Multidimensional Phase Recovery and Interpolative Decomposition Butterfly Factorization

TL;DR

This paper introduces a new framework for efficiently evaluating oscillatory integral transforms in multiple dimensions by recovering phase functions from indirect data and applying a specialized butterfly factorization.

Contribution

The paper presents a novel $O(N ext{log}N)$ method for phase function recovery and a multidimensional interpolative decomposition butterfly factorization for fast matrix-vector multiplication.

Findings

Effective phase recovery from indirect access demonstrated.

Achieves $O(N ext{log}N)$ complexity in multidimensional settings.

Numerical results confirm the efficiency and accuracy of the framework.

Abstract

This paper focuses on the fast evaluation of the matvec $g=Kf$ for $K\in \mathbb{C}^{N\times N}$, which is the discretization of a multidimensional oscillatory integral transform $g(x) = \int K(x,\xi) f(\xi)d\xi$ with a kernel function $K(x,\xi)=e^{2\pi\i \Phi(x,\xi)}$, where $\Phi(x,\xi)$ is a piecewise smooth phase function with $x$ and $\xi$ in $\mathbb{R}^d$ for $d=2$ or $3$. A new framework is introduced to compute $Kf$ with $O(N\log N)$ time and memory complexity in the case that only indirect access to the phase function $\Phi$ is available. This framework consists of two main steps: 1) an $O(N\log N)$ algorithm for recovering the multidimensional phase function $\Phi$ from indirect access is proposed; 2) a multidimensional interpolative decomposition butterfly factorization (MIDBF) is designed to evaluate the matvec $Kf$ with an $O(N\log N)$ complexity once $\Phi$ is available. Numerical results are provided to demonstrate the effectiveness of the proposed framework.