Forward-backward resolvent splitting methods for general mixed variational inequalities.
Foundations of boundedly rational choice and satisficing decisions.
Fourier analysis, linear programming, and densities of distance avoiding sets in
We derive new upper bounds for the densities of measurable sets in which avoid a finite set of prescribed distances. The new bounds come from the solution of a linear programming problem. We apply this method to obtain new upper bounds for measurable sets which avoid the unit distance in dimensions . This gives new lower bounds for the measurable chromatic number in dimensions . We apply it to get a short proof of a variant of a recent result of Bukh which in turn generalizes theorems of Furstenberg,...
Fractional virus epidemic model on financial networks
In this study, we present an epidemic model that characterizes the behavior of a financial network of globally operating stock markets. Since the long time series have a global memory effect, we represent our model by using the fractional calculus. This model operates on a network, where vertices are the stock markets and edges are constructed by the correlation distances. Thereafter, we find an analytical solution to commensurate system and use the well-known differential transform method to obtain...
France Telecom workforce scheduling problem: a challenge
In this paper, we describe the methodology used to tackle France Telecom workforce scheduling problem (the subject of the Roadef Challenge 2007) and we report the results obtained on the different data sets provided for the competition. Since the problem at hand appears to be NP-hard and due to the high dimensions of the instance sets, we use a two-step heuristical approach. We first devise a problem-tailored heuristic that provides good feasible solutions and then we use a meta-heuristic scheme...
Free material optimization.
Frequency planning and ramifications of coloring
This paper surveys frequency assignment problems coming up in planning wireless communication services. It particularly focuses on cellular mobile phone systems such as GSM, a technology that revolutionizes communication. Traditional vertex coloring provides a conceptual framework for the mathematical modeling of many frequency planning problems. This basic form, however, needs various extensions to cover technical and organizational side constraints. Among these ramifications are T-coloring and...
Fritz John's type conditions and associated duality forms in convex non differentiable vector-optimization
Frobenius numbers by lattice point enumeration.
From Eckart and Young approximation to Moreau envelopes and vice versa
In matricial analysis, the theorem of Eckart and Young provides a best approximation of an arbitrary matrix by a matrix of rank at most r. In variational analysis or optimization, the Moreau envelopes are appropriate ways of approximating or regularizing the rank function. We prove here that we can go forwards and backwards between the two procedures, thereby showing that they carry essentially the same information.
From scalar to vector optimization
Initially, second-order necessary optimality conditions and sufficient optimality conditions in terms of Hadamard type derivatives for the unconstrained scalar optimization problem , , are given. These conditions work with arbitrary functions , but they show inconsistency with the classical derivatives. This is a base to pose the question whether the formulated optimality conditions remain true when the “inconsistent” Hadamard derivatives are replaced with the “consistent” Dini derivatives. It...
From the median to the generalized center
Full approximability of a class of problems over power sets.
In this paper results concerning structural and approximability properties of the subclass of NP-Complete Optimization Problems, defined over a lattice are considered. First, various approaches to the concept of Fully Polynomial Approximation Scheme are presented with application to several known problems in the class of NP-Complete Optimization Problems.Secondly, a characterization of full Approximability for the class of Max Subset Problems is introduced.
Full convergence of the proximal point method for quasiconvex functions on Hadamard manifolds
In this paper we propose an extension of the proximal point method to solve minimization problems with quasiconvex objective functions on Hadamard manifolds. To reach this goal, we initially extend the concepts of regular and generalized subgradient from Euclidean spaces to Hadamard manifolds and prove that, in the convex case, these concepts coincide with the classical one. For the minimization problem, assuming that the function is bounded from below, in the quasiconvex and lower semicontinuous...
Full convergence of the proximal point method for quasiconvex functions on Hadamard manifolds
In this paper we propose an extension of the proximal point method to solve minimization problems with quasiconvex objective functions on Hadamard manifolds. To reach this goal, we initially extend the concepts of regular and generalized subgradient from Euclidean spaces to Hadamard manifolds and prove that, in the convex case, these concepts coincide with the classical one. For the minimization problem, assuming that the function is bounded from below, in the quasiconvex and lower semicontinuous...
Full convergence of the proximal point method for quasiconvex functions on Hadamard manifolds
In this paper we propose an extension of the proximal point method to solve minimization problems with quasiconvex objective functions on Hadamard manifolds. To reach this goal, we initially extend the concepts of regular and generalized subgradient from Euclidean spaces to Hadamard manifolds and prove that, in the convex case, these concepts coincide with the classical one. For the minimization problem, assuming that the function is bounded from below, in the quasiconvex and lower semicontinuous...
Full-Newton step infeasible interior-point algorithm for SDO problems
In this paper we propose a primal-dual path-following interior-point algorithm for semidefinite optimization. The algorithm constructs strictly feasible iterates for a sequence of perturbations of the given problem and its dual problem. Each main step of the algorithm consists of a feasibility step and several centering steps. At each iteration, we use only full-Newton step. Moreover, we use a more natural feasibility step, which targets at the -center. The iteration bound of the algorithm coincides...
Funciones penalidad y lagrangianos aumentados.
Por medio de un conjunto de propiedades se caracteriza una amplia familia de funciones que pueden emplearse como penalidad para la resolución numérica de un problema de programación matemática. A partir de ellas se construye un algoritmo de penalizaciones demostrando su convergencia a un punto factible óptimo. Se estudia la situación de los mínimos sin restricciones respecto de la región factible, la monotonía de la sucesión de valores de la función auxiliar y se dan varias cotas de convergencia....
Functional equations in dynamic programming.
Funzioni semiconcave, singolarità e pile di sabbia
La semiconcavità è una nozione che generalizza quella di concavità conservandone la maggior parte delle proprietà ma permettendo di ampliarne le applicazioni. Questa è una rassegna dei punti più salienti della teoria delle funzioni semiconcave, con particolare riguardo allo studio dei loro insiemi singolari. Come applicazione, si discuterà una formula di rappresentazione per la soluzione di un modello dinamico per la materia granulare.