HQP (Huge Quadratic Programming) consists of mainly two parts: the actual HQP optimizer and the front-end Omuses. Both parts are designed as framework in the programming language C++.
The actual HQP optimizer treats nonlinearly constrained problems with a sequential quadratic programming (SQP) algorithm. An interior-point method is applied to the solution of convex quadratic subproblems.
The implementation is based on sparse matrix codes of the Meschach library for matrix computations in C. The matrix library was extended with additional routines for the analysis and direct solution of sparse equation systems.
The tool command language Tcl is used for selecting solver modules, configuring parameters and for controlling the execution.
The front-end Omuses provides additional support for the efficient problem formulation. This is possible thanks to the availability of great software packages that have been integrated with Omuses.
ADOL-C is exploited for the automatic differentiation and structural analysis of model equations.
Furthermore Omuses provides numerical solvers for differential equations defining constraints in dynamic optimization problems. Besides own implementations (Dopri5, Euler, GRK4, IMP, OdeTs, SDIRK, RK4), the following additional software packages are currently integrated:
Please note that the integrated software packages underly copyright restrictions of their respective authors. That is why the DASPK software is not included, though an interface is provided. HQP is available under the GNU Library (or Lesser) General Public License.
Optimization problems can be formulated natively in C/C++. The following interfaces exist (sorted from high-level to low-level):
HQP is hosted at SourceForge.
The most interesting applications are where HQP is running round-the-clock to help making our world a better place. They include
R. Franke and B. Weidmann. Steaming ahead with optimizing power plant boiler startup. Power Engineering International -- PEi, 15(6), 2007.
R. Franke and L. Vogelbacher. Nonlinear model predictive control for cost optimal startup of steam power plants. at -- Automatisierungstechnik, 54(12):630--637, 2006.
H. Linke, E. Arnold, and R. Franke. Optimal water management of a canal system. In J.C. Refsgaard and E.A. Karalis, editors, Operational Water Management, pages 211--218. Balkema, Rotterdam, 1997.
Furthermore, there finds several applications to the research on
Z.K. Nagy, B. Mahn, R. Franke, and F. Allgöwer. Evaluation study of an efficient output feedback nonlinear model predictive control for temperature tracking in an industrial batch reactor. Control Engineering Practice, 15(7):839 -- 850, 2007.
C. Hoffmann and H. Puta. Dynamic optimization of energy supply systems with Modelica models. In Proceedings of the 5th International Modelica Conference. Modelica Association, Vienna, Austria, September 2006.
H. Linke. Wasserbewirtschaftung von Binnenschiffahrtsgewässern auf Basis einer modellgestützten Vorhersage des Systemverhaltens. Dissertation, Cuvillier Verlag, Göttingen, 2006.
G. Reichl. Optimierte Bewirtschaftung von Kläranlagen basierend auf der Modellierung mit Modelica. Dissertation, Cuvillier Verlag, Göttingen, 2005.
R. Nyström, R. Franke, I. Harjunkowski, and A. Kroll. Production campaign planning including grade transition sequencing and dynamic optimization. Computers and Chemical Engineering, 29:2163--2179, 2005.
R. Franke. Integrierte dynamische Modellierung und Optimierung von Systemen mit saisonaler Wärmespeicherung, Dissertation, volume 394 of Fortschritt-Berichte VDI, series 6. VDI-Verlag, Düsseldorf, 1998.