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mirror of https://github.com/QuantumPackage/qp2.git synced 2024-11-03 20:13:43 +01:00
qp2/src/dressing/selection_weight.irp.f

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