Results 21 to 30 of about 3,401,789 (270)
SMASH: Co-designing Software Compression and Hardware-Accelerated Indexing for Efficient Sparse Matrix Operations [PDF]
Micro, 2019 Important workloads, such as machine learning and graph analytics applications, heavily involve sparse linear algebra operations. These operations use sparse matrix compression as an effective means to avoid storing zeros and performing unnecessary ...
Konstantinos Kanellopoulos +8 more
semanticscholar +1 more source
Large Covariance Estimation by Thresholding Principal Orthogonal Complements [PDF]
, 2013 This paper deals with the estimation of a high-dimensional covariance with a conditional sparsity structure and fast-diverging eigenvalues. By assuming sparse error covariance matrix in an approximate factor model, we allow for the presence of some cross-
Fan, Jianqing +2 more
core +2 more sources
On sharp performance bounds for robust sparse signal recoveries [PDF]
, 2009 It is well known in compressive sensing that l_1 minimization can recover the sparsest solution for a large class of underdetermined systems of linear equations, provided the signal is sufficiently sparse.
Hassibi, Babak, Xu, Weiyu
core +1 more source
Load-balancing Sparse Matrix Vector Product Kernels on GPUs
ACM Transactions on Parallel Computing, 2020 Efficient processing of Irregular Matrices on Single Instruction, Multiple Data (SIMD)-type architectures is a persistent challenge. Resolving it requires innovations in the development of data formats, computational techniques, and implementations that ...
H. Anzt +8 more
semanticscholar +1 more source
Sparse Matrix Methods in Optimization [PDF]
SIAM Journal on Scientific and Statistical Computing, 1984 Sparse matrix techniques for the solution of three subdivisions of optimization are surveyed. Newton type, sparse quasi-Newton type methods conjugate-gradient methods are considered for the unconstrained optimization. Solving the null-space equations and the range-space equations for the linearly constrained optimization, the emphasis is laid on the ...
Gill, Philip E. +3 more
openaire +1 more source
Efficient Tiled Sparse Matrix Multiplication through Matrix Signatures
International Conference for High Performance Computing, Networking, Storage and Analysis, 2020 Tiling is a key technique to reduce data movement in matrix computations. While tiling is well understood and widely used for dense matrix/tensor computations, effective tiling of sparse matrix computations remains a challenging problem.
S. Kurt +3 more
semanticscholar +1 more source
Parallelizable sparse inverse formulation Gaussian processes (SpInGP)
, 2017 We propose a parallelizable sparse inverse formulation Gaussian process (SpInGP) for temporal models. It uses a sparse precision GP formulation and sparse matrix routines to speed up the computations.
Grigorievskiy, Alexander +2 more
core +1 more source
An Improved Lower Bound for Sparse Reconstruction from Subsampled Hadamard Matrices
, 2019 We give a short argument that yields a new lower bound on the number of subsampled rows from a bounded, orthonormal matrix necessary to form a matrix with the restricted isometry property. We show that a matrix formed by uniformly subsampling rows of an $
Błasiok, Jarosław +4 more
core +1 more source
Cache-Oblivious Sparse Matrix–Vector Multiplication by Using Sparse Matrix Partitioning Methods [PDF]
SIAM Journal on Scientific Computing, 2009 In this article, we introduce a cache-oblivious method for sparse matrix–vector multiplication. Our method attempts to permute the rows and columns of the input matrix using a recursive hypergraph-based sparse matrix partitioning scheme so that the resulting matrix induces cache-friendly behavior during sparse matrix–vector multiplication. Matrices are
Yzelman, A.N., Bisseling, R.H.
openaire +2 more sources
Fast Methods for Recovering Sparse Parameters in Linear Low Rank Models
, 2016 In this paper, we investigate the recovery of a sparse weight vector (parameters vector) from a set of noisy linear combinations. However, only partial information about the matrix representing the linear combinations is available.
Amini, Arash +2 more
core +1 more source