3,042 research outputs found
Improving Tatonnement Methods of Solving Heterogeneous Agent Models
This paper modifies standard block Gauss-Seidel iterations used by tatonnement methods for solving large scale deterministic heterogeneous agent models. The composite method between first- and second-order tatonnement methods is shown to considerably improve convergence both in terms of speed as well as robustness relative to conventional first-order tatonnement methods. In addition, the relative advantage of the modified algorithm increases in the size and complexity of the economic model. Therefore, the algorithm allows significant reductions in computational time when solving large models. The algorithm is particularly attractive since it is easy to implement – it only augments conventional and intuitive tatonnement iterations with standard numerical methods.
Improving Tatonnement Methods for Solving Heterogeneous Agent Models
This paper modifies standard block Gauss-Seidel iterations used by tatonnement methods for solving large scale deterministic heterogeneous agent models. The composite method between first- and second-order tatonnement methods is shown to considerably improve convergence both in terms of speed as well as robustness relative to conventional first-order tatonnement methods. In addition, the relative advantage of the modified algorithm increases in the size and complexity of the economic model. Therefore, the algorithm allows significant reductions in computational time when solving large models. The algorithm is particularly attractive since it is easy to implement - it only augments conventional and intuitive tatonnement iterations with standard numerical methods.
Improving Tatonnement Methods for Solving Heterogeneous Agent Models
This paper modifies standard block Gauss-Seidel iterations used by tatonnement methods for solving large scale deterministic heterogeneous agent models. The composite method between first- and second-order tatonnement methods is shown to considerably improve convergence both in terms of speed as well as robustness relative to conventional first-order tatonnement methods. In addition, the relative advantage of the modified algorithm increases in the size and complexity of the economic model. Therefore, the algorithm allows significant reductions in computational time when solving large models. The algorithm is particularly attractive since it is easy to implement - it only augments conventional and intuitive tatonnement iterations with standard numerical methods
Application of A Distributed Nucleus Approximation In Grid Based Minimization of the Kohn-Sham Energy Functional
In the distributed nucleus approximation we represent the singular nucleus as
smeared over a smallportion of a Cartesian grid. Delocalizing the nucleus
allows us to solve the Poisson equation for theoverall electrostatic potential
using a linear scaling multigrid algorithm.This work is done in the context of
minimizing the Kohn-Sham energy functionaldirectly in real space with a
multiscale approach. The efficacy of the approximation is illustrated
bylocating the ground state density of simple one electron atoms and
moleculesand more complicated multiorbital systems.Comment: Submitted to JCP (July 1, 1995 Issue), latex, 27pages, 2figure
Co-simulation of Continuous Systems: A Tutorial
Co-simulation consists of the theory and techniques to enable global
simulation of a coupled system via the composition of simulators. Despite the
large number of applications and growing interest in the challenges, the field
remains fragmented into multiple application domains, with limited sharing of
knowledge.
This tutorial aims at introducing co-simulation of continuous systems,
targeted at researchers new to the field
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