Randomized-Accelerated FEAST: A Hybrid Approach for Large-Scale Eigenvalue Problems

TL;DR

RA-FEAST is a hybrid algorithm that combines contour-integration eigensolvers with randomized linear algebra to efficiently compute large-scale eigenvalues, achieving significant speedups while maintaining accuracy.

Contribution

The paper introduces RA-FEAST, a novel hybrid method that integrates randomized initialization with FEAST to accelerate large-scale eigenvalue computations.

Findings

Achieves up to 38x speedup on sparse graph Laplacian benchmarks.

Maintains high-accuracy eigenspace approximations.

Demonstrates over tenfold speedup compared to standard FEAST.

Abstract

We present Randomized-Accelerated FEAST (RA-FEAST), a hybrid algorithm that combines contour-integration-based eigensolvers with randomized numerical linear algebra techniques for efficiently computing partial eigendecompositions of large-scale matrices arising in statistical applications. By incorporating randomized subspace initialization to enable aggressive quadrature reduction and truncated refinement iterations, our method achieves significant computational speedups (up to 38x on sparse graph Laplacian benchmarks at n = 8000) while maintaining high-accuracy approximations to the target eigenspace. We provide a probabilistic error bound for the randomized warmstart, a stability result for inexact FEAST iterations under general perturbations, and a simple complexity model characterizing the trade-off between initialization cost and solver speedup. Empirically, we demonstrate that RA-FEAST can be more than an order of magnitude faster than standard FEAST while preserving accuracy on sparse Laplacian problems representative of modern spectral methods in statistics.