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Improved weights
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@ -31,7 +31,7 @@ subroutine update_pt2_and_variance_weights(pt2, variance, norm, N_st)
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double precision :: avg, rpt2(N_st), element, dt, x
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integer :: k
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dt = 2.d0 * selection_factor
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dt = n_iter * selection_factor
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do k=1,N_st
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rpt2(k) = pt2(k)/(1.d0 + norm(k))
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@ -40,7 +40,6 @@ subroutine update_pt2_and_variance_weights(pt2, variance, norm, N_st)
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avg = sum(rpt2(1:N_st)) / dble(N_st)
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do k=1,N_st
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element = exp(dt*(rpt2(k)/avg -1.d0))
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element = max(0.8d0, element)
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element = min(1.2d0 , element)
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pt2_match_weight(k) *= element
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enddo
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@ -48,12 +47,9 @@ subroutine update_pt2_and_variance_weights(pt2, variance, norm, N_st)
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avg = sum(variance(1:N_st)) / dble(N_st)
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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 = max(0.8d0, element)
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element = min(1.2d0 , element)
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variance_match_weight(k) *= element
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enddo
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print *, "rPT2-weights:", real(pt2_match_weight(:),4)
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print *, "Variance-weights:", real(variance_match_weight(:),4)
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SOFT_TOUCH pt2_match_weight variance_match_weight
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end
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@ -67,21 +63,27 @@ BEGIN_PROVIDER [ double precision, selection_weight, (N_states) ]
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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) = 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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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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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) * variance_match_weight(1:N_states) * pt2_match_weight(1:N_states)
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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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end select
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