• SDPT3

  • Referenced in 703 articles [sw04009]
  • infeasible primal-dual predictor-corrector path-following method, with either ... structure are exploited. We also exploit low-rank structures in the constraint matrices associated ... semidefinite cones are calculated via the Lanczos method. Numerical experiments show that this general purpose...
  • ALEA

  • Referenced in 60 articles [sw10167]
  • python framework for spectral methods and low-rank approximations in uncertainty quantification. ALEA is intended ... research framework for numerical methods in Uncertainty Quantification (UQ). Its emphasis lies on: generalised polynomial...
  • 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...
  • DSDP5

  • Referenced in 30 articles [sw04411]
  • requirements for an interior-point method, sparse and low-rank data structures, extensibility that allows...
  • Algorithm 971

  • Referenced in 10 articles [sw22686]
  • witnessed intense development of randomized methods for low-rank approximation. These methods target principal component ... several tests, the randomized algorithms for low-rank approximation outperform or at least match ... reliability. However, the classical procedures remain the methods of choice for estimating spectral norms...
  • softImpute

  • Referenced in 83 articles [sw12263]
  • Completion via Iterative Soft-Thresholded SVD. Iterative methods for matrix completion that use nuclear-norm ... columns or both, and for computing low-rank SVDs on large sparse centered matrices...
  • RTRMC

  • Referenced in 43 articles [sw20435]
  • exploit the geometry of the low-rank constraint to recast the problem as an unconstrained ... then apply second-order Riemannian trust-region methods (RTRMC 2) and Riemannian conjugate gradient methods...
  • SLRA

  • Referenced in 24 articles [sw11262]
  • approximate GCD computations. This paper presents optimization methods and software for the approximate GCD problem ... low-rank approximation formulations of the problem are solved by the variable projection method. Optimization...
  • NeNMF

  • Referenced in 34 articles [sw17586]
  • NeNMF: An optimal gradient method for non-negative matrix factorization. Nonnegative matrix factorization ... matrix by the product of two low-rank nonnegative matrix factors. It has been widely ... multiplicative update rule (MUR), the projected gradient method (PG), the projected nonnegative least squares (PNLS...
  • ProxSDP

  • Referenced in 4 articles [sw41321]
  • low-rank structure provides a substantial speedup and allows the operator splitting method to efficiently ... instances. As opposed to other low-rank based methods, the proposed algorithm has convergence guarantees...
  • tlrmvnmvt

  • Referenced in 2 articles [sw41644]
  • package tlrmvnmvt: Low-Rank Methods for MVN and MVT Probabilities. Implementation of the classic Genz ... algorithm and a novel tile-low-rank algorithm for computing relatively high-dimensional multivariate normal ... Turkiyyah, G. M. ”Exploiting Low Rank Covariance Structures for Computing High-Dimensional Normal and Student ... Normal and Student-t Probabilities with Low-Rank Methods in R,” Journal of Statistical Software...
  • hubauth

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

  • Referenced in 2 articles [sw26564]
  • Norms and Non-Convex Proximal Splitting Methods. Low-rank rank inducing norms and non-convex...
  • Cross

  • Referenced in 7 articles [sw30282]
  • Cross: efficient low-rank tensor completion. The completion of tensors, or high-order arrays, attracts ... methods are NP-hard. In this article, we propose a framework for low-rank tensor ... recovery error over certain classes of low-rank tensors for the proposed procedure. The results ... order tensors. Simulation studies show that the method performs well under a variety of settings...
  • RADI

  • Referenced in 16 articles [sw40963]
  • immediate and efficient low-rank formulation, which is a generalization of the Cholesky-factored variant ... Lyapunov ADI method. We discuss important implementation aspects of the algorithm, such as reducing...
  • NLEIGS

  • Referenced in 34 articles [sw22547]
  • cost comparable to the Newton rational Krylov method but converges more reliably, in particular ... target set, and it also features low-rank approximation techniques for increased computational efficiency. Small...
  • SymNMF

  • Referenced in 15 articles [sw12668]
  • SymNMF: nonnegative low-rank approximation of a similarity matrix for graph clustering. Nonnegative matrix factorization ... provides a lower rank approximation of a matrix by a product of two nonnegative factors ... often superior to those by other methods such as K-means. In this paper...
  • LRIPy

  • Referenced in 2 articles [sw26565]
  • Convex Proximal Splitting Methods. Python code for Low-rank optimization by Low-Rank Inducing Norms...
  • PNKH-B

  • Referenced in 2 articles [sw40451]
  • 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 ... projected variable metric method. We present an interior point method to solve the quadratic projection ... Since the interior point method effectively exploits the low-rank structure, its computational cost only...
  • PL-ranking

  • Referenced in 2 articles [sw28415]
  • method for cross-modal retrieval named Pairwise-Listwise ranking (PL-ranking) based on the low ... design an efficient low-rank stochastic subgradient descent method to solve the proposed optimization problem...