• SLRA

  • Referenced in 24 articles [sw11262]
  • weighted 2-norm. Backward error minimization and Sylvester low-rank approximation formulations of the problem...
  • LRIPy

  • Referenced in 2 articles [sw26565]
  • convex Douglas-Rachford. Purpose: Low-rank rank inducing norms and non-convex Proximal Splitting Algoriths ... attempt to find exact rank/cardinality-r solutions to minimization problems with convex loss functions, i.e., avoiding ... proximal mappings of the low-rank inducing Frobenius and Spectral norms, as well as, their...
  • LRINorm

  • Referenced in 2 articles [sw26564]
  • Convex Proximal Splitting Methods. Low-rank rank inducing norms and non-convex Proximal Splitting Algoriths ... attempt to find exact rank/cardinality-r solutions to minimization problems with convex loss functions, i.e., avoiding ... proximal mappings of the low-rank inducing Frobenius and Spectral norms, as well as, their...
  • MSCRA_rankmin

  • Referenced in 3 articles [sw37064]
  • low-rank matrix recovery problem which can be modeled as a structured rank minimization problem ... reduction of the error and approximate rank bounds of the first stage convex relaxation ... Numerical experiments are conducted for some structured low-rank matrix recovery examples to confirm...
  • TARM

  • Referenced in 1 article [sw30728]
  • affine rank minimization. The affine rank minimization (ARM) problem arises in many real-world applications ... goal is to recover a low-rank matrix from a small amount of noisy affine...
  • PL-ranking

  • Referenced in 2 articles [sw28415]
  • low-rank optimization framework. Motivated by the fact that optimizing the top of ranking ... ranked list for a given sample and learning a low-dimensional common subspace for multi ... ranking. First, we use a pairwise ranking loss constraint to optimize the top of ranking ... number of iterations is reduced. Finally, low-rank based regularization is applied to exploit...
  • SpeeDP

  • Referenced in 4 articles [sw07003]
  • large max-cut instances We consider low-rank semidefinite programming (LRSDP) relaxations of unconstrained ... convex nonlinear programming problem of minimizing a quadratic function subject to separable quadratic equality constraints...
  • SE-Sync

  • Referenced in 9 articles [sw40678]
  • relaxation of the maximum-likelihood estimation whose minimizer provides an exact MLE so long ... this relaxation by exploiting its low-rank, geometric, and graph-theoretic structure to reduce...
  • HODLRlib

  • Referenced in 3 articles [sw32777]
  • original articles[1][2]. Low-rank approximation of the appropriate blocks are obtained using ... solver is fairly general, works with minimal restrictions and has been parallelized using OpenMP...
  • rpca

  • Referenced in 1 article [sw29201]
  • possible to recover both the low-rank and the sparse components exactly by solving ... Component Pursuit; among all feasible decompositions, simply minimize a weighted combination of the nuclear norm...
  • ARMS

  • Referenced in 65 articles [sw00048]
  • ARMS: an algebraic recursive multilevel solver for general...
  • CSDP

  • Referenced in 202 articles [sw00169]
  • CSDP, A C Library for Semidefinite Programming. This...
  • EIGIFP

  • Referenced in 46 articles [sw00235]
  • eigifp is a MATLAB program for computing a...
  • Expokit

  • Referenced in 198 articles [sw00258]
  • Expokit provides a set of routines aimed at...
  • Gmsh

  • Referenced in 720 articles [sw00366]
  • Gmsh is a 3D finite element grid generator...
  • LAPACK

  • Referenced in 1695 articles [sw00503]
  • LAPACK is written in Fortran 90 and provides...
  • LSQR

  • Referenced in 394 articles [sw00530]
  • Algorithm 583: LSQR: Sparse Linear Equations and Least...
  • Macaulay2

  • Referenced in 1904 articles [sw00537]
  • Macaulay2 is a software system devoted to supporting...
  • Magma

  • Referenced in 3296 articles [sw00540]
  • Computer algebra system (CAS). Magma is a large...
  • Maple

  • Referenced in 5363 articles [sw00545]
  • The result of over 30 years of cutting...