7 papers
Factored Sparse Approximate Inverse Preconditioning via Spectral Optimization
Francesco Brarda, Tianshi Xu, Vassilis Kalantzis +2
In this paper, we study value selection for fixed-pattern factorized sparse approximate inverse preconditioners. Given a prescribed sparsity pattern for a factor we choose its…
Hybrid Digital-Analog Approximate Inverse Preconditioning for Krylov Methods
Shikhar Shah, Rui Peng Li, Tayfun Gokmen +3
Analog in-memory computing enables highly parallel matrix-vector multiplications with reduced data movement, but the resulting operations are noisy, quantized, and affected by devi…
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…
Subspace Projection Methods for Fast Spectral Embeddings of Evolving Graphs
Mohammad Eini, Abdullah Karaaslanli, Vassilis Kalantzis +1
Several graph data mining, signal processing, and machine learning downstream tasks rely on information related to the eigenvectors of the associated adjacency or Laplacian matrix.…
Fast Linear Solvers via AI-Tuned Markov Chain Monte Carlo-based Matrix Inversion
Anton Lebedev, Won Kyung Lee, Soumyadip Ghosh +7
Large, sparse linear systems are pervasive in modern science and engineering, and Krylov subspace solvers are an established means of solving them. Yet convergence can be slow for…
Regenerative Ulam-von Neumann Algorithm: An Innovative Markov chain Monte Carlo Method for Matrix Inversion
Soumyadip Ghosh, Lior Horesh, Vassilis Kalantzis +2
This paper presents a regenerative variant of the classical Ulam-von Neumann Markov chain Monte Carlo algorithm for the approximation of the matrix inverse. The algorithm presented…