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Integer Quadratic Programming for Control

The main topic of this talk is integer quadratic programming with applications to Model Predictive Control (MPC). In each sampling time, MPC requires the solution of a Quadratic Programming (QP) problem. In recent years, the range of application of MPC has been extended to hybrid systems. Hybrid systems are systems where continuous dynamics interact with logic. When this extension is made, binary variables are introduced in the problem. As a consequence, the QP problem has to be replaced by a far more challenging Mixed Integer Quadratic Programming (MIQP) problem, which is known to have a computational complexity which grows exponentially in the number of binary optimization variables. In this talk, the problem to efficiently solve MIQP problems originating from hybrid MPC is addressed. The objective with the presentation is to give an overview of the problems in this field that have been addressed by the speaker. Four different algorithms are outlined and discussed in the presentation. The first algorithm is a preprocessing algorithm that is directly applied to the MIQP problem. Apart from the MPC application, this algorithm is also useful in Multiuser Detection (MUD). The second and third algorithms are active set QP algorithms that have been tailored for MPC and used to compute relaxations of MIQP problems. The fourth algorithm is a tailored algorithm that decreases the computational complexity for an SDP relaxation of the MIQP problem.

Type of Seminar:
Public Seminar
Dr. Daniel Axehill
May 20, 2008   14.00

Contact Person:

C. Jones
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