This paper presents the development of a hybrid fuzzy modeling method for short-term load forecasting. The new approach employs the orthogonal least squares method to create the fuzzy model and a constrained optimization algorithm to perform the parameter learning. The proposed model is tested...

In recent years, convex optimization methods were successfully applied for various image processing tasks and a large number of first-order methods were designed to minimize the corresponding functionals. Interestingly, it was shown recently in Grewenig et al. (<CitationRef CitationID="CR24">2010</CitationRef>) that the simple idea of...</citationref>

Among the penalty based approaches for constrained optimization, augmented Lagrangian (AL) methods are better in at least three ways: (i) they have theoretical convergence properties, (ii) they distort the original objective function minimally, thereby providing a better function landscape for...

Impulsive control consideres so called interventions meaning immediate change of the state of the system; between intervention, the continuous time jump Markov process is uncontrollable, with “natural” jump intensities. Multicriteria control problem for such model is considered, and the...

A constrained optimization problem is formulated and solved in order to determine the smallest confidence region for the parameters of the Pareto distribution in a proposed family of sets. The objective function is the area of the region, whereas the constraints are related to the required...

We consider a class of law invariant utilities which contains the Rank Dependent Expected Utility (RDU) and the cumulative prospect theory (CPT). We show that the computation of demand for a contingent claim when utilities are within that class, although not as simple as in the Expected Utility...

In this working paper we go on with the study of a mapping in Rn associated to a quadratic optimization problem with one equality linear constraint. After showing some general properties related to homogeneity and the inverse mapping, we present some results regarding how the mapping behaves...

This paper presents a method capable of estimating richly parametrized versions of the dynamic conditional correlation (DCC) model that go beyond the standard scalar case. The algorithm is based on the maximization of a Gaussian quasi-likelihood using a Bregman-proximal trust-region method to...