Sign-Switched Antithetic Coupling for Signed Gradient-Particle Methods

Abstract

Gradient random-walk methods represent the spatial derivative of a solution by signed particles and recover the solution by summing their masses. We reduce the sampling error of such simulations for one-dimensional convection-diffusion equations by running them in pairs with particles matched in order of position. Reflected pairing, the standard antithetic choice, gives matched particles opposite displacements. In sign-switched pairing, matched particles of equal sign receive opposite displacements and those of opposite sign the same displacement. Each simulation keeps the probability distribution of the solver, so the pair average has the expectation and bias of one simulation. For a fixed matching, we prove that sign-switched pairing minimizes the variance contribution of each matched pair at the last diffusion step. We also derive an exact formula for the variance it removes relative to reflected pairing. For the heat equation both results extend to every step, and we measure the accumulated variance reduction numerically. In Burgers tests, with sample sizes chosen from pilot runs and checked on fresh runs, sign-switched pairing meets a mean-squared-error target with 4 simulations, compared with 65 for independent sampling and 34 for reflected pairing. Against within-sign pairing, which matches only particles of equal sign, it has about 26% lower variance with 8192 particles. With 2048 particles it meets a second target in about 40% less time. The gain persists for further profiles, later times, other viscosities, and a cubic flux, supporting sign-switched pairing as an inexpensive way to improve repeated field estimates where particles of opposite sign meet.