Resumo em Inglês:

We present an algorithm for the constrained saddle point problem with a convex-concave function L and convex sets with nonempty interior. The method consists of moving away from the current iterate by choosing certain perturbed vectors. The values of gradients of L at these vectors provide an appropriate direction. Bregman functions allow us to define a curve which starts at the current iterate with this direction, and is fully contained in the interior of the feasible set. The next iterate is obtained by moving along such a curve with a certain step size. We establish convergence to a solution with minimal conditions upon the function L, weaker than Lipschitz continuity of the gradient of L, for instance, and including cases where the solution needs not be unique. We also consider the case in which the perturbed vectors are on certain specific curves starting at the current iterate, in which case another convergence proof is provided. In the case of linear programming, we obtain a family of interior point methods where all the iterates and perturbed vectors are computed with very simple formulae, without factorization of matrices or solution of linear systems, which makes the method attractive for very large and sparse matrices. The method may be of interest for massively parallel computing. Numerical examples for the linear programming case are given.

Resumo em Inglês:

Our main concern here is to give a numerical scheme to solve a nonlinear multi-term fractional (arbitrary) orders differential equation. Some results concerning the existence and uniqueness have been also obtained.

Resumo em Inglês:

The convergence of the method of successive approximations is usually studied by the fixed point theorem. An alternative to this theorem is given in this work, where a contraction mapping is not necessary. An application to nonlinear integral equations of Fredholm type and second kind is also presented.

Resumo em Inglês:

Initial and initial-boundary value problems for the Kuramoto-Sivashinsky model of "gas - solid particles" media are considered. Existence, uniqueness and exponential decay of global strong solutions are proved for small initial data.

Resumo em Inglês:

We propose a discrete approximation scheme to a class of Linear Quadratic Continuous Time Problems. It is shown, under positiveness of the matrix in the integral cost, that optimal solutions of the discrete problems provide a sequence of bounded variation functions which converges almost everywhere to the unique optimal solution. Furthermore, the method of discretization allows us to derive a number of interesting results based on finite dimensional optimization theory, namely, Karush-Kuhn-Tucker conditions of optimality and weak and strong duality. A number of examples are provided to illustrate the theory.