Sparse approximation (also known as sparse representation) theory deals with sparse solutions for systems of linear equations. Techniques for finding...

sparse coding R {\displaystyle R} with a given dictionary D {\displaystyle \mathbf {D} } is known as sparse approximation (or sometimes just sparse coding...

Cholesky factorization of a symmetric positive definite matrix is a sparse approximation of the Cholesky factorization. An incomplete Cholesky factorization...

special cases of the sparse general Vecchia approximation. These methods approximate the true model in a way the covariance matrix is sparse. Typically, each...

feedforward neural networks for classification, regression, clustering, sparse approximation, compression and feature learning with a single layer or multiple...

Matching pursuit (MP) is a sparse approximation algorithm which finds the "best matching" projections of multidimensional data onto the span of an over-complete...

(abbreviated as ILU) of a matrix is a sparse approximation of the LU factorization often used as a preconditioner. Consider a sparse linear system A x = b {\displaystyle...

Cholesky factorization — sparse approximation to the Cholesky factorization Incomplete LU factorization — sparse approximation to the LU factorization...

the California Institute of Technology. He is known for work on sparse approximation, numerical linear algebra, and random matrix theory. Tropp studied...

sensing in speech signals Low-density parity-check code Noiselet Sparse approximation Sparse coding Verification-based message-passing algorithms in compressed...

Zero-truncated Poisson distribution Compound Poisson distribution Sparse approximation Hurdle model pscl, glmmTMB and brms R packages Bilder, Christopher;...

Originally, shearlets were introduced in 2006 for the analysis and sparse approximation of functions f ∈ L 2 ( R 2 ) {\displaystyle f\in L^{2}(\mathbb {R}...

roughly 100,000 neurons. Other models are based on matching pursuit, a sparse approximation algorithm which finds the "best matching" projections of multidimensional...

Wang-Q (2011). "Compactly supported shearlets are optimally sparse". Journal of Approximation Theory. 163 (11): 1564–1589. arXiv:1002.2661. doi:10.1016/j...

hierarchical matrices (H-matrices) are used as data-sparse approximations of non-sparse matrices. While a sparse matrix of dimension n {\displaystyle n} can be...

In the mathematical theory of artificial neural networks, universal approximation theorems are theorems of the following form: Given a family of neural...

weighted completion time Block Sorting (Sorting by Block Moves) Sparse approximation Variations of the Steiner tree problem. Specifically, with the discretized...

a function of many variables Hierarchical matrix, a data-sparse approximation of a non-sparse matrix Hilbert matrix, a square matrix with entries being...

different when restricted to special classes of graphs. For instance, for sparse graphs (graphs in which the number of edges is at most a constant times...

In mathematics, low-rank approximation refers to the process of approximating a given matrix by a matrix of lower rank. More precisely, it is a minimization...

Lasso (statistics) Least-squares spectral analysis Matching pursuit Sparse approximation A. M. Tillmann Equivalence of Linear Programming and Basis Pursuit...

improving approximate solutions for a class of problems, in which the i-th approximation (called an "iterate") is derived from the previous ones. A specific...

won the Fields Medal for proving Roth's theorem on the Diophantine approximation of algebraic numbers. He was also a winner of the De Morgan Medal and...

of admissible solutions, increasing the correctness of the function approximation. This way, embedding this prior information into a neural network results...

Sylvain Fischer, Rafael Redondo, Laurent Perrinet, Gabriel Cristobal. Sparse approximation of images inspired from the functional architecture of the primary...

independent sets in sparse graphs, a case that does not make sense for the complementary clique problem, there has also been work on approximation algorithms that...

randomized rounding is a widely used approach for designing and analyzing approximation algorithms. Many combinatorial optimization problems are computationally...

both sparse (few edges crossing the cut) and balanced (close to a bisection). The problem is known to be NP-hard, and the best known approximation algorithm...

[better source needed] Sparse approximation Singular value decomposition Matrix norm k-means clustering Low-rank approximation Michal Aharon; Michael...

S2CID 18432970. Joel A. Tropp (2004). "Greed is good: Algorithmic results for sparse approximation" (PDF). CiteSeerX 10.1.1.84.5256. Mutual coherence R1magic : R package...