The elementary notion of a function originated in the work of mathematicians of the seventeenth century, and was somewhat closely connected with investigations in the field of algebra. This paper will be concerned with an investigation of a generalized type of continuity known as semi-continuity.

We will study the solution stability of a parametric quasi-variational inequality without the monotonicity assumption of operators. By using the degree theory and the natural map we show that under certain conditions, the solution map of the problem is lower semi-continuous with respect to parameters.

The thesis presents the results of our research on symmetry for some semilinear elliptic problems and on existence of solution for quasilinear problems involving singularities. The text is composed of two parts, each of which begins with a specific introduction. The first part is devoted to symmetry and symmetry-breaking results. We study a class of partial differential equations involving radial weights on balls, annuli or $R^N$ --where these weights are unbounded--. We show in particular that on unbounded domains, focusing on symmetric functions permits to recover compactness, which implies existence of solutions. Then, we stress the fact that symmetry-breaking occurs on bounded domains, depending both on the weights and on the nonlinearity of the equation. We also show that for the considered class of problems, the multibumps-solution phenomenon appears on the annulus as well as on the ball. The second part of the thesis is devoted to partial and ordinary differential equations with singularities. Using concentration-compactness tools, we show that a rather large class of functionals is lower semi-continuous, leading to the existence of a ground state solution. We also focus on the unicity of solutions for such a class of problems.

Submitted by Izabel Franco (izabel-franco@ufscar.br) on 2016-10-10T17:42:51Z No. of bitstreams: 1 DissMBB.pdf: 961528 bytes, checksum: 881a8b78aa15e0ec1fc94c8c87e7e3a3 (MD5) / Approved for entry into archive by Marina Freitas (marinapf@ufscar.br) on 2016-10-20T19:59:42Z (GMT) No. of bitstreams: 1 DissMBB.pdf: 961528 bytes, checksum: 881a8b78aa15e0ec1fc94c8c87e7e3a3 (MD5) / Approved for entry into archive by Marina Freitas (marinapf@ufscar.br) on 2016-10-20T19:59:47Z (GMT) No. of bitstreams: 1 DissMBB.pdf: 961528 bytes, checksum: 881a8b78aa15e0ec1fc94c8c87e7e3a3 (MD5) / Made available in DSpace on 2016-10-20T19:59:53Z (GMT). No. of bitstreams: 1 DissMBB.pdf: 961528 bytes, checksum: 881a8b78aa15e0ec1fc94c8c87e7e3a3 (MD5) Previous issue date: 2016-07-27 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / The mainly purpose of this paper is to study the asymptotic behaviour of abstract evolution equations. The first part of this work is dedicated to the attraction theory for univoque autonomous and nonautonomous problems and for multivoque autonomous problems. After that, we analyse the existence of the appropriate type of attractor for a reaction-diffusion equation (autonomous and with uniqueness property), a variation of the previous equation (which makes it no longer possible to ensure the uniqueness property) and a delayed differential equation (non-autonomous). For the two lasting equations, we also investigate the upper-semicontinuity of the families of the corresponding attractors. / O principal objetivo desta dissertação é estudar o comportamento assintótico de equações de evolução abstratas. A primeira parte do trabalho apresenta e compara, quando possível, a teoria de atração para problemas autônomos e não autônomos unívocos e problemas autônomos multívocos. Após apresentados os resultados, analisamos a existência dos atratores apropriados para uma equação de reação-difusão (autônoma e com unicidade de solucão), uma variação da equação anterior (fazendo com que o problema não tenha mais unicidade de solução) e uma equação diferencial com retardo (não autônoma). Nos dois últimos, investigamos também a semicontinuidade superior para as famílias de atratores correspondentes.

Many complexity measures are defined as the size of a minimal representation in a specific model class. One such complexity measure, which is important because it is widely applied, is statistical complexity. It is defined for discrete-time, stationary stochastic processes within a theory called computational mechanics. Here, a mathematically rigorous, more general version of this theory is presented, and abstract properties of statistical complexity as a function on the space of processes are investigated. In particular, weak-* lower semi-continuity and concavity are shown, and it is argued that these properties should be shared by all sensible complexity measures. Furthermore, a formula for the ergodic decomposition is obtained. The same results are also proven for two other complexity measures that are defined by different model classes, namely process dimension and generative complexity. These two quantities, and also the information theoretic complexity measure called excess entropy, are related to statistical complexity, and this relation is discussed here. It is also shown that computational mechanics can be reformulated in terms of Frank Knight's prediction process, which is of both conceptual and technical interest. In particular, it allows for a unified treatment of different processes and facilitates topological considerations. Continuity of the Markov transition kernel of a discrete version of the prediction process is obtained as a new result.

Many complexity measures are defined as the size of a minimal representation in a specific model class. One such complexity measure, which is important because it is widely applied, is statistical complexity. It is defined for discrete-time, stationary stochastic processes within a theory called computational mechanics. Here, a mathematically rigorous, more general version of this theory is presented, and abstract properties of statistical complexity as a function on the space of processes are investigated. In particular, weak-* lower semi-continuity and concavity are shown, and it is argued that these properties should be shared by all sensible complexity measures. Furthermore, a formula for the ergodic decomposition is obtained. The same results are also proven for two other complexity measures that are defined by different model classes, namely process dimension and generative complexity. These two quantities, and also the information theoretic complexity measure called excess entropy, are related to statistical complexity, and this relation is discussed here. It is also shown that computational mechanics can be reformulated in terms of Frank Knight''s prediction process, which is of both conceptual and technical interest. In particular, it allows for a unified treatment of different processes and facilitates topological considerations. Continuity of the Markov transition kernel of a discrete version of the prediction process is obtained as a new result.