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mirror of https://github.com/QuantumPackage/qp2.git synced 2024-12-22 20:34:58 +01:00

Added switch for multiple selection weights, including variance

This commit is contained in:
Anthony Scemama 2019-06-04 11:16:47 +02:00
parent ce0a5f4e70
commit 4a72ca6b12
4 changed files with 72 additions and 11 deletions

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@ -333,13 +333,7 @@ subroutine ZMQ_pt2(E, pt2,relative_error, error, variance, norm, N_in)
pt2(k) = 0.d0 pt2(k) = 0.d0
enddo enddo
! Adjust PT2 weights for next selection call update_pt2_and_variance_weights(pt2, variance, norm)
double precision :: pt2_avg
pt2_avg = sum(pt2) / dble(N_states)
do k=1,N_states
pt2_match_weight(k) *= (pt2(k)/pt2_avg)**2
enddo
SOFT_TOUCH pt2_match_weight
end subroutine end subroutine

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@ -9,12 +9,73 @@ BEGIN_PROVIDER [ double precision, pt2_match_weight, (N_states) ]
pt2_match_weight = 1.d0 pt2_match_weight = 1.d0
END_PROVIDER 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, variance, norm, N_states)
implicit none
BEGIN_DOC
! Updates the rPT2- and Variance- matching weights.
END_DOC
integer, intent(in) :: N_st
double precision, intent(in) :: pt2(N_st)
double precision, intent(in) :: variance(N_st)
double precision, intent(in) :: norm(N_st)
double precision :: avg, rpt2(N_st)
integer :: k
do k=1,N_st
rpt2(k) = pt2(k)/(1.d0 + norm(k))
enddo
avg = sum(rpt2(1:N_st)) / dble(N_st)
do k=1,N_states
pt2_match_weight(k) *= (rpt2(k)/avg)**2
enddo
avg = sum(variance(1:N_st)) / dble(N_st)
do k=1,N_states
variance_match_weight(k) *= (variance(k)/avg)**2
enddo
SOFT_TOUCH pt2_match_weight variance_match_weight
end
BEGIN_PROVIDER [ double precision, selection_weight, (N_states) ] BEGIN_PROVIDER [ double precision, selection_weight, (N_states) ]
implicit none implicit none
BEGIN_DOC BEGIN_DOC
! Weights used in the selection criterion ! Weights used in the selection criterion
END_DOC END_DOC
select (weight_selection)
case (0)
selection_weight(1:N_states) = weight_one_e_dm(1:N_states)
case (1)
selection_weight(1:N_states) = c0_weight(1:N_states)
case (2)
selection_weight(1:N_states) = c0_weight(1:N_states) * pt2_match_weight(1:N_states) selection_weight(1:N_states) = c0_weight(1:N_states) * pt2_match_weight(1:N_states)
case (3)
selection_weight(1:N_states) = c0_weight(1:N_states) * variance_match_weight(1:N_states)
case (4)
selection_weight(1:N_states) = c0_weight(1:N_states) * variance_match_weight(1:N_states) * pt2_match_weight(1:N_states)
case (5)
selection_weight(1:N_states) = c0_weight(1:N_states) * variance_match_weight(1:N_states)
end select
END_PROVIDER END_PROVIDER

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@ -28,12 +28,18 @@ doc: Force the wave function to be an eigenfunction of |S^2|
interface: ezfio,provider,ocaml interface: ezfio,provider,ocaml
default: True default: True
[used_weight] [weight_one_e_dm]
type: integer type: integer
doc: Weight used in the calculation of the one-electron density matrix. 0: 1./(c_0^2), 1: 1/N_states, 2: input state-average weight, 3: 1/(Norm_L3(Psi)) doc: Weight used in the calculation of the one-electron density matrix. 0: 1./(c_0^2), 1: 1/N_states, 2: input state-average weight, 3: 1/(Norm_L3(Psi))
interface: ezfio,provider,ocaml interface: ezfio,provider,ocaml
default: 1 default: 1
[weight_selection]
type: integer
doc: Weight used in the selection. 0: input state-average weight, 1: 1./(c_0^2), 2: rPT2 matching, 3: variance matching, 4: variance and rPT2 matching, 5: variance minimization and matching
interface: ezfio,provider,ocaml
default: 2
[threshold_generators] [threshold_generators]
type: Threshold type: Threshold
doc: Thresholds on generators (fraction of the square of the norm) doc: Thresholds on generators (fraction of the square of the norm)

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@ -305,9 +305,9 @@ BEGIN_PROVIDER [ double precision, state_average_weight, (N_states) ]
logical :: exists logical :: exists
state_average_weight(:) = 1.d0 state_average_weight(:) = 1.d0
if (used_weight == 0) then if (weight_one_e_dm == 0) then
state_average_weight(:) = c0_weight(:) state_average_weight(:) = c0_weight(:)
else if (used_weight == 1) then else if (weight_one_e_dm == 1) then
state_average_weight(:) = 1./N_states state_average_weight(:) = 1./N_states
else else
call ezfio_has_determinants_state_average_weight(exists) call ezfio_has_determinants_state_average_weight(exists)