Geometric optics approximation sampling

TL;DR

This paper introduces a novel, dimension-independent sampling method based on geometric optics principles, utilizing reflector antenna systems to efficiently generate independent samples for complex target measures without gradient information.

Contribution

The paper presents a new gradient-free, dimension-independent sampling technique using reflector antenna systems and supporting paraboloids, applicable to high-dimensional Bayesian inverse problems.

Findings

Demonstrates efficiency over measure transport and MCMC methods

Provides a dimension-independent approach for constructing reflector surfaces

Validates theoretical results with numerical experiments

Abstract

In this article, we propose a new dimensionality-independent and gradient-free sampler, called Geometric Optics Approximation Sampling, which is based on the reflector antenna system. The core idea is to construct a reflecting surface that redirects rays from a source with a predetermined simpler measure towards a output domain while achieving a desired distribution defined by the projection of a complex target measure of interest. Given a such reflecting surface, one can generate arbitrarily many independent and uncorrelated samples from the target measure simply by dual re-simulating or rays tracing the reflector antenna system and then projecting the traced rays onto target domain. In order to obtain a desired reflecting surface, we use the method of supporting paraboloid to solve the reflector antenna problem that does not require a gradient information regarding the density of the target measure. Furthermore, within the supporting paraboloid method, we utilize a low-discrepancy sequence or a random sequence to discretize the target measure, which in turn yields a dimensionality-independent approach for constructing the reflecting surface. Meanwhile, we present a dual re-simulation or ray tracing method based on its dual reflecting surface, which enables drawing samples from the target measure using the reflector antenna system obtained through the dimensionality-independent method. Several examples and numerical experiments comparing with measure transport samplers as well as traditional Markov chain Monte Carlo simulations are provided in this paper to demonstrate the efficiency and applicability of our geometric optics approximation sampling, especially in the context of Bayesian inverse problems. Additionally, these numerical results confirm the theoretical findings.