Coordinate-Space Hartree-Fock-Bogoliubov Description of Superfluid Fermi Systems

Properties of strongly interacting, two-component finite Fermi systems are discussed within the recently developed coordinate-space Hartree-Fock-Bogoliubov (HFB) code {\hfbax}. Two illustrative examples are presented: (i) weakly bound deformed Mg isotopes, and (ii) spin-polarized atomic condensates in a strongly deformed harmonic trap.Comment: 4 pages, 2 figures, ENAM 2008 conference proceedings (EPJA

Parallel inverse iteration with reorthogonalization

A parallel method for finding orthogonal eigenvectors of real symmetric tridiagonal is described. The method uses inverse iteration with repeated Modified Gram-Schmidt (MGS) reorthogonalization of the unconverged iterates for clustered eigenvalues. This approach is more parallelizable than reorthogonalizing against fully converged eigenvectors, as is done by LAPACK's current DSTEIN routine. The new method is found to provide accuracy and speed comparable to DSTEIN's and to have good parallel scalability even for matrices with large clusters of eigenvalues. We present al results for residual and orthogonality tests, plus timings on IBM RS/6000 (sequential) and Intel Touchstone DELTA (parallel) computers

Performance of a fully parallel dense real symmetric eigensolver in quantum chemistry applications

The parallel performance of a dense, standard and generalized, real, symmetric eigensolver based on bisection for eigenvalues and repeated inverse iteration and reorthogonalization for eigenvectors is described. The performance of this solver, called PeIGS, is given for two test problems and for three ``real-world`` quantum chemistry applications: SCF-Hartree-Fock, density functional theory,and Moeller-Plesset theory. The distinguishing feature of the repeated inverse iteration and orthogonalization method used by PEIGS is that orthogonalization may be performed across multiple processors as dictated by the spectrum. For each problem we describe the spectrum and the clustering of the eigenvalues, the most important factor in determining the execution time. For a spectrum that is well spaced, there is essentially no orthogonalization time. Most of the time is consumed in the Householder reduction to tridiagonal form. For large clusters, almost all of the time is consumed in the Householder reduction and in orthogonalization. Performance results from the Intel Paragon, and Kendall Square Research KSR-2 are reported

Optimization of waste loading in high-level glass in the presence of uncertainty

Hanford high-level liquid waste will be converted into a glass form for long-term storage. The glass must meet certain constraints on its composition and properties in order to have desired properties for processing (e.g., electrical conductivity, viscosity, and liquidus temperature) and acceptable durability for long-term storage. The Optimal Waste Loading (OWL) models, based on rigorous mathematical optimization techniques, have been developed to minimize the number of glass logs required and determine glass-former compositions that will produce a glass meeting all relevant constraints. There is considerable uncertainty in many of the models and data relevant to the formulation of high-level glass. In this paper, we discuss how we handle uncertainty in the glass property models and in the high-level waste composition to the vitrification process. Glass property constraints used in optimization are inequalities that relate glass property models obtained by regression analysis of experimental data to numerical limits on property values. Therefore, these constraints are subject to uncertainty. The sampling distributions of the regression models are used to describe the uncertainties associated with the constraints. The optimization then accounts for these uncertainties by requiring the constraints to be satisfied within specified confidence limits. The uncertainty in waste composition is handled using stochastic optimization. Given means and standard deviations of component masses in the high-level waste stream, distributions of possible values for each component are generated. A series of optimization runs is performed; the distribution of each waste component is sampled for each run. The resultant distribution of solutions is then statistically summarized. The ability of OWL models to handle these forms of uncertainty make them very useful tools in designing and evaluating high-level waste glasses formulations