mirror of
https://github.com/QuantumPackage/qp2.git
synced 2024-11-03 20:13:43 +01:00
151 lines
4.9 KiB
Fortran
151 lines
4.9 KiB
Fortran
BEGIN_PROVIDER [ double precision, pt2_match_weight, (N_states) ]
|
|
implicit none
|
|
BEGIN_DOC
|
|
! Weights adjusted along the selection to make the PT2 contributions
|
|
! of each state coincide.
|
|
END_DOC
|
|
pt2_match_weight(:) = 1.d0
|
|
END_PROVIDER
|
|
|
|
|
|
|
|
BEGIN_PROVIDER [ double precision, variance_match_weight, (N_states) ]
|
|
implicit none
|
|
BEGIN_DOC
|
|
! Weights adjusted along the selection to make the variances
|
|
! of each state coincide.
|
|
END_DOC
|
|
variance_match_weight(:) = 1.d0
|
|
END_PROVIDER
|
|
|
|
|
|
|
|
subroutine update_pt2_and_variance_weights(pt2_data, N_st)
|
|
implicit none
|
|
use selection_types
|
|
BEGIN_DOC
|
|
! Updates the PT2- and Variance- matching weights.
|
|
END_DOC
|
|
integer, intent(in) :: N_st
|
|
type(pt2_type), intent(in) :: pt2_data
|
|
double precision :: pt2(N_st)
|
|
double precision :: variance(N_st)
|
|
|
|
double precision :: avg, element, dt, x
|
|
integer :: k
|
|
integer, save :: i_iter=0
|
|
integer, parameter :: i_itermax = 1
|
|
double precision, allocatable, save :: memo_variance(:,:), memo_pt2(:,:)
|
|
|
|
pt2(:) = pt2_data % pt2(:)
|
|
variance(:) = pt2_data % variance(:)
|
|
|
|
if (i_iter == 0) then
|
|
allocate(memo_variance(N_st,i_itermax), memo_pt2(N_st,i_itermax))
|
|
memo_pt2(:,:) = 1.d0
|
|
memo_variance(:,:) = 1.d0
|
|
endif
|
|
|
|
i_iter = i_iter+1
|
|
if (i_iter > i_itermax) then
|
|
i_iter = 1
|
|
endif
|
|
|
|
dt = 2.0d0
|
|
|
|
avg = sum(pt2(1:N_st)) / dble(N_st) - 1.d-32 ! Avoid future division by zero
|
|
do k=1,N_st
|
|
element = exp(dt*(pt2(k)/avg -1.d0))
|
|
element = min(2.0d0 , element)
|
|
element = max(0.5d0 , element)
|
|
memo_pt2(k,i_iter) = element
|
|
pt2_match_weight(k) *= product(memo_pt2(k,:))
|
|
enddo
|
|
|
|
|
|
avg = sum(variance(1:N_st)) / dble(N_st) + 1.d-32 ! Avoid future division by zero
|
|
do k=1,N_st
|
|
element = exp(dt*(variance(k)/avg -1.d0))
|
|
element = min(2.0d0 , element)
|
|
element = max(0.5d0 , element)
|
|
memo_variance(k,i_iter) = element
|
|
variance_match_weight(k) *= product(memo_variance(k,:))
|
|
enddo
|
|
|
|
if (N_det < 100) then
|
|
! For tiny wave functions, weights are 1.d0
|
|
pt2_match_weight(:) = 1.d0
|
|
variance_match_weight(:) = 1.d0
|
|
endif
|
|
|
|
threshold_davidson_pt2 = min(1.d-6, &
|
|
max(threshold_davidson, 1.e-1 * PT2_relative_error * minval(abs(pt2(1:N_states)))) )
|
|
|
|
SOFT_TOUCH pt2_match_weight variance_match_weight threshold_davidson_pt2
|
|
end
|
|
|
|
|
|
|
|
|
|
BEGIN_PROVIDER [ double precision, selection_weight, (N_states) ]
|
|
implicit none
|
|
BEGIN_DOC
|
|
! Weights used in the selection criterion
|
|
END_DOC
|
|
select case (weight_selection)
|
|
|
|
case (0)
|
|
print *, 'Using input weights in selection'
|
|
selection_weight(1:N_states) = c0_weight(1:N_states) * state_average_weight(1:N_states)
|
|
|
|
case (1)
|
|
print *, 'Using 1/c_max^2 weight in selection'
|
|
selection_weight(1:N_states) = c0_weight(1:N_states)
|
|
|
|
case (2)
|
|
print *, 'Using pt2-matching weight in selection'
|
|
selection_weight(1:N_states) = c0_weight(1:N_states) * pt2_match_weight(1:N_states)
|
|
print *, '# PT2 weight ', real(pt2_match_weight(:),4)
|
|
|
|
case (3)
|
|
print *, 'Using variance-matching weight in selection'
|
|
selection_weight(1:N_states) = c0_weight(1:N_states) * variance_match_weight(1:N_states)
|
|
print *, '# var weight ', real(variance_match_weight(:),4)
|
|
|
|
case (4)
|
|
print *, 'Using variance- and pt2-matching weights in selection'
|
|
selection_weight(1:N_states) = c0_weight(1:N_states) * sqrt(variance_match_weight(1:N_states) * pt2_match_weight(1:N_states))
|
|
print *, '# PT2 weight ', real(pt2_match_weight(:),4)
|
|
print *, '# var weight ', real(variance_match_weight(:),4)
|
|
|
|
case (5)
|
|
print *, 'Using variance-matching weight in selection'
|
|
selection_weight(1:N_states) = c0_weight(1:N_states) * variance_match_weight(1:N_states)
|
|
print *, '# var weight ', real(variance_match_weight(:),4)
|
|
|
|
case (6)
|
|
print *, 'Using CI coefficient-based selection'
|
|
selection_weight(1:N_states) = c0_weight(1:N_states)
|
|
|
|
case (7)
|
|
print *, 'Input weights multiplied by variance- and pt2-matching'
|
|
selection_weight(1:N_states) = c0_weight(1:N_states) * sqrt(variance_match_weight(1:N_states) * pt2_match_weight(1:N_states)) * state_average_weight(1:N_states)
|
|
print *, '# PT2 weight ', real(pt2_match_weight(:),4)
|
|
print *, '# var weight ', real(variance_match_weight(:),4)
|
|
|
|
case (8)
|
|
print *, 'Input weights multiplied by pt2-matching'
|
|
selection_weight(1:N_states) = c0_weight(1:N_states) * pt2_match_weight(1:N_states) * state_average_weight(1:N_states)
|
|
print *, '# PT2 weight ', real(pt2_match_weight(:),4)
|
|
|
|
case (9)
|
|
print *, 'Input weights multiplied by variance-matching'
|
|
selection_weight(1:N_states) = c0_weight(1:N_states) * variance_match_weight(1:N_states) * state_average_weight(1:N_states)
|
|
print *, '# var weight ', real(variance_match_weight(:),4)
|
|
|
|
end select
|
|
print *, '# Total weight ', real(selection_weight(:),4)
|
|
|
|
END_PROVIDER
|
|
|