Search
Search Results
-
A semi-randomized Kaczmarz method with simple random sampling for large-scale linear systems
Randomized Kaczmarz-type methods are appealing for large-scale linear systems arising from big data problems. One of the keys of randomized...
-
A linear algebra perspective on the random multi-block ADMM: the QP case
Embedding randomization procedures in the Alternating Direction Method of Multipliers (ADMM) has recently attracted an increasing amount of interest...
-
Learning Elliptic Partial Differential Equations with Randomized Linear Algebra
Given input–output pairs of an elliptic partial differential equation (PDE) in three dimensions, we derive the first theoretically rigorous scheme...
-
-
Subspace Acceleration for a Sequence of Linear Systems and Application to Plasma Simulation
We present an acceleration method for sequences of large-scale linear systems, such as the ones arising from the numerical solution of time-dependent...
-
Randomized linear algebra for model reduction—part II: minimal residual methods and dictionary-based approximation
A methodology for using random sketching in the context of model order reduction for high-dimensional parameter-dependent systems of equations was...
-
A randomized operator splitting scheme inspired by stochastic optimization methods
In this paper, we combine the operator splitting methodology for abstract evolution equations with that of stochastic methods for large-scale...
-
Randomized Kaczmarz algorithm with averaging and block projection
The randomized Kaczmarz algorithm is a simple iterative method for solving linear systems of equations. This study proposes a variant of the...
-
CPQR-based randomized algorithms for generalized CUR decompositions
Based on the column pivoted QR decomposition, we propose some randomized algorithms including pass-efficient ones for the generalized CUR...
-
Randomized Kaczmarz for tensor linear systems
Solving linear systems of equations is a fundamental problem in mathematics. When the linear system is so large that it cannot be loaded into memory...
-
A Robust Randomized Indicator Method for Accurate Symmetric Eigenvalue Detection
We propose a robust randomized indicator method for the reliable detection of eigenvalue existence within an interval for symmetric matrices A . An...
-
Simpler is better: a comparative study of randomized pivoting algorithms for CUR and interpolative decompositions
Matrix skeletonizations like the interpolative and CUR decompositions provide a framework for low-rank approximation in which subsets of a given...
-
A fast randomized algorithm for computing an approximate null space
Randomized algorithms in numerical linear algebra can be fast, scalable and robust. This paper examines the effect of sketching on the right singular...
-
Multi-step greedy Kaczmarz algorithms with simple random sampling for solving large linear systems
By exploiting the simple random sampling and the greedy technique for capturing large residual entries, we put forth two multi-step greedy Kaczmarz...
-
Classification and Applications of Randomized Functional Numerical Algorithms for the Solution of Second-Kind Fredholm Integral Equations
Systematization of numerical randomized functional algorithms for approximation of solutions to second-kind Fredholm integral equation is performed...
-
Efficient randomized tensor-based algorithms for function approximation and low-rank kernel interactions
In this paper, we introduce a method for multivariate function approximation using function evaluations, Chebyshev polynomials, and tensor-based...
-
A block-randomized stochastic method with importance sampling for CP tensor decomposition
One popular way to compute the CANDECOMP/PARAFAC (CP) decomposition of a tensor is to transform the problem into a sequence of overdetermined least...
-
A Randomized Singular Value Decomposition for Third-Order Oriented Tensors
The oriented singular value decomposition (O-SVD) proposed by Zeng and Ng provides a hybrid approach to the t-product-based third-order tensor...
-
Randomized linear algebra for model reduction. Part I: Galerkin methods and error estimation
We propose a probabilistic way for reducing the cost of classical projection-based model order reduction methods for parameter-dependent linear...
-
Optimal decorrelated score subsampling for generalized linear models with massive data
In this paper, we consider the unified optimal subsampling estimation and inference on the low-dimensional parameter of main interest in the presence...