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