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Using nonlinear model predictive control for dynamic decision problems in economics

URN zum Zitieren dieses Dokuments: urn:nbn:de:bvb:703-epub-1855-4

Titelangaben

Grüne, Lars ; Semmler, Willi ; Stieler, Marleen:
Using nonlinear model predictive control for dynamic decision problems in economics.
Department of Mathematics, University of Bayreuth
Bayreuth , 2015 . - 32 S.

Volltext

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Angaben zu Projekten

Projekttitel:
Offizieller ProjekttitelProjekt-ID
Marie-Curie Initial Training Network "Sensitivity Analysis for Deterministic Controller Design" (SADCO)264735-SADCO
Fulbright CommissionOhne Angabe
Internationales Doktorandenkolleg "Identifikation, Optimierung und Steuerung für technische Anwendungen"K-NW-2004-143

Projektfinanzierung: Andere
European Union "FP7-People-ITN" programme; Fulbright Commission; Elitenetzwerk Bayern

Abstract

This paper presents a new approach to solve dynamic decision models in economics. The proposed procedure, called Nonlinear Model Predictive Control (NMPC), relies on the iterative solution of optimal control problems on finite time horizons and is well established in engineering applications for stabilization and tracking problems. Only quite recently, extensions to more general optimal control problems including those appearing in economic applications have been investigated. Like Dynamic Programming (DP), NMPC does not rely on linearization techniques but uses the full nonlinear model and in this sense provides a global solution to the problem. However, unlike DP, NMPC only computes one optimal trajectory at a time, thus avoids to grid the state space and for this reason the computational demand grows much more moderately with the space dimension than for DP. In this paper we explain the basic idea of NMPC, give a proof concerning the accuracy of NMPC for discounted optimal control problems, present implementational details, and demonstrate the ability of NMPC to solve dynamic decision problems in economics by solving low and high dimensional examples, including models with multiple equilibria, tracking and stochastic problems.

Weitere Angaben

Publikationsform: Preprint, Postprint, Working paper, Diskussionspapier
Zusätzliche Informationen (öffentlich sichtbar): 2013 (first version), 2015 (substantially revised version)
Keywords: complex decision models; long and short horizon models; dynamic optimization; multiple equilibria; regime changes; NMPC
Themengebiete aus DDC: 500 Naturwissenschaften und Mathematik > 510 Mathematik
Institutionen der Universität: Fakultäten > Fakultät für Mathematik, Physik und Informatik > Mathematisches Institut > Lehrstuhl Mathematik V (Angewandte Mathematik) > Lehrstuhl Mathematik V (Angewandte Mathematik) - Univ.-Prof. Dr. Lars Grüne
Profilfelder > Advanced Fields > Nichtlineare Dynamik
Fakultäten
Fakultäten > Fakultät für Mathematik, Physik und Informatik
Fakultäten > Fakultät für Mathematik, Physik und Informatik > Mathematisches Institut
Fakultäten > Fakultät für Mathematik, Physik und Informatik > Mathematisches Institut > Lehrstuhl Mathematik V (Angewandte Mathematik)
Profilfelder
Profilfelder > Advanced Fields
Sprache: Englisch
Titel an der UBT entstanden: Ja
URN: urn:nbn:de:bvb:703-epub-1855-4
Eingestellt am: 06 Mrz 2015 11:19
Letzte Änderung: 06 Mrz 2015 11:19
URI: https://epub.uni-bayreuth.de/id/eprint/1855