An estimator design problem is considered which involves both L 2 (least squares) and H ~ (worst-case frequency-domain) aspects. Specifically, the goal of the problem is to minimize an L 2 state-estimation error criterion subject to a prespecified H a constraint on the state-estimation error. The H ~ estimation-error constraint is embedded within the optimization process by replacing the covari...

Constraint-based modeling has been used in many application areas of Intelligent Tutoring Systems as a powerful means to analyse erroneous student solutions and generate helpful feedback. In contrast to domains where the structure of the problem under consideration allows a constraint to (almost) uniquely determine the possible cause of a particular student error there are other applications, w...

Recently, several optimization approaches for portfolio selection have been proposed in order to alleviate the estimation error in the optimal portfolio. Among such are the normconstrained variance minimization and the robust portfolio models. In this paper, we examine the role of the norm constraint in the portfolio optimization from several directions. First, it is shown that the norm constra...

The OT error-driven learner is known to admit guarantees of efficiency, stochastic tolerance and noise robustness which hold independently of any substantive assumptions on the constraints. This paper shows that the HG learner instead does not admit such constraint-independent guarantees. The HG theory of error-driven learning thus needs to be substantially restricted to specific constraint sets.

In this research, as the main contribution, a comprehensive study is carried out on the mathematical modeling and analysis of the inverse kinematics and dynamics of an over-constraint three translational degree-of-freedom parallel manipulator. Due to the inconsistency between the number of equations and unknowns, the problem of obtaining the constraint forces and torques of an over-constraint m...

We consider regularization of nonlinear ill-posed problems with constraints which are non-convex. As a special case we consider separable constraints, i.e. the regularization takes place in a sequence space and the constraint acts on each sequence element with a possibly non-convex function. We derive conditions under which such a constraint provides a regularization. Moreover, we derive estima...

A new algorithm for mesh simplification with triangle constriction is presented in this paper. Constricting error defined by a combination of square volume error variation with constraint (SVEC), shape factor and normal constraint factor of triangle. Gauss curvature factor of each constricted triangle is used to distinguish strong feature triangle or nonstrong feature triangle. The triangle whi...

Recently, the concept of a "Proteomic Constraint" was introduced to explain the frequency of genetic code deviations in mitochondrial genomes. The Proteomic Constraint was proposed to be proportional to the size of the mitochondrially encoded proteome, hence small proteomes are expected to experience smaller total numbers of errors resulting from genetic code deviations, leading to less likelih...