Wavelet and Linear Algebra

Abstract The theory of constrained best approximation in Hilbert spaces has been systematically developed well over a decade and effective characterizations of best approximations have been known under some qualifications on the constraints. Yet, the existing theory does not explain how to characterize a best approximation in the face of data uncertainty in the constraints, despite the reality that the data of the constraints are often uncertain (that is, they are not known exactly) due to estimation errors, prediction errors or lack of information. This paper explains when the best approximation over uncertain linear constraints in a real Hilbert space is immunized against bounded data uncertainty. This study is done by characterizing the best approximation of the robust counterpart of the uncertain constrained best approximation problem where the constraints are enforced for all possible uncertainties within the prescribed uncertainty sets. We show that under a new robust strong conical hull intersection property (robust strong CHIP) the same kind of effective characterizations of constrained best approximation hold for the robust best approximation that is immunized against bounded data uncertainty. We also establish a strong duality theorem for the robust constrained best approximation problem and its associated dual problem under the robust strong CHIP. Some examples are given to illustrate the obtained results.

Abstract In this paper, we employ the generalized Guignard's constraint qualification to present the dual cone characterizations of the constraint set $S$ with set valued constraints in $\R^n.$ The obtained results provide sufficient conditions for which the ``strong conical hull intersection property`` (strong CHIP, in short) holds. Moreover, we establish necessary and sufficient conditions for characterizing ``perturbation property`` of the constrained best approximation to any point $x \in \R^{n}$ from a convex set $\tS:=K \cap S$ by the strong CHIP of $K$ and $S$ at a reference point, where $K$ is a non-empty closed convex set in $ \R^{n}.$ Finally, under the generalized Guignard's constraint qualification we derive the Lagrange multipliers characterizations of the constrained best approximation with set valued constraints. The clarification of our results is illustrated by the numerical experiments.

Abstract ‎Many optimization problems can be reduced to a
problem with an increasing and co-radiant objective function by a suitable transformation of variables. Functions, which are increasing and co-radiant, have found many applications in microeconomic analysis. In this paper, the abstract convexity of positive valued affine increasing and co-radiant (ICR) functions are discussed. Moreover, the basic properties of this class of functions such as support set, subdifferential and maximal elements of support set are characterized. Finally, as an application, necessary and sufficient conditions for the global minimum of the difference of two strictly positive valued affine ICR functions are presented.

Abstract In this paper, we give a fundamental convexity preserving for spectral functions. Indeed, we investigate infimal convolution, Moreau envelope and proximal average for convex spectral functions, and show that this properties are inherited from the properties of its corresponding convex function. This results have many applications in Applied Mathematics such as semi-definite programmings and engineering problems.

Abstract In this paper, we first give a characterization of sub-topical functions with respect to their lower level sets and epigraph. Next, by using two different classes of elementary functions, we present a characterization of sub-topical functions with respect to their polar functions, and investigate the relation between polar functions and support sets of this class of functions. Finally, we obtain more results on the polar of sub-topical functions.