A primal algorithm for the weighted minimum covering ball problem in $$\mathbb {R}^n$$

The nonlinear programming problem of finding the minimum covering ball of a finite set of points in \(\mathbb {R}^n\), with a positive weight corresponding to each point, is solved by a directional search method. At each iteration, the search path is either a ray or the arc of a circle and is determined by bisectors of points. Each step size along the search path is determined explicitly. The primal algorithm is shown to search along the farthest point Voronoi diagram of the given points. We provide computational results that show the efficiency of the algorithm when compared to general convex nonlinear optimization solvers.     

The Dirichlet problem for second order elliptic equations with coefficients in the Stummel class in unbounded domains

In this note we prowe existence and unicity of solution of a Dirichlet problem for second order elliptic operator in the divergence form, with the coefficients of the lower order terms belonging to a variant of the Stummel-Kato class, in an unbounded domain, extending the works [6] and [2].

---

Sunto In questa nota proviamo un Teorema di esistenza e unicità per la soluzione di un problema di Dirichlet relativo ad un operatore ellittico del secondo ordine in forma di divergenza, con i coefficienti dei termini di ordine inferiore appartenenti ad una variante dello spazio di Stummel-Kato, in un dominio non limitato, estendendo i lavori [6] e [2].

     

Rounding-based heuristics for nonconvex MINLPs

We propose two primal heuristics for nonconvex mixed-integer nonlinear programs. Both are based on the idea of rounding the solution of a continuous nonlinear program subject to linear constraints. Each rounding step is accomplished through the solution of a mixed-integer linear program. Our heuristics use the same algorithmic scheme, but they differ in the choice of the point to be rounded (which is feasible for nonlinear constraints but possibly fractional) and in the linear constraints. We propose a feasibility heuristic, that aims at finding an initial feasible solution, and an improvement heuristic, whose purpose is to search for an improved solution within the neighborhood of a given point. The neighborhood is defined through local branching cuts or box constraints. Computational results show the effectiveness in practice of these simple ideas, implemented within an open-source solver for nonconvex mixed-integer nonlinear programs.     

Bayesian Nonparametric Estimation for Reinforced Markov Renewal Processes

Starting from the definitions and the properties of reinforced renewal processes and reinforced Markov renewal processes, we characterize, via exchangeability and de Finetti’s representation theorem, a prior that consists of a family of Dirichlet distributions on the space of Markov transition matrices and beta-Stacy processes on distribution functions. Then, we show that this family is conjugate and give some estimate results.

---

     

A new family of heavy transition metal coordination compounds and its application,I. Design,synthesis and characterization of volatile organohafnium precursors

The first three representatives of a new family of volatile organohafnium compounds suitable as metallo-organic chemical vapour deposition precursors were synthesized. A combination of cyclopentadienyl and alkoxo-ligands with a bicyclo[2.2.1]heptanc framework was used. Volatility at relatively low temperatures for hafnium compounds was found and the precursors were characterized by elemental analysis and spectroscopic methods (IR, ¹H and ¹³C NMR, mass spectrometry and mass-analysed ion kinetic energy spectroscopy). The outlook for use in hafnium functional materials synthesis was derived from the fragmentation data.     

A note on Taylor boundary conditions for accurate image restoration

In recent years, several efforts were made in order to introduce boundary conditions for deblurring problems that allow to get accurate reconstructions. This resulted in the birth of Reflective, Anti-Reflective and Mean boundary conditions, which are all based on the idea of guaranteeing the continuity of the signal/image outside the boundary. Here we propose new boundary conditions that are obtained by suitably combining Taylor series and finite difference approximations. Moreover, we show that also Anti-Reflective and Mean boundary conditions can be attributed to the same framework. Numerical results show that, in case of low levels of noise and blurs able to perform a suitable smoothing effect on the original image (e.g. Gaussian blur), the proposed boundary conditions lead to a significant improvement of the restoration accuracy with respect to those available in the literature.