Several approaches for subset recovery and improved forecasting accuracy have been proposed and studied. One way is to apply a regularization strategy and solve the model selection task as a continuous optimization problem. One of the most popular approaches in this research field is given by...

This paper discusses the theoretical and practical aspects of new methods for solving DEA problems under real-life geometrical uncertainty and probability uncertainty of sample data. The proposed minimax approach to solve problems with geometrical uncertainty of sample data involves an...

This paper introduces a method for solving numerical dynamic stochastic optimization problems that avoids rootfinding operations. The idea is applicable to many microeconomic and macroeconomic problems, including life cycle, buffer-stock, and stochastic growth problems. Software is provided.

We analyze a dynamic multi-pollutant problem where abatement costs of several pollutants are not separable. The pollutants can be either technological substitutes or complements. Environmental damage is induced by the stock of accumulated pollution. We find that optimal emission paths are...

We consider a class of optimality criteria and show that each crite- rion has its unique and equivalent dual within the class. This property can be used to find a variety of optimal designs, including a class of compound optimal designs and their relationships. As an example, we show that one...

In this paper the problem of designing experiments for a model which is called Monod model and is frequently used in microbiology is studied. The model is defined implicitly by a differential equation and has numerous applications in microbial growth kinetics, environmental research,...

Shear forming is a versatile process for manufacturing complex lightweight components which are required in increasing numbers by many different industries. Inherent advantages of the process are simple tooling, low tool costs, good external and internal surface quality, close dimensional...

Multidimensional scaling is very common in exploratory data analysis. It is mainly used to represent sets of objects with respect to their proximities in a low dimensional Euclidean space. Widely used optimization algorithms try to improve the representation via shifting its coordinates in...

Sei A = {1, . . . , n} eine endliche Menge und f : A -- IR gegeben. Im folgenden Beitrag werden Algorithmen zur Ermittlung der Kleinst-Quadrate Regression g : A -- IR von f beschrieben, wobei g monoton bzgl. einer nicht notwendigerweise vollständigen Präordnung auf A ist. Neben den...