• Tensorlab

  • Referenced in 77 articles [sw14255]
  • block term decompositions (BTD) and low multilinear rank approximation (LMLRA), complex optimization: quasi-Newton ... complex variables including numerical complex differentiation, global minimization of bivariate polynomials and rational functions: both ... cumulants, tensor visualization, estimating a tensor’s rank or multilinear rank...
  • SLRA

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

  • Referenced in 3 articles [sw37064]
  • modeled as a structured rank minimization problem. We reformulate this problem as a mathematical program ... reduction of the error and approximate rank bounds of the first stage convex relaxation...
  • TARM

  • Referenced in 1 article [sw30728]
  • turbo-type algorithm for affine rank minimization. The affine rank minimization (ARM) problem arises ... goal is to recover a low-rank matrix from a small amount of noisy affine...
  • DSPCA

  • Referenced in 35 articles [sw04804]
  • positive, semidefinite symmetric matrix by a rank-one matrix, with an upper bound ... problem. We also discuss Nesterov’s smooth minimization technique applied to the SDP arising...
  • MSOPS-II

  • Referenced in 13 articles [sw11982]
  • significant enhancements allow the new MSOPS-II ranking process to be used as part ... general-purpose multi/many objective optimisation algorithm, requiring minimal initial configuration...
  • IML - Integer Matrix Library

  • Referenced in 16 articles [sw00440]
  • matrix. Certified linear system solving: compute a minimal denominator solution x to a system ... integer matrix with arbitrary shape and rank profile. In addition, IML provides some low level...
  • Neurofitter

  • Referenced in 14 articles [sw09289]
  • error function or fitness function makes the ranking of different parameter sets possible. The second ... find the best parameter set in a minimal amount of time. In this review...
  • preCICE

  • Referenced in 27 articles [sw08713]
  • proven scalability on 10000s of MPI Ranks. The software offers methods for transient equation coupling ... CalculiX, are available. Due to the minimally-invasive approach of preCICE, adapters for in-house...
  • RANKBOOST+

  • Referenced in 2 articles [sw35566]
  • RankBoost designed to minimize a different upper bound on rank loss...
  • OEIG

  • Referenced in 4 articles [sw07166]
  • possible to rank deficient; i.e., we search for λ that locally minimize the smallest singular...
  • svt

  • Referenced in 1 article [sw37232]
  • achieve shrinkage and low rank solutions. To minimize a nuclear norm regularized loss function...
  • timma

  • Referenced in 1 article [sw17706]
  • Inhibition Interaction using Maximization and Minimization Averaging. Prediction and ranking of drug combinations based...
  • SBmethod

  • Referenced in 12 articles [sw07710]
  • Rendl [2000]; Helmberg and Kiwiel [1999] for minimizing the maximum eigenvalue of an affine matrix ... matrices such as sparsity and low rank structure. The code comes with ABSOLUTELY NO WARRANTY...
  • GZoltar

  • Referenced in 2 articles [sw26885]
  • analyzed to both minimize the test suite and return a ranked list of diagnosis candidates...
  • PAGE

  • Referenced in 7 articles [sw23828]
  • method that uses predefined gene sets and ranks of genes to identify significant biological changes ... given microarray data set is minimal or moderate. Results: We developed a modified gene...
  • PL-ranking

  • Referenced in 2 articles [sw28415]
  • ranking loss constraint ignores class information, we further adopt a listwise constraint to minimize ... number of iterations is reduced. Finally, low-rank based regularization is applied to exploit...
  • 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...
  • PAL-Tiling

  • Referenced in 2 articles [sw28419]
  • objective under convergence guarantees. To simulate the minimization subject to the constraint that the matrices ... enables an automatic determination of the factorization rank...