• ALEA

  • Referenced in 60 articles [sw10167]
  • python framework for spectral methods and low-rank approximations in uncertainty quantification. ALEA is intended...
  • ADMiRA

  • Referenced in 35 articles [sw31664]
  • low-rank matrix posing the inverse problem as an approximation problem with a specified target ... sparse vector and a low-rank matrix and extending efficient greedy algorithms from the vector ... low-rank matrix case. The performance guarantee is given in terms of the rank-restricted ... general case of noisy measurements and approximately low-rank solution. With a sparse measurement operator...
  • NLEIGS

  • Referenced in 34 articles [sw22547]
  • target set, and it also features low-rank approximation techniques for increased computational efficiency. Small...
  • JIVE

  • Referenced in 22 articles [sw09511]
  • decomposition consists of three terms: a low-rank approximation capturing joint variation across data types ... low-rank approximations for structured variation individual to each data type, and residual noise. JIVE...
  • UQLab

  • Referenced in 43 articles [sw19740]
  • Gaussian process modelling, a.k.a. Kriging, low-rank tensor approximations), rare event estimation (structural reliability), global...
  • RandNLA

  • Referenced in 22 articles [sw41749]
  • matrix multiplication, least-squares (LS) approximation, low-rank matrix approximation, and Laplacian-based linear equation...
  • SLRA

  • Referenced in 24 articles [sw11262]
  • presents optimization methods and software for the approximate GCD problem of multiple univariate polynomials ... norm. Backward error minimization and Sylvester low-rank approximation formulations of the problem are solved...
  • Algorithm 844

  • Referenced in 18 articles [sw04407]
  • reduced rank approximation to a sparse matrix A. Unfortunately, the approximations based on traditional decompositions ... algorithm, to obtain two kinds of low-rank approximations. The first, the SPQR, approximation...
  • SymNMF

  • Referenced in 15 articles [sw12668]
  • SymNMF: nonnegative low-rank approximation of a similarity matrix for graph clustering. Nonnegative matrix factorization...
  • Algorithm 971

  • Referenced in 10 articles [sw22686]
  • intense development of randomized methods for low-rank approximation. These methods target principal component analysis ... several tests, the randomized algorithms for low-rank approximation outperform or at least match...
  • FFTSVD

  • Referenced in 10 articles [sw08886]
  • octree and uses sampling to calculate low-rank approximations to dominant source distributions and responses...
  • Colibri

  • Referenced in 9 articles [sw12043]
  • large static and dynamic graphs. Low-rank approximations of the adjacency matrix of a graph ... desirable to track the low-rank structure as the graph evolves over time, efficiently...
  • hubauth

  • Referenced in 9 articles [sw38374]
  • three different algorithms: Gauss quadrature, low-rank approximation, and a hybrid method. The functions were...
  • LOBPCG

  • Referenced in 33 articles [sw09638]
  • Preconditioned low-rank methods for high-dimensional elliptic PDE eigenvalue problems. We consider elliptic ... addressed by approximating the desired solution vector x in a low-rank tensor format...
  • ID

  • Referenced in 4 articles [sw14543]
  • software package for low-rank approximation of matrices via interpolative decompositions. This software distribution provides ... Fortran routines for computing low-rank approximations to matrices, in the forms of interpolative decompositions ... approximation obtained via skeletonization, the approximation obtained via subsampling, and the approximation obtained via subset ... well as tools for computing low-rank approximations in the form of SVDs. Section...
  • NeNMF

  • Referenced in 34 articles [sw17586]
  • technique that approximates a nonnegative matrix by the product of two low-rank nonnegative matrix ... terms of efficiency as well as approximation accuracy. Compared to PNLS and AS that suffer...
  • PLANC

  • Referenced in 2 articles [sw41123]
  • PLANC. Parallel low-rank approximation with nonnegativity constraints. We consider the problem of low-rank ... approximation of massive dense nonnegative tensor data, for example, to discover latent patterns in video ... scalable parallel algorithms to compute the low-rank approximation. We present a software package called ... Parallel Low-rank Approximation with Nonnegativity Constraints, which implements our solution and allows for extension...
  • FDEMtools

  • Referenced in 4 articles [sw33993]
  • algorithm is based on a low-rank approximation of the Jacobian of the non-linear...
  • randUTV

  • Referenced in 4 articles [sw30518]
  • problems such as low-rank approximation, solving ill-conditioned linear systems, and determining bases...
  • PNKH-B

  • Referenced in 2 articles [sw40451]
  • gradient evaluations are expensive, and the (approximate) Hessian is only available through matrix-vector products ... each iteration, PNKH-B uses a low-rank approximation of the (approximate) Hessian to determine ... metric is its consistency with the low-rank approximation of the Hessian on the Krylov ... interior point method effectively exploits the low-rank structure, its computational cost only scales linearly...