POP - The Parametric Optimization Toolbox. In this paper, we describe POP, a MATLAB toolbox for parametric optimization. It features (a) efficient implementations of multiparametric programming problem solvers for multiparametric linear and quadratic programming problems and their mixed-integer counter-parts, (b) a versatile problem generator capable of creating random multiparametric programming problems of arbitrary size, and (c) a comprehensive library of multiparametric programming test problems featuring benchmark test sets for multiparametric linear, quadratic, mixed-integer linear, and mixed-integer quadratic programming problems. In addition, POP is equipped with a graphical user interface which enables the user-friendly use of all functionalities of POP and a link to the solvers of the Multi-Parametric Toolbox (MPT), as well as the ability to design explicit MPC problems. These features are demonstrated in detailed computational studies providing insights into the versatility and applicability of POP. Additionally, the example of a periodic chromatographic system is used to show the scalability of multiparametric programming in general and POP, in particular.
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References in zbMATH (referenced in 4 articles )
Showing results 1 to 4 of 4.
- Burnak, Baris; Katz, Justin; Pistikopoulos, Efstratios N.: A space exploration algorithm for multiparametric programming via Delaunay triangulation (2021)
- Pappas, Iosif; Diangelakis, Nikolaos A.; Pistikopoulos, Efstratios N.: The exact solution of multiparametric quadratically constrained quadratic programming problems (2021)
- Avraamidou, Styliani; Pistikopoulos, Efstratios N.: Multi-parametric global optimization approach for tri-level mixed-integer linear optimization problems (2019)
- Oberdieck, Richard; Diangelakis, Nikolaos A.; Pistikopoulos, Efstratios N.: Explicit model predictive control: a connected-graph approach (2017)