Eigensolver
Eigensolver
Section SCF::Eigensolver
Type integer
Which eigensolver to use to obtain the lowest eigenvalues and
eigenfunctions of the Kohn-Sham Hamiltonian. The default is
conjugate gradients (cg), except that when parallelization in states is
enabled, the default is rmmdiis.
Options:
- cg:
Conjugate-gradients algorithm.
- plan:
Preconditioned Lanczos scheme. Ref: Y. Saad, A. Stathopoulos, J. Chelikowsky, K. Wu and S. Ogut,
"Solution of Large Eigenvalue Problems in Electronic Structure Calculations", BIT 36, 1 (1996).
- cg_new:
An alternative conjugate-gradients eigensolver, faster for
larger systems but less mature.
Ref: Jiang et al., Phys. Rev. B 68, 165337 (2003)
- evolution:
(Experimental) Propagation in imaginary time.
- rmmdiis:
Residual minimization scheme, direct inversion in the
iterative subspace eigensolver, based on the implementation of
Kresse and Furthmüller [Phys. Rev. B 54, 11169
(1996)]. This eigensolver requires almost no orthogonalization
so it can be considerably faster than the other options for
large systems. To improve its performance a large number of ExtraStates
are required (around 10-20% of the number of occupied states).
Note: with unocc, you will need to stop the calculation
by hand, since the highest states will probably never converge.
Usage with more than one block of states per node is experimental, unfortunately.
- chebyshev_filter:
A Chebyshev-filtered subspace iteration method, which avoids most of the explicit computation of
eigenvectors and results in a significant speedup over iterative diagonalization methods.
This method may be viewed as an approach to solve the original nonlinear Kohn-Sham equation by a
nonlinear subspace iteration technique, without emphasizing the intermediate linearized Kohn-Sham
eigenvalue problems. For further details, see [Zhou et. al.](http://dx.doi.org/10.1016/j.jcp.2014.06.056)