Bilinear Optimal Control of an Advection-reaction-diffusion System

TL;DR

This paper addresses the complex problem of bilinear optimal control in advection-reaction-diffusion systems, proving existence of solutions and developing an efficient nested conjugate gradient method for numerical solutions.

Contribution

It establishes the existence of optimal controls without extra assumptions and proposes a novel nested CG algorithm for efficient computation under divergence-free constraints.

Findings

Proved existence of optimal controls in general settings.

Developed a nested CG method with preconditioning and inexact stepsize strategy.

Numerical experiments demonstrate the efficiency of the proposed method.

Abstract

We consider the bilinear optimal control of an advection-reaction-diffusion system, where the control arises as the velocity field in the advection term. Such a problem is generally challenging from both theoretical analysis and algorithmic design perspectives mainly because the state variable depends nonlinearly on the control variable and an additional divergence-free constraint on the control is coupled together with the state equation. Mathematically, the proof of the existence of optimal solutions is delicate, and up to now, only some results are known for a few special cases where additional restrictions are imposed on the space dimension and the regularity of the control. We prove the existence of optimal controls and derive the first-order optimality conditions in general settings without any extra assumption. Computationally, the well-known conjugate gradient (CG) method can be applied conceptually. However, due to the additional divergence-free constraint on the control variable and the nonlinear relation between the state and control variables, it is challenging to compute the gradient and the optimal stepsize at each CG iteration, and thus nontrivial to implement the CG method. To address these issues, we advocate a fast inner preconditioned CG method to ensure the divergence-free constraint and an efficient inexactness strategy to determine an appropriate stepsize. An easily implementable nested CG method is thus proposed for solving such a complicated problem. For the numerical discretization, we combine finite difference methods for the time discretization and finite element methods for the space discretization. Efficiency of the proposed nested CG method is promisingly validated by the results of some preliminary numerical experiments.