A comparison of eigensolvers for large‐scale 3D modal analysis using AMG‐preconditioned iterative methods

By Peter Arbenz, U. Hetmaniuk, R. Lehoucq, R. Tuminaro. 2005

107 citations recorded by the source corpus.

Subfields: Atomic and Molecular Physics, and Optics, Computational Mechanics, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/bdffb01c5c3c68f5e6219c13f18ecbb868e933a1

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Atomic and Molecular Physics, and Optics

1. Krishnan Suresh
2. M. Takalloozadeh

In Computational Mechanics

1. H. A. Kim
2. Krishnan Suresh
3. Jennifer A. Scott
4. Peter D. Dunning
5. Evgueni E. Ovtchinnikov
6. M. Takalloozadeh
7. Amir M. Mirzendehdel

In Computational Theory and Mathematics

1. Krishnan Suresh
2. Jennifer A. Scott
3. Peter D. Dunning
4. Evgueni E. Ovtchinnikov
5. H. A. Kim
6. M. Takalloozadeh

Citations in the corpus

• Level‐set topology optimization with many linear buckling constraints using an efficient and robust eigensolver (2016, methodology): “We note that the block CG approach has been successfully used by several authors in combination with preconditioning techniques [29, 30].”
• Stress-constrained topology optimization: a topological level-set approach (2013, background): “33 In today’s computational architecture, the primary 34 computational cost in an iterative linear solver is 35 memory-access [50].”