Opticon: An algorithm for the optimal control of nonlinear stochastic models

Matulka, Josef; Neck, Reinhard

doi:10.1007/bf02071066

Cited by 55 publications

(28 citation statements)

References 4 publications

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“…In our experiments we use the last version of the OPTCON algorithm, which is called OPTCON2. We skip the presentation of the OPTCON algorithm, which can be found in more detail in Matulka and Neck (1992) and Blueschke-Nikolaeva et al (2012), and discuss only those issues relevant for this paper.…”

Section: Optconmentioning

confidence: 99%

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Solving Nonlinear Stochastic Optimal Control Problems Using Evolutionary Heuristic Optimization

Savin

Blueschke

2013

SSRN Journal

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Solving nonlinear stochastic optimal control problems using evolutionary heuristic optimization * I. Savin † ‡ and D. Blueschke § Abstract Policy makers constantly face optimal control problems: what controls allow to achieve certain targets in, e.g., GDP growth or inflation? Conventionally this is done by applying certain linear-quadratic optimization algorithms to dynamic econometric models. Several algorithms extend this baseline framework to nonlinear stochastic problems. However, those algorithms are limited in a variety of ways including, most importantly, restriction to local best solutions only and the symmetry of objective function. In Blueschke et al. (2013a) a new flexible optimization method based on Differential Evolution is suggested. It allows to lift these limitations and achieve better approximations of the policy targets, but is designed to deterministic problems only. This study extends the methodology by dealing with stochastic problems in two different ways: applying extreme event analysis and by minimizing the median objective value. Thus, this research is aimed to broaden the range of decision support information used by policy makers in choosing optimal strategy under much more realistic conditions. Terms of use: Documents in

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Section: Optconmentioning

confidence: 99%

“…Chow (1975), Chow (1981) and Kendrick (1981)). One of the algorithms which deals with these types of problems is OPTCON described in Matulka and Neck (1992) and Blueschke-Nikolaeva et al (2012). However, the OPTCON algorithm still relies on the LQ optimization technique and, therefore, has some limitations typical for this framework.…”

Section: Introductionmentioning

confidence: 99%

Solving Nonlinear Stochastic Optimal Control Problems Using Evolutionary Heuristic Optimization

Savin

Blueschke

2013

SSRN Journal

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“…Thus, an objective function can be specified, for ease of computation generally in the form of an additively separable quadratic welfare loss function, which is minimized subject to the econometric system. OPTCON (Matulka and Neck, 1992) is an optimization algorithm that is able to deal with both deterministic and stochastic setups (with uncertainty in the parameters and in the residuals) for linear and non-linear problems, which distinguishes OPTCON from other widely used algorithms (Chow, 1975;Chow, 1981, Kendrick, 1981. Due to limitations in previous versions of OPTCON concerning highly non-linear systems of medium to large scale, version 3.0001b (Haber, 2000) is used for the (deterministic) optimizations in this paper.…”

Section: Model Frameworkmentioning

confidence: 99%

Impacts of Family Businesses on Economic Stability and Growth –An Optimization Approach

Haber

2005

IFAC Proceedings Volumes

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Family businesses are an important factor for technological progress and high economic efficiency, determining a significant part of economic strength. At present, inside the European Monetary Union (EMU), there are efforts to reduce public debt to fulfill the criteria imposed by the Stability and Growth Pact (SGP), which are mandatory for members of the Euro zone. Due to these constraints, public expenditure has been cut in several European countries. In this paper, the economic implications for optimal budgetary stabilization under alternative rates of technological change are evaluated for Austria, in order to assess the importance of family businesses for stabilization policy and growth.

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“…Here we use the algorithm OPTCON, developed by Matulka and Neck (1992); it determines approximate solutions of deterministic or stochastic optimum control problems with a quadratic objective function and a nonlinear multivariable dynamic model. The objective function has to be quadratic in the deviations of the state and control variables from their respective desired values.…”

Section: The Optimum Control Approachmentioning

confidence: 99%

Tradeoffs of Austrian Budgetary Policies: An Optimum Control Analysis

Neck

Stieber

2005

IFAC Proceedings Volumes

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In this paper, we use optimum control methods to determine optimal budgetary policies for Austria over a five-year horizon. Optimal fiscal policies are calculated using the algorithm OPTCON. Optimal policies are compared to a simulation using extrapolated values of budgetary policy variables in terms of their performance with respect to Austrian macroeconomic targets. Policy tradeoffs between these variables are quantified.

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Opticon: An algorithm for the optimal control of nonlinear stochastic models

Cited by 55 publications

References 4 publications

Solving Nonlinear Stochastic Optimal Control Problems Using Evolutionary Heuristic Optimization

Solving Nonlinear Stochastic Optimal Control Problems Using Evolutionary Heuristic Optimization

Impacts of Family Businesses on Economic Stability and Growth –An Optimization Approach

Tradeoffs of Austrian Budgetary Policies: An Optimum Control Analysis

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