Title: A Powerful Truncated Newton Method for Potential Energy Minimization Classic Reprint
Publisher: Forgotten Books
Publication Date: 2018
Language: English
ISBN 10: 0656021489
ISBN 13: 9780656021482
Binding: HRD
Condition: New
Weight: 278 grams

About this title

Synopsis

Efficient optimization for complex molecular models using truncated Newton methods.

This concise guide explains how truncated Newton algorithms perform on a model potential energy function, comparing several variants and highlighting practical implementation choices. It focuses on large-scale problems where the dominant cost is in solving the Newton system, and it shows how preconditioning and line search strategies can dramatically reduce computation.

The material describes a concrete set of methods tested on a deoxyribose energy model with 39 degrees of freedom. You’ll see how analytic second derivatives compare with finite-difference approaches, and how a flexible code structure supports different potential energy components and modular Hessian assembly. The discussion covers reverse communication for Hessian-vector products and the impact of choosing line-search parameters in practice.

How truncated Newton variants differ in preconditioning and using analytic vs. finite-difference Hessians.
When and why a truncated Newton direction can speed up convergence versus full Newton or nonlinear CG.
How implementation details like line-search backtracking and reverse communication influence performance.
What the results imply for applying these methods to large, indefinite Hessian regions often seen in potential energy problems.

Ideal for readers of computational chemistry and optimization who want practical guidance on method selection, tuning, and interpreting performance metrics in real-world energy minimization tasks.

"About this title" may belong to another edition of this title.

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