• 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 ... low-rank constraint to recast the problem as an unconstrained optimization problem on a single...
  • Manopt

  • Referenced in 124 articles [sw08493]
  • particular, optimization on manifolds is well-suited to deal with rank and orthogonality constraints. Such ... pervasively in machine learning applications, including low-rank matrix completion, sensor network localization, camera network ... experimenting with state of the art Riemannian optimization algorithms. We aim particularly at reaching practitioners...
  • rsvd

  • Referenced in 10 articles [sw16104]
  • used to compute the near optimal low-rank singular value decomposition of massive data sets...
  • OptShrink

  • Referenced in 20 articles [sw33657]
  • algorithm for improved low-rank signal matrix denoising by optimal, data-driven singular value shrinkage ... data-driven algorithm for denoising a low-rank signal matrix buried in noise. It takes...
  • 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...
  • LRIPy

  • Referenced in 2 articles [sw26565]
  • Python Package for Rank Constrained Optimization by Low-Rank Inducing Norms and Non-Convex ... Proximal Splitting Methods. Python code for Low-rank optimization by Low-Rank Inducing Norms...
  • Optspace

  • Referenced in 8 articles [sw12630]
  • consider the problem of reconstructing a low-rank matrix from a small subset ... followed by local manifold optimization, for solving the low-rank matrix completion problem ... original matrix, so that local optimization reconstructs the correct matrix with high probability. We present...
  • MALSAR

  • Referenced in 5 articles [sw14319]
  • Multi-Task Learning; Alternating Structural Optimization; Incoherent Low-Rank and Sparse Learning; Robust Low-Rank...
  • PL-ranking

  • Referenced in 2 articles [sw28415]
  • ranking (PL-ranking) based on the low-rank optimization framework. Motivated by the fact that ... 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 ... efficient low-rank stochastic subgradient descent method to solve the proposed optimization problem. The experimental...
  • SE-Sync

  • Referenced in 12 articles [sw40678]
  • low-rank, geometric, and graph-theoretic structure to reduce it to an equivalent optimization problem...
  • 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 ... aforementioned problems. It applies Nesterov’s optimal gradient method to alternatively optimize one factor with...
  • LRINorm

  • Referenced in 2 articles [sw26564]
  • MATLAB Package for Rank Constrained Optimization by Low-Rank Inducing Norms and Non-Convex Proximal...
  • Hm-toolbox

  • Referenced in 7 articles [sw32776]
  • matrices. Matrices with hierarchical low-rank structure, including HODLR and HSS matrices, constitute a versatile ... optimal performance. Nevertheless, it maintains the favorable complexity of hierarchical low-rank matrices and offers...
  • Meta-AAD

  • Referenced in 2 articles [sw41888]
  • anomalies given a time budget. Some re-ranking strategies have been proposed to approximate ... greedy strategies could be sub-optimal since some low-ranked instances could be more helpful ... select the most proper instance to explicitly optimize the number of discovered anomalies throughout...
  • Cross

  • Referenced in 7 articles [sw30282]
  • Cross: efficient low-rank tensor completion. The completion of tensors, or high-order arrays, attracts ... achieve recovery is not guaranteed to be optimal. In addition, the implementation of some previous ... article, we propose a framework for low-rank tensor completion via a novel tensor measurement...
  • ProxSDP

  • Referenced in 4 articles [sw41321]
  • Exploiting low-rank structure in semidefinite programming by approximate operator splitting. In contrast to many ... other convex optimization classes, state-of-the-art semidefinite programming solvers are still unable ... able to exploit the low-rank property inherent to several semidefinite programming problems. Exploiting...
  • 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 ... input, and a symmetric nonnegative lower rank approximation is computed. We show that SymNMF ... SymNMF and spectral clustering. We propose two optimization algorithms for SymNMF and discuss their convergence...
  • BudgetedSVM

  • Referenced in 4 articles [sw10893]
  • open-source C++ toolbox comprising highly-optimized implementations of recently proposed algorithms for scalable training ... approximators: Adaptive Multi-hyperplane Machines, Low-rank Linearization SVM, and Budgeted Stochastic Gradient Descent. BudgetedSVM...
  • FMMTL

  • Referenced in 2 articles [sw12639]
  • low-rank expansions. In the domain of high performance computing, this includes the optimized construction...
  • HODLRlib

  • Referenced in 4 articles [sw32777]
  • higher dimensions. Further, the solver has been optimized and the running time of the solver ... original articles[1][2]. Low-rank approximation of the appropriate blocks are obtained using...