The shooting algorithm for partially control-affine problems with application to an SIRS epidemiological model

TL;DR

This paper introduces a shooting algorithm tailored for partially-affine optimal control problems, demonstrating its convergence and applying it to an epidemiological SIRS model for optimal treatment and vaccination strategies.

Contribution

The paper develops a novel shooting algorithm for partially-affine control problems and proves its local quadratic convergence, with practical application to epidemiological modeling.

Findings

Algorithm converges quadratically under certain conditions

Successfully applied to an epidemiological SIRS model

Provides a new computational tool for control problems with mixed control structures

Abstract

In this article we propose a shooting algorithm for partially-affine optimal control problems, this is, systems in which the controls appear both linearly and nonlinearly in the dynamics. Since the shooting system generally has more equations than unknowns, the algorithm relies on the Gauss-Newton method. As a consequence, the convergence is locally quadratic provided that the derivative of the shooting function is injective and Lipschitz continuous at the optimal solution. We provide a proof of the convergence for the proposed algorithm using recently developed second order sufficient conditions for weak optimality of partially-affine problems. We illustrate the applicability of the algorithm by solving an optimal treatment-vaccination epidemiological problem.