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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...
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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...
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Randomized Machine Learning Algorithms to Forecast the Evolution of Thermokarst Lakes Area in Permafrost Zones
AbstractRandomized machine learning focuses on problems with considerable uncertainty in data and models. Machine learning algorithms are formulated...
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SVD-based algorithms for fully-connected tensor network decomposition
The popular fully-connected tensor network (FCTN) decomposition has achieved successful applications in many fields. A standard method to this...
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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...
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Randomized rounding algorithms for large scale unsplittable flow problems
Unsplittable flow problems cover a wide range of telecommunication and transportation problems and their efficient resolution is key to a number of...
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Greedy Randomized Adaptive Search Procedure
Greedy randomized adaptive search procedure (GRASP) is a metaheuristic framework which has been extensively used for solving a wide variety of hard...
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An Accelerated Block Randomized Kaczmarz Method
The Kaczmarz method is a kind of row iterative method for solving large-scale linear equations. In this paper, we give a block accelerated randomized...
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Practical Sketching Algorithms for Low-Rank Tucker Approximation of Large Tensors
Low-rank approximation of tensors has been widely used in high-dimensional data analysis. It usually involves singular value decomposition (SVD) of...
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Sketch-based multiplicative updating algorithms for symmetric nonnegative tensor factorizations with applications to face image clustering
Nonnegative tensor factorizations (NTF) have applications in statistics, computer vision, exploratory multi-way data analysis, and blind source...
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Randomized Algorithms
Randomized algorithms Randomized algorithm provide a useful tool for scientific computing. Compared with standard deterministic...
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Distributed algorithms, the Lovász Local Lemma, and descriptive combinatorics
In this paper we consider coloring problems on graphs and other combinatorial structures on standard Borel spaces. Our goal is to obtain sufficient...
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Stochastic Simulation Algorithms for Iterative Solution of the Lamé Equation
AbstractIn this paper, iterative stochastic simulation algorithms for the Lamé equation describing the displacements of an isotropic elastic body are...
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A Randomized Algorithm for Tensor Singular Value Decomposition Using an Arbitrary Number of Passes
Efficient and fast computation of a tensor singular value decomposition (t-SVD) with a few passes over the underlying data tensor is crucial because...
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Randomized Kaczmarz Method for Single Particle X-Ray Image Phase Retrieval
In this chapter, we investigate phase retrieval algorithm for the single-particle X-ray imaging data. We present a variance-reduced randomized...
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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...
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Learning-augmented algorithms for online subset sum
As one of Karp’s 21 NP-complete problems, the subset sum problem, as well as its generalization, has been well studied. Among the rich literature,...
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The topology of randomized symmetry-breaking distributed computing
Studying distributed computing through the lens of algebraic topology has been the source of many significant breakthroughs during the last 2...
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Actor-Critic Reinforcement Learning Algorithms for Mean Field Games in Continuous Time, State and Action Spaces
This paper investigates mean field games in continuous time, state and action spaces with an infinite number of agents, where each agent aims to...
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On the properties of the exceptional set for the randomized Euler and Runge-Kutta schemes
We show that the probability of the exceptional set decays exponentially for a broad class of randomized algorithms approximating solutions of ODEs,...