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A sparse matrix obtained when solving a finite element problem in two dimensions. The non-zero elements are shown in black. In numerical analysis and scientific computing, a sparse matrix or sparse array is a matrix in which most of the elements are zero. [1]
A frontal solver is an approach to solving sparse linear systems which is used extensively in finite element analysis. [1] Algorithms of this kind are variants of Gauss elimination that automatically avoids a large number of operations involving zero terms due to the fact that the matrix is only sparse. [2]
Collaborative (joint) sparse coding: The original version of the problem is defined for a single signal . In the collaborative (joint) sparse coding model, a set of signals is available, each believed to emerge from (nearly) the same set of atoms from . In this case, the pursuit task aims to recover a set of sparse representations that best ...
One then solves =, =, which can be done efficiently because the matrices are triangular. For a typical sparse matrix, the LU factors can be much less sparse than the original matrix — a phenomenon called fill-in. The memory requirements for using a direct solver can then become a bottleneck in solving linear systems.
The Sparse Approximate Inverse preconditioner minimises ‖ ‖, where ‖ ‖ is the Frobenius norm and = is from some suitably constrained set of sparse matrices. Under the Frobenius norm, this reduces to solving numerous independent least-squares problems (one for every column).
These equations describe boundary-value problems, in which the solution-function's values are specified on boundary of a domain; the problem is to compute a solution also on its interior. Relaxation methods are used to solve the linear equations resulting from a discretization of the differential equation, for example by finite differences. [2 ...
Problems in higher dimensions also lead to banded matrices, in which case the band itself also tends to be sparse. For instance, a partial differential equation on a square domain (using central differences) will yield a matrix with a bandwidth equal to the square root of the matrix dimension, but inside the band only 5 diagonals are nonzero.
When solving the minimization problems arising in the framework of bundle adjustment, the normal equations have a sparse block structure owing to the lack of interaction among parameters for different 3D points and cameras. This can be exploited to gain tremendous computational benefits by employing a sparse variant of the Levenberg–Marquardt ...