Otimização global para os problemas de redução H2 de modelos e redução H2 da ordem do controlador

A branch and bound algorithm is proposed to solve the H2-norm model reduction problem and the H2-norm controller reduction problem, with conditions assuring convergence to the global optimum in finite time. The lower and upper bounds used in the optimization procedure are obtained through linear matrix inequalities formulations. Examples illustrate the results.

New limits on doubly charged bileptons from CERN LEP data and the search at future electron-positron and electron-photon colliders

We show that the accumulated CERN LEP-II data taken at √s = 130-206 GeV can establish more restrictive bounds on doubly charged bilepton couplings and masses than any other experiment so far. We also analyze the discovery potential of a prospective linear collider operating in both e+e- and e γ modes.

Final state and thermodynamics of a dark energy universe

As it follows from the classical analysis, the typical final state of a dark energy universe where a dominant energy condition is violated is a finite-time, sudden future singularity (a big rip). For a number of dark energy universes (including scalar phantom and effective phantom theories as well as specific quintessence models) we demonstrate that quantum effects play the dominant role near a big rip, driving the universe out of a future singularity (or, at least, moderating it). As a consequence, the entropy bounds with quantum corrections become well defined near a big rip. Similarly, black hole mass loss due to phantom accretion is not so dramatic as was expected: masses do not vanish to zero due to the transient character of the phantom evolution stage. Some examples of cosmological evolution for a negative, time-dependent equation of state are also considered with the same conclusions. The application of negative entropy (or negative temperature) occurrence in the phantom thermodynamics is briefly discussed.

Global optimization for the ℋ∞-norm model reduction problem

A branch and bound algorithm is proposed to solve the [image omitted]-norm model reduction problem for continuous and discrete-time linear systems, with convergence to the global optimum in a finite time. The lower and upper bounds in the optimization procedure are described by linear matrix inequalities (LMI). Also proposed are two methods with which to reduce the convergence time of the branch and bound algorithm: the first one uses the Hankel singular values as a sufficient condition to stop the algorithm, providing to the method a fast convergence to the global optimum. The second one assumes that the reduced model is in the controllable or observable canonical form. The [image omitted]-norm of the error between the original model and the reduced model is considered. Examples illustrate the application of the proposed method.