LCPQ
/
qp2
mirror of https://github.com/QuantumPackage/qp2.git
9
1
Fork 0
Code Releases Activity

Moved selection_weight

This commit is contained in:
Anthony Scemama 2020-12-23 02:42:38 +01:00
parent 61a9c8d29f
commit fa2102c8d7
6 changed files with 307 additions and 146 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

143
src/cipsi/selection.irp.f
View File

@ -1,148 +1,5 @@
use bitmasks
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
subroutine get_mask_phase(det1, pm, Nint)
use bitmasks
implicit none

150
src/cipsi/selection_weight.irp.f Normal file
View File

@ -0,0 +1,150 @@
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

2
src/davidson/davidson_parallel.irp.f
View File

@ -505,7 +505,7 @@ subroutine H_S2_u_0_nstates_zmq(v_0,s_0,u_0,N_st,sze)
print *, irp_here, ': Failed in zmq_set_running'
endif
call omp_set_nested(.True.)
call omp_set_max_active_levels(4)
!$OMP PARALLEL DEFAULT(shared) NUM_THREADS(2) PRIVATE(ithread)
ithread = omp_get_thread_num()
if (ithread == 0 ) then

2
src/determinants/configurations.irp.f
View File

@ -68,7 +68,7 @@ subroutine configuration_to_dets(o,d,sze,n_alpha,Nint)
integer ,intent(in) :: Nint
integer ,intent(in) :: n_alpha ! Number of alpha electrons
integer ,intent(inout) :: sze ! Dimension of the output dets
integer(bit_kind),intent(in) :: o(Nint,2) ! Occ patters
integer(bit_kind),intent(in) :: o(Nint,2) ! Configurations
integer(bit_kind),intent(out) :: d(Nint,2,sze) ! Output determinants
integer :: i, k, n, ispin, ispin2

150
src/dressing/selection_weight.irp.f Normal file
View File

@ -0,0 +1,150 @@
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

6
src/utils/linear_algebra.irp.f
View File

@ -19,7 +19,11 @@ subroutine svd(A,LDA,U,LDU,D,Vt,LDVt,m,n)
double precision,allocatable :: A_tmp(:,:)
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
allocate(work(1))