34 citations · 163 across the 27 of their papers we have counts for
10 papers · 1 filter
Counting Triangles of Graphs via Randomized Trace Estimation with Incomplete Matrix-Vector Products
Soumyadip Ghosh, Lior Horesh, Vasileios Kalantzis +3
Counting triangles in graphs is a fundamental operation in network analysis, underpinning metrics such as clustering coefficients and serving as a signal for community detection, l…
Analysis of Power Iteration Algorithm with Partially Observed Matrix-vector Products
Soumyadip Ghosh, Lior Horesh, Vassilis Kalantzis +3
We consider the problem of computing the dominant eigenvector of a symmetric matrix via the power iteration algorithm subject to constraints in the computation of matrix-vector pr…
Multi-Function Multi-Way Analog Technology for Sustainable Machine Intelligence Computation
Vassilis Kalantzis, Mark S. Squillante, Shashanka Ubaru +12
Numerical computation is essential to many areas of artificial intelligence (AI), whose computing demands continue to grow dramatically, yet their continued scaling is jeopardized…
Randomized matrix-free quadrature: unified and uniform bounds for stochastic Lanczos quadrature and the kernel polynomial method
Tyler Chen, Thomas Trogdon, Shashanka Ubaru
We analyze randomized matrix-free quadrature algorithms for spectrum and spectral sum approximation. The algorithms studied include the kernel polynomial method and stochastic Lanc…
Sparse graph based sketching for fast numerical linear algebra
Dong Hu, Shashanka Ubaru, Alex Gittens +3
In recent years, a variety of randomized constructions of sketching matrices have been devised, that have been used in fast algorithms for numerical linear algebra problems, such a…
Projection techniques to update the truncated SVD of evolving matrices
Vassilis Kalantzis, Georgios Kollias, Shashanka Ubaru +3
This paper considers the problem of updating the rank-k truncated Singular Value Decomposition (SVD) of matrices subject to the addition of new rows and/or columns over time. Such…
Find the dimension that counts: Fast dimension estimation and Krylov PCA
Shashanka Ubaru, Abd-Krim Seghouane, Yousef Saad
High dimensional data and systems with many degrees of freedom are often characterized by covariance matrices. In this paper, we consider the problem of simultaneously estimating t…
Spectrum-Adapted Polynomial Approximation for Matrix Functions
Li Fan, David I Shuman, Shashanka Ubaru +1
We propose and investigate two new methods to approximate for large, sparse, Hermitian matrices . The main idea behind both methods is to first estimat…
Sampling and multilevel coarsening algorithms for fast matrix approximations
Shashanka Ubaru, Yousef Saad
This paper addresses matrix approximation problems for matrices that are large, sparse and/or that are representations of large graphs. To tackle these problems, we consider algori…
Fast estimation of approximate matrix ranks using spectral densities
Shashanka Ubaru, Yousef Saad, Abd-Krim Seghouane
In many machine learning and data related applications, it is required to have the knowledge of approximate ranks of large data matrices at hand. In this paper, we present two comp…