If you are using FEAST, please consider citing one or more publications below in your work.
Main Reference
- E. Polizzi,
Density-Matrix-Based Algorithms for Solving Eigenvalue Problems,
Phys. Rev. B. Vol. 79, 115112 (2009) [Article link] [Preprint]
Specific References
- Mathematical analysis and convergence studies
P. T. P. Tang, E. Polizzi,
FEAST as a Subspace Iteration EigenSolver Accelerated by Approximate Spectral Projection,
SIAM Journal on Matrix Analysis and Applications (SIMAX), 35, 354-390 (2014) [Article link][Preprint]
- Non-Hermitian solver
J. Kestyn, E. Polizzi, P. T. P. Tang,
FEAST Eigensolver for Non-Hermitian Problems,
SIAM Journal on Scientific Computing (SISC), 38-5, ppS772-S799 (2016) [Article link][Preprint]
- Hermitian solver using Zolotarev quadrature
S. Güttel, E. Polizzi, P. T. P. Tang, G. Viaud,
Optimized Quadrature Rules and Load Balancing for the FEAST Eigenvalue Solver,
SIAM Journal on Scientific Computing (SISC), 37 (4), pp2100-2122 (2015). [Article link] [Preprint]
- Eigenvalue count using stochastic estimates
E. Di Napoli, E. Polizzi, Y. Saad
Efficient Estimation of Eigenvalue Counts in an Interval,
Numerical Linear Algebra with Applications, V23, I4, pp674-692,(2016). [Article link] [Preprint]
- Polynomial Non-linear eigenvalue problem -- Residual Inverse Iterations
B. Gavin, A. Miedlar, E. Polizzi
FEAST Eigensolver for Nonlinear Eigenvalue Problems
Journal of Computational Science, V. 27, 107, (2018) [Article link] [Preprint]
- IFEAST
B. Gavin, E. Polizzi Krylov eigenvalue strategy using the FEAST algorithm with inexact system solves
Numerical Linear Algebra with Applications, vol 25, number 5, 20 pages (2018). [Article link] [Preprint]
- PFEAST
J. Kesyn, V. Kalantzis, E. Polizzi, Y. Saad
PFEAST: A High Performance Sparse Eigenvalue Solver Using Distributed-Memory Linear Solvers
Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, ACM/IEEE Supercomputing Conference (SC’16), pp 16:1-16:12, (2016). [Article link]