hamiltonian_solver¶
- psi4.driver.p4util.hamiltonian_solver(engine, guess, *, nroot, r_convergence=0.0001, max_ss_size=100, maxiter=60, verbose=1)[source]¶
Finds the smallest eigenvalues and associated right and left hand eigenvectors of a large real Hamiltonian eigenvalue problem emulated through an engine.
A Hamiltonian eigenvalue problem (EVP) has the following structure:
[A B][X] = [1 0](w)[X] [B A][Y] [0 -1](w)[Y]
with A, B of some large dimension N, the problem is of dimension 2Nx2N.
The real, Hamiltonian EVP can be rewritten as the NxN, non-hermitian EVP: \((A+B)(A-B)(X+Y) = w^2(X+Y)\)
With left-hand eigenvectors: \((X-Y)(A-B)(A+B) = w^2(X-Y)\)
if \((A-B)\) is positive definite, we can transform the problem to arrive at the hermitian NxN EVP: \((A-B)^{1/2}(A+B)(A-B)^{1/2} = w^2 T\)
Where \(T = (A-B)^{-1/2}(X+Y)\).
We use a Davidson like iteration where we transform \((A+B)\) (H1) and \((A-B)\) (H2) in to the subspace defined by the trial vectors. The subspace analog of the NxN hermitian EVP is diagonalized and left \((X-Y)\) and right \((X+Y)\) eigenvectors of the NxN non-hermitian EVP are approximated. Residual vectors are formed for both and the guess space is augmented with two correction vectors per iteration. The advantages and properties of this algorithm are described in the literature [stratmann:1998] .
- Parameters:
engine (
Type
[SolverEngine
]) – The engine drive all operations involving data structures that have at least one “large” dimension. SeeSolverEngine
for requirementsguess (
List
) – list {engine dependent} At least nroot initial expansion vectorsnroot (
int
) – Number of roots desiredr_convergence (
float
) – Convergence tolerance for residual vectorsmax_ss_size (
int
) – The maximum number of trial vectors in the iterative subspace that will be stored before a collapse is done.maxiter (
int
) – The maximum number of iterationsverbose (
int
) – The amount of logging info to print (0 -> none, 1 -> some, >1 -> everything)
- Returns:
best_values (numpy.ndarray) – (nroots, ) The best approximation of the eigenvalues of w, computed on the last iteration of the solver
best_R (List[vector]) – (nroots) The best approximation of the right hand eigenvectors, \(X+Y\), computed on the last iteration of the solver.
best_L (List[vector]) – (nroots) The best approximation of the left hand eigenvectors, \(X-Y\), computed on the last iteration of the solver.
stats (List[Dict]) – Statistics collected on each iteration
count : int, iteration number
res_norm : np.ndarray (nroots, ), the norm of residual vector for each roots
val : np.ndarray (nroots, ), the eigenvalue corresponding to each root
delta_val : np.ndarray (nroots, ), the change in eigenvalue from the last iteration to this ones
collapse : bool, if a subspace collapse was performed
product_count : int, the running total of product evaluations that was performed
done : bool, if all roots were converged
Notes
The solution vector is normalized to 1/2
The solver will return even when maxiter iterations are performed without convergence. The caller must check
stats[-1]['done']
for failure and handle each case accordingly.References
R. Eric Stratmann, G. E. Scuseria, and M. J. Frisch, “An efficient implementation of time-dependent density-functional theory for the calculation of excitation energies of large molecules.” J. Chem. Phys., 109, 8218 (1998)