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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

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Quasi-Newton parallel geometry optimization methods.

Steven K Burger¹, Paul W Ayers

¹Department of Chemistry, McMaster University, 1280 Main St. West, Hamilton, Ontario L8S 4M1, Canada. burgers@mcmaster.ca

The Journal of Chemical Physics

|July 24, 2010

Summary

This summary is machine-generated.

Parallel algorithms accelerate molecular system minimization. Three methods, including finite difference and Lanczos, show significant speed-ups on multiple processors for molecular simulations.

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Area of Science:

Computational Chemistry
Molecular Modeling
Numerical Analysis

Background:

Unconstrained minimization is crucial for molecular systems.
Quasi-Newton methods require efficient Hessian updates for speed.

Purpose of the Study:

To evaluate parallel algorithms for unconstrained minimization of molecular systems.
To compare different strategies for updating the quasi-Newton Hessian.

Main Methods:

Examined three approaches for updating the quasi-Newton Hessian: finite difference, concurrent minimizations, and the Lanczos method.
Investigated the impact of preconditioning and multiple secant updates.
Tested methods on the Rosenbrock function and molecular systems like histidine dipeptide.

Main Results:

All tested parallel algorithms demonstrated significant speed-up with increasing processor count (up to eight).
The choice of directions for Hessian updates critically impacts performance.
Preconditioning and multiple secant updates are vital for efficiency.

Conclusions:

Parallelization of unconstrained minimization algorithms offers substantial performance gains for molecular systems.
The Lanczos method and finite difference approaches are viable strategies for parallel molecular minimization.
Optimized Hessian updates are key to achieving efficient parallel computations in molecular simulations.