; if processors have private memory that is not immediately visible to other processors, the memory is said to be distributed. In that case, processors communicate by message-passing.
See: Vector computer.
Positive definite matrix
See: Matrix properties.
An auxiliary matrix in an iterative method that approximates in some sense the coefficient matrix or its inverse. The preconditioner, or preconditioning matrix, is applied in every step of the iterative method.
Red/black ordering
See: Ordering of unknowns.
Reduced system
Linear system obtained by eliminating certain variables from another linear system. Although the number of variables is smaller than for the original system, the matrix of a reduced system generally has more nonzero entries. If the original matrix was symmetric and positive definite, then the reduced system has a smaller condition number.
Relaxed incomplete factorization
See: Incomplete factorization.
If an iterative method is employed to solve for in a linear system , then the residual corresponding to a vector is .
Search direction
Vector that is used to update an iterate.
Shared memory
See: Parallel computer.
Simultaneous displacements, method of
Jacobi method.
Sparse matrix
Matrix for which the number of zero elements is large enough that algorithms avoiding operations on zero elements pay off. Matrices derived from partial differential equations typically have a number of nonzero elements that is proportional to the matrix size, while the total number of matrix elements is the square of the matrix size.
Spectral condition number
The product

where and denote the largest and smallest eigenvalues, respectively. For linear systems derived from partial differential equations in 2D, the condition number is proportional to the number of unknowns.

Spectral radius
The spectral radius of a matrix is .
The set of all eigenvalues of a matrix.
Stationary iterative method
Iterative method that performs in each iteration the same operations on the current iteration vectors.
Stopping criterion
Since an iterative method computes successive approximations to the solution of a linear system, a practical test is needed to determine when to stop the iteration. Ideally this test would measure the distance of the last iterate to the true solution, but this is not possible. Instead, various other metrics are used, typically involving the residual.
Storage scheme
The way elements of a matrix are stored in the memory of a computer. For dense matrices, this can be the decision to store rows or columns consecutively. For sparse matrices, common storage schemes avoid storing zero elements; as a result they involve indices, stored as integer data, that indicate where the stored elements fit into the global matrix.
Successive displacements, method of
Gauss-Seidel method.
Symmetric matrix
See: Matrix properties.
Description of an algorithm, abstracting away from implementational details.
Adapt software for a specific application and computing environment in order to obtain better performance in that case only. itemize
Upper triangular matrix
Matrix for which if .
Vector computer
Computer that is able to process consecutive identical operations (typically additions or multiplications) several times faster than intermixed operations of different types. Processing identical operations this way is called `pipelining' the operations.
Vector norms
See: Norms.

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