919 research outputs found
Solution of the Skyrme-Hartree-Fock-Bogolyubov equations in the Cartesian deformed harmonic-oscillator basis. (VII) HFODD (v2.49t): a new version of the program
We describe the new version (v2.49t) of the code HFODD which solves the
nuclear Skyrme Hartree-Fock (HF) or Skyrme Hartree-Fock-Bogolyubov (HFB)
problem by using the Cartesian deformed harmonic-oscillator basis. In the new
version, we have implemented the following physics features: (i) the isospin
mixing and projection, (ii) the finite temperature formalism for the HFB and
HF+BCS methods, (iii) the Lipkin translational energy correction method, (iv)
the calculation of the shell correction. A number of specific numerical methods
have also been implemented in order to deal with large-scale multi-constraint
calculations and hardware limitations: (i) the two-basis method for the HFB
method, (ii) the Augmented Lagrangian Method (ALM) for multi-constraint
calculations, (iii) the linear constraint method based on the approximation of
the RPA matrix for multi-constraint calculations, (iv) an interface with the
axial and parity-conserving Skyrme-HFB code HFBTHO, (v) the mixing of the HF or
HFB matrix elements instead of the HF fields. Special care has been paid to
using the code on massively parallel leadership class computers. For this
purpose, the following features are now available with this version: (i) the
Message Passing Interface (MPI) framework, (ii) scalable input data routines,
(iii) multi-threading via OpenMP pragmas, (iv) parallel diagonalization of the
HFB matrix in the simplex breaking case using the ScaLAPACK library. Finally,
several little significant errors of the previous published version were
corrected.Comment: Accepted for publication to Computer Physics Communications. Program
files re-submitted to Comp. Phys. Comm. Program Library after correction of
several minor bug
Solving a Class of LP Problems with a Primal-Dual Logarithmic Barrier Method
Applying a higher order primal-dual logarithmic barrier method for solving large real-life linear programming problems is addressed in this paper. The efficiency of interior point algorithm on these problems is compared with the one of the state-of-the-art simplex code MINOS version 5.3. Based on such experience, a wide class of LP problems is identified for which logarithmic barrier approach seems advantageous over the simplex one. Additionally, some practical rules for model builders are derived that should allow them to create problems that can easily be solved with logarithmic barrier algorithms
Recommended from our members
A solution to the crucial problem of population degeneration in high-dimensional evolutionary optimization
Three popular evolutionary optimization algorithms are tested on high-dimensional benchmark functions. An important phenomenon responsible for many failures - population degeneration - is discovered. That is, through evolution, the population of searching particles degenerates into a subspace of the search space, and the global optimum is exclusive from the subspace. Subsequently, the search will tend to be confined to this subspace and eventually miss the global optimum. Principal components analysis (PCA) is introduced to discover population degeneration and to remedy its adverse effects. The experiment results reveal that an algorithm's efficacy and efficiency are closely related to the population degeneration phenomenon. Guidelines for improving evolutionary algorithms for high-dimensional global optimization are addressed. An application to highly nonlinear hydrological models demonstrates the efficacy of improved evolutionary algorithms in solving complex practical problems. © 2011 IEEE
Advances in design and implementation of optimization software
Developing optimization software that is capable of solving large and complex real-life problems is a huge effort. It is based on a deep knowledge of four areas: theory of optimization algorithms, relevant results of computer science, principles of software engineering, and computer technology. The paper highlights the diverse requirements of optimization software and introduces the ingredients needed to fulfill them. After a review of the hardware/software environment it gives a survey of computationally successful techniques for continuous optimization. It also outlines the perspective offered by parallel computing, and stresses the importance of optimization modeling systems. The inclusion of many references is intended to both give due credit to results in the field of optimization software and help readers obtain more detailed information on issues of interest
- …