• SDPT3

  • Referenced in 707 articles [sw04009]
  • structure are exploited. We also exploit low-rank structures in the constraint matrices associated...
  • SDPLR

  • Referenced in 148 articles [sw04745]
  • based on the idea of low-rank factorization. A specialized version of SDPLR is also ... Programming Algorithm for Semidefinite Programs via Low-rank Factorization” written by S. Burer and R.D.C...
  • Manopt

  • Referenced in 124 articles [sw08493]
  • pervasively in machine learning applications, including low-rank matrix completion, sensor network localization, camera network...
  • softImpute

  • Referenced in 83 articles [sw12263]
  • columns or both, and for computing low-rank SVDs on large sparse centered matrices...
  • RTRMC

  • Referenced in 43 articles [sw20435]
  • RTRMC : Low-rank matrix completion via preconditioned optimization on the Grassmann manifold. We address ... problem of recovering large matrices of low rank when most of the entries are unknown ... exploit the geometry of the low-rank constraint to recast the problem as an unconstrained...
  • Chebfun2

  • Referenced in 41 articles [sw12708]
  • form u(y)v(x) (low rank approximants), where ... Chebfun objects. The so-called low rank approximations are constructed using an iterative algorithm that ... functions of two variables is of low rank or can be approximated...
  • ADMiRA

  • Referenced in 35 articles [sw31664]
  • address compressed sensing of a low-rank matrix posing the inverse problem as an approximation ... sparse vector and a low-rank matrix and extending efficient greedy algorithms from the vector ... from the sparse vector to the low-rank matrix case. The performance guarantee is given ... case of noisy measurements and approximately low-rank solution. With a sparse measurement operator...
  • ALEA

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

  • Referenced in 33 articles [sw09638]
  • Preconditioned low-rank methods for high-dimensional elliptic PDE eigenvalue problems. We consider elliptic ... desired solution vector x in a low-rank tensor format. In this paper ... hierarchical Tucker decomposition to develop a low-rank variant of LOBPCG, a classical preconditioned eigenvalue ... MALS with LOBPCG and with our low-rank variant is proposed. A number of numerical...
  • LowRankModels

  • Referenced in 38 articles [sw27002]
  • package for modeling and fitting generalized low rank models (GLRMs). GLRMs model a data array ... low rank matrix, and include many well known models in data analysis, such as principal...
  • Pyglrm

  • Referenced in 37 articles [sw27003]
  • package for modeling and fitting generalized low rank models (GLRMs), based on the Julia package ... model a data array by a low rank matrix, and include many well known models...
  • Tensorlab

  • Referenced in 77 articles [sw14255]
  • MLSVD), block term decompositions (BTD) and low multilinear rank approximation (LMLRA), complex optimization: quasi-Newton...
  • STRUMPACK

  • Referenced in 46 articles [sw17483]
  • both dense and sparse systems using low-rank structured factorization with randomized sampling...
  • UQLab

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

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

  • Referenced in 34 articles [sw17586]
  • matrix by the product of two low-rank nonnegative matrix factors. It has been widely...
  • OptShrink

  • Referenced in 20 articles [sw33657]
  • OptShrink: an algorithm for improved low-rank signal matrix denoising by optimal, data-driven singular ... data-driven algorithm for denoising a low-rank signal matrix buried in noise. It takes ... matrix, an estimate of the signal matrix rank and returns as an output the improved ... black-box manner wherever improving low-rank matrix estimation is desirable. The algorithm outperforms...
  • 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...
  • DSDP5

  • Referenced in 30 articles [sw04411]
  • interior-point method, sparse and low-rank data structures, extensibility that allows applications to customize...
  • Jellyfish

  • Referenced in 29 articles [sw12431]
  • valued decision variables regularized to have low rank. Particular examples of problems solvable by Jellyfish...