Optimized Two-Step Coarse Propagators in Parareal Algorithms

TL;DR

This paper introduces an optimized two-step coarse propagator for the parareal algorithm, significantly improving convergence speed and providing a practical framework for accelerating parallel-in-time solutions of parabolic equations.

Contribution

It develops a rigorous error estimate for the two-step parareal algorithm and constructs an optimized coarse propagator achieving near-zero convergence factor.

Findings

Achieves an optimized convergence factor of approximately 0.0064.

Demonstrates rapid convergence in numerical experiments on linear and nonlinear parabolic equations.

Provides a quantitative guideline for designing coarse propagators based on the theoretical estimate.

Abstract

In this work, we propose a novel framework for accelerating the parareal algorithm, in which the coarse propagator is formulated as a two-step method and optimized with respect to the convergence factor.} We derive a rigorous error estimate for the proposed two-step parareal algorithm, yielding an explicit bound on the linear convergence factor. This estimate is not only of theoretical interest: it provides a quantitative guideline for selecting and designing coarse propagators. Guided by this estimate, we {consider the linear parabolic equation as an illustrative example and }construct an optimized two-step coarse propagator~(O2CP) that delivers very fast convergence in practice. The resulting method attains an optimized convergence factor of approximately $0.0064$, substantially smaller than that of commonly used practical coarse propagators in the classical parareal setting, while keeping the computational cost moderate. Numerical experiments on linear and nonlinear parabolic equations fully support the theoretical analysis and demonstrate rapid convergence of the two-step parareal algorithm equipped with the O2CP.