Parallel low-memory quasi-Newton optimization algorithm for molecular structure

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F13%3A00350549" target="_blank" >RIV/68407700:21230/13:00350549 - isvavai.cz</a>
Alternative codes found
RIV/61388963:_____/13:00398455
Result on the web
<a href="https://doi.org/10.1016/j.cplett.2013.08.050" target="_blank" >https://doi.org/10.1016/j.cplett.2013.08.050</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.cplett.2013.08.050" target="_blank" >10.1016/j.cplett.2013.08.050</a>

Result language
angličtina
Original language name
Parallel low-memory quasi-Newton optimization algorithm for molecular structure
Original language description
We present a novel parallel gradient optimization algorithm designed for the optimization of molecular geometry – the parallel preconditioned LBFGS (PP-LBFGS) method. In each step, several additional gradient calculations (performed in parallel with the calculation of the potential) are used to improve the most important elements of the Hessian. The sparsity of the connectivity matrix and the graph theory are used to estimate multiple Hessian elements from each additional gradient calculation. The simplest variant of the algorithm, which requires 4 gradient evaluations per cycle, converges 2x–4x faster than the LBFGS algorithm, depending on the size of the system.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10400 - Chemical sciences

Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2013
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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