Exploiting Chordality in Optimization Algorithms for Model Predictive Control

TL;DR

This paper demonstrates how leveraging chordal structure in optimization problems can significantly improve the efficiency and parallelizability of algorithms used in model predictive control, especially for distributed and scenario-based formulations.

Contribution

It introduces a framework that exploits chordal structure for efficient computation and parallelization in optimization algorithms for model predictive control.

Findings

Enhanced computational efficiency in MPC problems

Effective parallelization of interior-point methods

Applicability to distributed and scenario-based formulations

Abstract

In this chapter we show that chordal structure can be used to devise efficient optimization methods for many common model predictive control problems. The chordal structure is used both for computing search directions efficiently as well as for distributing all the other computations in an interior-point method for solving the problem. The chordal structure can stem both from the sequential nature of the problem as well as from distributed formulations of the problem related to scenario trees or other formulations. The framework enables efficient parallel computations.