In this technical article, we present a dual active-set solver for quadratic programming that has properties suitable for use in embedded model predictive control applications. In particu- lar, the solver is efficient, can easily be warm started, and is simple to code. Moreover, the exact worst-case computational complex- ity of the solver can be determined offline and, by using outer proximal-point iterations, ill-conditioned problems can be handled in a robust manner.

A Dual Active-Set Solver for Embedded Quadratic Programming Using Recursive LDLT Updates

Bemporad A.;
2022-01-01

Abstract

In this technical article, we present a dual active-set solver for quadratic programming that has properties suitable for use in embedded model predictive control applications. In particu- lar, the solver is efficient, can easily be warm started, and is simple to code. Moreover, the exact worst-case computational complex- ity of the solver can be determined offline and, by using outer proximal-point iterations, ill-conditioned problems can be handled in a robust manner.
2022
Embedded optimization, model predictive control (MPC), quadratic programming (QP)
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11771/30978
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