BRST-BFV formalism for the generalized Schwinger model

We derive the Wess-Zumino scalar term of the generalized Schwinger model both in the singular and nonsingular cases by using BRST-BFV framework. The photon propagators are also computed in the extended Lorentz gauge.

A generalized Jaynes-Cummings Hamiltonian and supersymmetric shape invariance

A class of shape-invariant bound-state problems which represent two-level systems are introduced. It is shown that the coupled-channel Hamiltonians obtained correspond to the generalization of the Jaynes-Cummings Hamiltonian.

Self-consistent equilibrium calculation through a direct variational technique in tokamak plasmas

A self-consistent equilibrium calculation, valid for arbitrary aspect ratio tokamaks, is obtained through a direct variational technique that reduces the equilibrium solution, in general obtained from the 2D Grad-Shafranov equation, to a 1D problem in the radial flux coordinate rho. The plasma current profile is supposed to have contributions of the diamagnetic, Pfirsch-Schluter and the neoclassical ohmic and bootstrap currents. An iterative procedure is introduced into our code until the flux surface averaged toroidal current density (J(T)), converges to within a specified tolerance for a given pressure profile and prescribed boundary conditions. The convergence criterion is applied between the (J(T)) profile used to calculate the equilibrium through the variational procedure and the one that results from the equilibrium and given by the sum of all current components. The ohmic contribution is calculated from the neoclassical conductivity and from the self-consistently determined loop voltage in order to give the prescribed value of the total plasma current. The bootstrap current is estimated through the full matrix Hirshman-Sigmar model with the viscosity coefficients as proposed by Shaing, which are valid in all plasma collisionality regimes and arbitrary aspect ratios. The results of the self-consistent calculation are presented for the low aspect ratio tokamak Experimento Tokamak Esferico. A comparison among different models for the bootstrap current estimate is also performed and their possible Limitations to the self-consistent calculation is analysed.

Chain sequences and symmetric generalized orthogonal polynomials

in this paper, we derive an explicit expression for the parameter sequences of a chain sequence in terms of the corresponding orthogonal polynomials and their associated polynomials. We use this to study the orthogonal polynomials K-n((lambda.,M,k)) associated with the probability measure dphi(lambda,M,k;x), which is the Gegenbauer measure of parameter lambda + 1 with two additional mass points at +/-k. When k = 1 we obtain information on the polynomials K-n((lambda.,M)) which are the symmetric Koornwinder polynomials. Monotonicity properties of the zeros of K-n((lambda,M,k)) in relation to M and k are also given. (C) 2002 Elsevier B.V. B.V. All rights reserved.

Supersymmetric variational energies for the confined Coulomb system

The methodology based on the association of the variational method with supersymmetric quantum mechanics is used to evaluate the energy states of the confined hydrogen atom. (C) 2002 Elsevier B.V. B.V. All rights reserved.

ANOMALIES OF VARIATIONAL PHASE-SHIFTS

It is demonstrated, contrary to various claims, that the phase shifts calculated via variational principles involving the Green function may exhibit anomalous behavior. These anomalies may appear in variational principles for the K matrix (Schwinger variational principle) of potential V, for (K-V) (Kohn-type and Newton variational principles), and other variational principles of higher order (Takatsuka-McKoy variational principle).

Supersymmetry, variational method and the Lennard-Jones (12,6) potential

The formalism of supersymmetric quantum mechanics is used to determine trial functions in order to obtain eigenvalues for the Lennard-Jones (12, 6) potential from variational method. The superpotential obtained provides an effective potential which can be directly comparable to the original one.

Lagrangian heuristic for a class of the generalized assignment problems

A Lagrangian based heuristic is proposed for many-to-many assignment problems taking into account capacity limits for task and agents. A modified Lagrangian bound studied earlier by the authors is presented and a greedy heuristic is then applied to get a feasible Lagrangian-based solution. The latter is also used to speed up the subgradient scheme to solve the modified Lagrangian dual problem. A numerical study is presented to demonstrate the efficiency of the proposed approach. (C) 2010 Elsevier Ltd. All rights reserved.

Comparison of Lagrangian Bounds for One Class of Generalized Assignment Problems

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Bayesian estimation of generalized exponential distribution under noninformative priors

The generalized exponential distribution, proposed by Gupta and Kundu (1999), is a good alternative to standard lifetime distributions as exponential, Weibull or gamma. Several authors have considered the problem of Bayesian estimation of the parameters of generalized exponential distribution, assuming independent gamma priors and other informative priors. In this paper, we consider a Bayesian analysis of the generalized exponential distribution by assuming the conventional non-informative prior distributions, as Jeffreys and reference prior, to estimate the parameters. These priors are compared with independent gamma priors for both parameters. The comparison is carried out by examining the frequentist coverage probabilities of Bayesian credible intervals. We shown that maximal data information prior implies in an improper posterior distribution for the parameters of a generalized exponential distribution. It is also shown that the choice of a parameter of interest is very important for the reference prior. The different choices lead to different reference priors in this case. Numerical inference is illustrated for the parameters by considering data set of different sizes and using MCMC (Markov Chain Monte Carlo) methods.