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https://github.com/QuantumPackage/qp2.git
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Moved selection_weight
This commit is contained in:
parent
ebafb1b968
commit
6d33e6ce81
@ -1,148 +1,5 @@
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use bitmasks
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use bitmasks
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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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subroutine get_mask_phase(det1, pm, Nint)
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subroutine get_mask_phase(det1, pm, Nint)
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use bitmasks
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use bitmasks
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implicit none
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implicit none
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150
src/cipsi/selection_weight.irp.f
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150
src/cipsi/selection_weight.irp.f
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@ -0,0 +1,150 @@
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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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@ -505,7 +505,7 @@ subroutine H_S2_u_0_nstates_zmq(v_0,s_0,u_0,N_st,sze)
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print *, irp_here, ': Failed in zmq_set_running'
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print *, irp_here, ': Failed in zmq_set_running'
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endif
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endif
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call omp_set_nested(.True.)
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call omp_set_max_active_levels(4)
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!$OMP PARALLEL DEFAULT(shared) NUM_THREADS(2) PRIVATE(ithread)
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!$OMP PARALLEL DEFAULT(shared) NUM_THREADS(2) PRIVATE(ithread)
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ithread = omp_get_thread_num()
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ithread = omp_get_thread_num()
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if (ithread == 0 ) then
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if (ithread == 0 ) then
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@ -68,7 +68,7 @@ subroutine configuration_to_dets(o,d,sze,n_alpha,Nint)
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integer ,intent(in) :: Nint
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integer ,intent(in) :: Nint
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integer ,intent(in) :: n_alpha ! Number of alpha electrons
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integer ,intent(in) :: n_alpha ! Number of alpha electrons
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integer ,intent(inout) :: sze ! Dimension of the output dets
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integer ,intent(inout) :: sze ! Dimension of the output dets
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integer(bit_kind),intent(in) :: o(Nint,2) ! Occ patters
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integer(bit_kind),intent(in) :: o(Nint,2) ! Configurations
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integer(bit_kind),intent(out) :: d(Nint,2,sze) ! Output determinants
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integer(bit_kind),intent(out) :: d(Nint,2,sze) ! Output determinants
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integer :: i, k, n, ispin, ispin2
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integer :: i, k, n, ispin, ispin2
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150
src/dressing/selection_weight.irp.f
Normal file
150
src/dressing/selection_weight.irp.f
Normal file
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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.
|
||||||
|
END_DOC
|
||||||
|
variance_match_weight(:) = 1.d0
|
||||||
|
END_PROVIDER
|
||||||
|
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||||||
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||||||
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|
||||||
|
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
|
||||||
|
|
||||||
@ -19,7 +19,11 @@ subroutine svd(A,LDA,U,LDU,D,Vt,LDVt,m,n)
|
|||||||
|
|
||||||
double precision,allocatable :: A_tmp(:,:)
|
double precision,allocatable :: A_tmp(:,:)
|
||||||
allocate (A_tmp(LDA,n))
|
allocate (A_tmp(LDA,n))
|
||||||
A_tmp(:,:) = A(:,:)
|
do k=1,n
|
||||||
|
do i=1,m
|
||||||
|
A_tmp(i,k) = A(i,k)
|
||||||
|
enddo
|
||||||
|
enddo
|
||||||
|
|
||||||
! Find optimal size for temp arrays
|
! Find optimal size for temp arrays
|
||||||
allocate(work(1))
|
allocate(work(1))
|
||||||
|
|||||||
Loading…
Reference in New Issue
Block a user