A Distributed Algorithm to Determine Lower and Upper Bounds in Branch
and Bound for Hybrid Model Predictive Control
Reference
A. Firooznia,
R. Bourdais, and
B. De Schutter,
"A Distributed Algorithm to Determine Lower and Upper Bounds in Branch
and Bound for Hybrid Model Predictive Control," Proceedings of the 54th IEEE Conference on Decision and
Control, Osaka, Japan, pp. 1736-1741, Dec. 2015.
Abstract
In this work, a class of model predictive control problems with mixed
real-valued and binary control signals is considered. The optimization
problem to be solved is a constrained Mixed Integer Quadratic
Programming (MIQP) problem. The main objective is to derive a
distributed algorithm for limiting the search space in branch and
bound approaches by tightening the lower and upper bounds of objective
function. To this aim, a distributed algorithm is proposed for the
convex relaxation of the MIQP problem via dual decomposition. The
effectiveness of the approach is illustrated with a case study.
Downloads
- Corresponding technical report:
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Bibtex entry
@inproceedings{FirDeS:15-024,
author={A. Firooznia and R. Bourdais and B. {D}e Schutter},
title={A Distributed Algorithm to Determine Lower and Upper Bounds in Branch and
Bound for Hybrid Model Predictive Control},
booktitle={Proceedings of the 54th IEEE Conference on Decision and Control},
address={Osaka, Japan},
pages={1736--1741},
month=dec,
year={2015}
}
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Last update: February 21, 2026.