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Yann Damour 2022-11-05 13:48:17 +01:00
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248
src/utils_trust_region/algo_trust.irp.f Normal file
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! Algorithm for the trust region
! step_in_trust_region:
! Computes the step in the trust region (delta)
! (automatically sets at the iteration 0 and which evolves during the
! process in function of the evolution of rho). The step is computing by
! constraining its norm with a lagrange multiplier.
! Since the calculation of the step is based on the Newton method, an
! estimation of the gain in energy is given using the Taylors series
! truncated at the second order (criterion_model).
! If (DABS(criterion-criterion_model) < 1d-12) then
! must_exit = .True.
! else
! must_exit = .False.
! This estimation of the gain in energy is used by
! is_step_cancel_trust_region to say if the step is accepted or cancelled.
! If the step must be cancelled, the calculation restart from the same
! hessian and gradient and recomputes the step but in a smaller trust
! region and so on until the step is accepted. If the step is accepted
! the hessian and the gradient are recomputed to produce a new step.
! Example:
! !### Initialization ###
! delta = 0d0
! nb_iter = 0 ! Must start at 0 !!!
! rho = 0.5d0
! not_converged = .True.
!
! ! ### TODO ###
! ! Compute the criterion before the loop
! call #your_criterion(prev_criterion)
!
! do while (not_converged)
! ! ### TODO ##
! ! Call your gradient
! ! Call you hessian
! call #your_gradient(v_grad) (1D array)
! call #your_hessian(H) (2D array)
!
! ! ### TODO ###
! ! Diagonalization of the hessian
! call diagonalization_hessian(n,H,e_val,w)
!
! cancel_step = .True. ! To enter in the loop just after
! ! Loop to Reduce the trust radius until the criterion decreases and rho >= thresh_rho
! do while (cancel_step)
!
! ! Hessian,gradient,Criterion -> x
! call trust_region_step_w_expected_e(tmp_n,W,e_val,v_grad,prev_criterion,rho,nb_iter,delta,criterion_model,tmp_x,must_exit)
!
! if (must_exit) then
! ! ### Message ###
! ! if step_in_trust_region sets must_exit on true for numerical reasons
! print*,'algo_trust1 sends the message : Exit'
! !### exit ###
! endif
!
! !### TODO ###
! ! Compute x -> m_x
! ! Compute m_x -> R
! ! Apply R and keep the previous MOs...
! ! Update/touch
! ! Compute the new criterion/energy -> criterion
!
! call #your_routine_1D_to_2D_antisymmetric_array(x,m_x)
! call #your_routine_2D_antisymmetric_array_to_rotation_matrix(m_x,R)
! call #your_routine_to_apply_the_rotation_matrix(R,prev_mos)
!
! TOUCH #your_variables
!
! call #your_criterion(criterion)
!
! ! Criterion -> step accepted or rejected
! call trust_region_is_step_cancelled(nb_iter,prev_criterion, criterion, criterion_model,rho,cancel_step)
!
! ! ### TODO ###
! !if (cancel_step) then
! ! Cancel the previous step (mo_coef = prev_mos if you keep them...)
! !endif
! #if (cancel_step) then
! #mo_coef = prev_mos
! #endif
!
! enddo
!
! !call save_mos() !### depend of the time for 1 iteration
!
! ! To exit the external loop if must_exit = .True.
! if (must_exit) then
! !### exit ###
! endif
!
! ! Step accepted, nb iteration + 1
! nb_iter = nb_iter + 1
!
! ! ### TODO ###
! !if (###Conditions###) then
! ! no_converged = .False.
! !endif
! #if (#your_conditions) then
! # not_converged = .False.
! #endif
!
! enddo
! Variables:
! Input:
! | n | integer | m*(m-1)/2 |
! | m | integer | number of mo in the mo_class |
! | H(n,n) | double precision | Hessian |
! | v_grad(n) | double precision | Gradient |
! | W(n,n) | double precision | Eigenvectors of the hessian |
! | e_val(n) | double precision | Eigenvalues of the hessian |
! | criterion | double precision | Actual criterion |
! | prev_criterion | double precision | Value of the criterion before the first iteration/after the previous iteration |
! | rho | double precision | Given by is_step_cancel_trus_region |
! | | | Agreement between the real function and the Taylor series (2nd order) |
! | nb_iter | integer | Actual number of iterations |
! Input/output:
! | delta | double precision | Radius of the trust region |
! Output:
! | criterion_model | double precision | Predicted criterion after the rotation |
! | x(n) | double precision | Step |
! | must_exit | logical | If the program must exit the loop |
subroutine trust_region_step_w_expected_e(n,H,W,e_val,v_grad,prev_criterion,rho,nb_iter,delta,criterion_model,x,must_exit)
include 'pi.h'
!BEGIN_DOC
! Compute the step and the expected criterion/energy after the step
!END_DOC
implicit none
! in
integer, intent(in) :: n, nb_iter
double precision, intent(in) :: H(n,n), W(n,n), v_grad(n)
double precision, intent(in) :: rho, prev_criterion
! inout
double precision, intent(inout) :: delta, e_val(n)
! out
double precision, intent(out) :: criterion_model, x(n)
logical, intent(out) :: must_exit
! internal
integer :: info
must_exit = .False.
call trust_region_step(n,nb_iter,v_grad,rho,e_val,W,x,delta)
call trust_region_expected_e(n,v_grad,H,x,prev_criterion,criterion_model)
! exit if DABS(prev_criterion - criterion_model) < 1d-12
if (DABS(prev_criterion - criterion_model) < thresh_model) then
print*,''
print*,'###############################################################################'
print*,'DABS(prev_criterion - criterion_model) <', thresh_model, 'stop the trust region'
print*,'###############################################################################'
print*,''
must_exit = .True.
endif
if (delta < thresh_delta) then
print*,''
print*,'##############################################'
print*,'Delta <', thresh_delta, 'stop the trust region'
print*,'##############################################'
print*,''
must_exit = .True.
endif
! Add after the call to this subroutine, a statement:
! "if (must_exit) then
! exit
! endif"
! in order to exit the optimization loop
end subroutine
! Variables:
! Input:
! | nb_iter | integer | actual number of iterations |
! | prev_criterion | double precision | criterion before the application of the step x |
! | criterion | double precision | criterion after the application of the step x |
! | criterion_model | double precision | predicted criterion after the application of x |
! Output:
! | rho | double precision | Agreement between the predicted criterion and the real new criterion |
! | cancel_step | logical | If the step must be cancelled |
subroutine trust_region_is_step_cancelled(nb_iter,prev_criterion, criterion, criterion_model,rho,cancel_step)
include 'pi.h'
!BEGIN_DOC
! Compute if the step should be cancelled
!END_DOC
implicit none
! in
double precision, intent(in) :: prev_criterion, criterion, criterion_model
! inout
integer, intent(inout) :: nb_iter
! out
logical, intent(out) :: cancel_step
double precision, intent(out) :: rho
! Computes rho
call trust_region_rho(prev_criterion,criterion,criterion_model,rho)
if (nb_iter == 0) then
nb_iter = 1 ! in order to enable the change of delta if the first iteration is cancelled
endif
! If rho < thresh_rho -> give something in output to cancel the step
if (rho >= thresh_rho) then !0.1d0) then
! The step is accepted
cancel_step = .False.
else
! The step is rejected
cancel_step = .True.
print*, '***********************'
print*, 'Step cancel : rho <', thresh_rho
print*, '***********************'
endif
end subroutine

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! Apply MO rotation
! Subroutine to apply the rotation matrix to the coefficients of the
! MOs.
! New MOs = Old MOs . Rotation matrix
! *Compute the new MOs with the previous MOs and a rotation matrix*
! Provided:
! | mo_num | integer | number of MOs |
! | ao_num | integer | number of AOs |
! | mo_coef(ao_num,mo_num) | double precision | coefficients of the MOs |
! Intent in:
! | R(mo_num,mo_num) | double precision | rotation matrix |
! Intent out:
! | prev_mos(ao_num,mo_num) | double precision | MOs before the rotation |
! Internal:
! | new_mos(ao_num,mo_num) | double precision | MOs after the rotation |
! | i,j | integer | indexes |
subroutine apply_mo_rotation(R,prev_mos)
include 'pi.h'
!BEGIN_DOC
! Compute the new MOs knowing the rotation matrix
!END_DOC
implicit none
! Variables
! in
double precision, intent(in) :: R(mo_num,mo_num)
! out
double precision, intent(out) :: prev_mos(ao_num,mo_num)
! internal
double precision, allocatable :: new_mos(:,:)
integer :: i,j
double precision :: t1,t2,t3
print*,''
print*,'---apply_mo_rotation---'
call wall_time(t1)
! Allocation
allocate(new_mos(ao_num,mo_num))
! Calculation
! Product of old MOs (mo_coef) by Rotation matrix (R)
call dgemm('N','N',ao_num,mo_num,mo_num,1d0,mo_coef,size(mo_coef,1),R,size(R,1),0d0,new_mos,size(new_mos,1))
prev_mos = mo_coef
mo_coef = new_mos
if (debug) then
print*,'New mo_coef : '
do i = 1, mo_num
write(*,'(100(F10.5))') mo_coef(i,:)
enddo
endif
! Save the new MOs and change the label
mo_label = 'MCSCF'
!call save_mos
call ezfio_set_determinants_mo_label(mo_label)
!print*,'Done, MOs saved'
! Deallocation, end
deallocate(new_mos)
call wall_time(t2)
t3 = t2 - t1
print*,'Time in apply mo rotation:', t3
print*,'---End apply_mo_rotation---'
end subroutine

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src/utils_trust_region/rotation_matrix.irp.f Normal file
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! Rotation matrix
! *Build a rotation matrix from an antisymmetric matrix*
! Compute a rotation matrix $\textbf{R}$ from an antisymmetric matrix $$\textbf{A}$$ such as :
! $$
! \textbf{R}=\exp(\textbf{A})
! $$
! So :
! \begin{align*}
! \textbf{R}=& \exp(\textbf{A}) \\
! =& \sum_k^{\infty} \frac{1}{k!}\textbf{A}^k \\
! =& \textbf{W} \cdot \cos(\tau) \cdot \textbf{W}^{\dagger} + \textbf{W} \cdot \tau^{-1} \cdot \sin(\tau) \cdot \textbf{W}^{\dagger} \cdot \textbf{A}
! \end{align*}
! With :
! $\textbf{W}$ : eigenvectors of $\textbf{A}^2$
! $\tau$ : $\sqrt{-x}$
! $x$ : eigenvalues of $\textbf{A}^2$
! Input:
! | A(n,n) | double precision | antisymmetric matrix |
! | n | integer | number of columns of the A matrix |
! | LDA | integer | specifies the leading dimension of A, must be at least max(1,n) |
! | LDR | integer | specifies the leading dimension of R, must be at least max(1,n) |
! Output:
! | R(n,n) | double precision | Rotation matrix |
! | info | integer | if info = 0, the execution is successful |
! | | | if info = k, the k-th parameter has an illegal value |
! | | | if info = -k, the algorithm failed |
! Internal:
! | B(n,n) | double precision | B = A.A |
! | work(lwork,n) | double precision | work matrix for dysev, dimension max(1,lwork) |
! | lwork | integer | dimension of the syev work array >= max(1, 3n-1) |
! | W(n,n) | double precision | eigenvectors of B |
! | e_val(n) | double precision | eigenvalues of B |
! | m_diag(n,n) | double precision | diagonal matrix with the eigenvalues of B |
! | cos_tau(n,n) | double precision | diagonal matrix with cos(tau) values |
! | sin_tau(n,n) | double precision | diagonal matrix with sin cos(tau) values |
! | tau_m1(n,n) | double precision | diagonal matrix with (tau)^-1 values |
! | part_1(n,n) | double precision | matrix W.cos_tau.W^t |
! | part_1a(n,n) | double precision | matrix cos_tau.W^t |
! | part_2(n,n) | double precision | matrix W.tau_m1.sin_tau.W^t.A |
! | part_2a(n,n) | double precision | matrix W^t.A |
! | part_2b(n,n) | double precision | matrix sin_tau.W^t.A |
! | part_2c(n,n) | double precision | matrix tau_m1.sin_tau.W^t.A |
! | RR_t(n,n) | double precision | R.R^t must be equal to the identity<=> R.R^t-1=0 <=> norm = 0 |
! | norm | integer | norm of R.R^t-1, must be equal to 0 |
! | i,j | integer | indexes |
! Functions:
! | dnrm2 | double precision | Lapack function, compute the norm of a matrix |
! | disnan | logical | Lapack function, check if an element is NaN |
subroutine rotation_matrix(A,LDA,R,LDR,n,info,enforce_step_cancellation)
implicit none
!BEGIN_DOC
! Rotation matrix to rotate the molecular orbitals.
! If the rotation is too large the transformation is not unitary and must be cancelled.
!END_DOC
include 'pi.h'
! Variables
! in
integer, intent(in) :: n,LDA,LDR
double precision, intent(inout) :: A(LDA,n)
! out
double precision, intent(out) :: R(LDR,n)
integer, intent(out) :: info
logical, intent(out) :: enforce_step_cancellation
! internal
double precision, allocatable :: B(:,:)
double precision, allocatable :: work(:,:)
double precision, allocatable :: W(:,:), e_val(:)
double precision, allocatable :: m_diag(:,:),cos_tau(:,:),sin_tau(:,:),tau_m1(:,:)
double precision, allocatable :: part_1(:,:),part_1a(:,:)
double precision, allocatable :: part_2(:,:),part_2a(:,:),part_2b(:,:),part_2c(:,:)
double precision, allocatable :: RR_t(:,:)
integer :: i,j
integer :: info2, lwork ! for dsyev
double precision :: norm, max_elem, max_elem_A, t1,t2,t3
! function
double precision :: dnrm2
logical :: disnan
print*,''
print*,'---rotation_matrix---'
call wall_time(t1)
! Allocation
allocate(B(n,n))
allocate(m_diag(n,n),cos_tau(n,n),sin_tau(n,n),tau_m1(n,n))
allocate(W(n,n),e_val(n))
allocate(part_1(n,n),part_1a(n,n))
allocate(part_2(n,n),part_2a(n,n),part_2b(n,n),part_2c(n,n))
allocate(RR_t(n,n))
! Pre-conditions
! Initialization
info=0
enforce_step_cancellation = .False.
! Size of matrix A must be at least 1 by 1
if (n<1) then
info = 3
print*, 'WARNING: invalid parameter 5'
print*, 'n<1'
return
endif
! Leading dimension of A must be >= n
if (LDA < n) then
info = 25
print*, 'WARNING: invalid parameter 2 or 5'
print*, 'LDA < n'
return
endif
! Leading dimension of A must be >= n
if (LDR < n) then
info = 4
print*, 'WARNING: invalid parameter 4'
print*, 'LDR < n'
return
endif
! Matrix elements of A must by non-NaN
do j = 1, n
do i = 1, n
if (disnan(A(i,j))) then
info=1
print*, 'WARNING: invalid parameter 1'
print*, 'NaN element in A matrix'
return
endif
enddo
enddo
do i = 1, n
if (A(i,i) /= 0d0) then
print*, 'WARNING: matrix A is not antisymmetric'
print*, 'Non 0 element on the diagonal', i, A(i,i)
call ABORT
endif
enddo
do j = 1, n
do i = 1, n
if (A(i,j)+A(j,i)>1d-16) then
print*, 'WANRING: matrix A is not antisymmetric'
print*, 'A(i,j) /= - A(j,i):', i,j,A(i,j), A(j,i)
print*, 'diff:', A(i,j)+A(j,i)
call ABORT
endif
enddo
enddo
! Fix for too big elements ! bad idea better to cancel if the error is too big
!do j = 1, n
! do i = 1, n
! A(i,j) = mod(A(i,j),2d0*pi)
! if (dabs(A(i,j)) > pi) then
! A(i,j) = 0d0
! endif
! enddo
!enddo
max_elem_A = 0d0
do j = 1, n
do i = 1, n
if (ABS(A(i,j)) > ABS(max_elem_A)) then
max_elem_A = A(i,j)
endif
enddo
enddo
print*,'max element in A', max_elem_A
if (ABS(max_elem_A) > 2 * pi) then
print*,''
print*,'WARNING: ABS(max_elem_A) > 2 pi '
print*,''
endif
! B=A.A
! - Calculation of the matrix $\textbf{B} = \textbf{A}^2$
! - Diagonalization of $\textbf{B}$
! W, the eigenvectors
! e_val, the eigenvalues
! Compute B=A.A
call dgemm('N','N',n,n,n,1d0,A,size(A,1),A,size(A,1),0d0,B,size(B,1))
! Copy B in W, diagonalization will put the eigenvectors in W
W=B
! Diagonalization of B
! Eigenvalues -> e_val
! Eigenvectors -> W
lwork = 3*n-1
allocate(work(lwork,n))
print*,'Starting diagonalization ...'
call dsyev('V','U',n,W,size(W,1),e_val,work,lwork,info2)
deallocate(work)
if (info2 == 0) then
print*, 'Diagonalization : Done'
elseif (info2 < 0) then
print*, 'WARNING: error in the diagonalization'
print*, 'Illegal value of the ', info2,'-th parameter'
else
print*, "WARNING: Diagonalization failed to converge"
endif
! Tau^-1, cos(tau), sin(tau)
! $$\tau = \sqrt{-x}$$
! - Calculation of $\cos(\tau)$ $\Leftrightarrow$ $\cos(\sqrt{-x})$
! - Calculation of $\sin(\tau)$ $\Leftrightarrow$ $\sin(\sqrt{-x})$
! - Calculation of $\tau^{-1}$ $\Leftrightarrow$ $(\sqrt{-x})^{-1}$
! These matrices are diagonals
! Diagonal matrix m_diag
do j = 1, n
if (e_val(j) >= -1d-12) then !0.d0) then !!! e_avl(i) must be < -1d-12 to avoid numerical problems
e_val(j) = 0.d0
else
e_val(j) = - e_val(j)
endif
enddo
m_diag = 0.d0
do i = 1, n
m_diag(i,i) = e_val(i)
enddo
! cos_tau
do j = 1, n
do i = 1, n
if (i==j) then
cos_tau(i,j) = dcos(dsqrt(e_val(i)))
else
cos_tau(i,j) = 0d0
endif
enddo
enddo
! sin_tau
do j = 1, n
do i = 1, n
if (i==j) then
sin_tau(i,j) = dsin(dsqrt(e_val(i)))
else
sin_tau(i,j) = 0d0
endif
enddo
enddo
! Debug, display the cos_tau and sin_tau matrix
!if (debug) then
! print*, 'cos_tau'
! do i = 1, n
! print*, cos_tau(i,:)
! enddo
! print*, 'sin_tau'
! do i = 1, n
! print*, sin_tau(i,:)
! enddo
!endif
! tau^-1
do j = 1, n
do i = 1, n
if ((i==j) .and. (e_val(i) > 1d-16)) then!0d0)) then !!! Convergence problem can come from here if the threshold is too big/small
tau_m1(i,j) = 1d0/(dsqrt(e_val(i)))
else
tau_m1(i,j) = 0d0
endif
enddo
enddo
max_elem = 0d0
do i = 1, n
if (ABS(tau_m1(i,i)) > ABS(max_elem)) then
max_elem = tau_m1(i,i)
endif
enddo
print*,'max elem tau^-1:', max_elem
! Debug
!print*,'eigenvalues:'
!do i = 1, n
! print*, e_val(i)
!enddo
!Debug, display tau^-1
!if (debug) then
! print*, 'tau^-1'
! do i = 1, n
! print*,tau_m1(i,:)
! enddo
!endif
! Rotation matrix
! \begin{align*}
! \textbf{R} = \textbf{W} \cos(\tau) \textbf{W}^{\dagger} + \textbf{W} \tau^{-1} \sin(\tau) \textbf{W}^{\dagger} \textbf{A}
! \end{align*}
! \begin{align*}
! \textbf{Part1} = \textbf{W} \cos(\tau) \textbf{W}^{\dagger}
! \end{align*}
! \begin{align*}
! \textbf{Part2} = \textbf{W} \tau^{-1} \sin(\tau) \textbf{W}^{\dagger} \textbf{A}
! \end{align*}
! First:
! part_1 = dgemm(W, dgemm(cos_tau, W^t))
! part_1a = dgemm(cos_tau, W^t)
! part_1 = dgemm(W, part_1a)
! And:
! part_2= dgemm(W, dgemm(tau_m1, dgemm(sin_tau, dgemm(W^t, A))))
! part_2a = dgemm(W^t, A)
! part_2b = dgemm(sin_tau, part_2a)
! part_2c = dgemm(tau_m1, part_2b)
! part_2 = dgemm(W, part_2c)
! Finally:
! Rotation matrix, R = part_1+part_2
! If $R$ is a rotation matrix:
! $R.R^T=R^T.R=\textbf{1}$
! part_1
call dgemm('N','T',n,n,n,1d0,cos_tau,size(cos_tau,1),W,size(W,1),0d0,part_1a,size(part_1a,1))
call dgemm('N','N',n,n,n,1d0,W,size(W,1),part_1a,size(part_1a,1),0d0,part_1,size(part_1,1))
! part_2
call dgemm('T','N',n,n,n,1d0,W,size(W,1),A,size(A,1),0d0,part_2a,size(part_2a,1))
call dgemm('N','N',n,n,n,1d0,sin_tau,size(sin_tau,1),part_2a,size(part_2a,1),0d0,part_2b,size(part_2b,1))
call dgemm('N','N',n,n,n,1d0,tau_m1,size(tau_m1,1),part_2b,size(part_2b,1),0d0,part_2c,size(part_2c,1))
call dgemm('N','N',n,n,n,1d0,W,size(W,1),part_2c,size(part_2c,1),0d0,part_2,size(part_2,1))
! Rotation matrix R
R = part_1 + part_2
! Matrix check
! R.R^t and R^t.R must be equal to identity matrix
do j = 1, n
do i=1,n
if (i==j) then
RR_t(i,j) = 1d0
else
RR_t(i,j) = 0d0
endif
enddo
enddo
call dgemm('N','T',n,n,n,1d0,R,size(R,1),R,size(R,1),-1d0,RR_t,size(RR_t,1))
norm = dnrm2(n*n,RR_t,1)
print*, 'Rotation matrix check, norm R.R^T = ', norm
! Debug
!if (debug) then
! print*, 'RR_t'
! do i = 1, n
! print*, RR_t(i,:)
! enddo
!endif
! Post conditions
! Check if R.R^T=1
max_elem = 0d0
do j = 1, n
do i = 1, n
if (ABS(RR_t(i,j)) > ABS(max_elem)) then
max_elem = RR_t(i,j)
endif
enddo
enddo
print*, 'Max error in R.R^T:', max_elem
print*, 'e_val(1):', e_val(1)
print*, 'e_val(n):', e_val(n)
print*, 'max elem in A:', max_elem_A
if (ABS(max_elem) > 1d-12) then
print*, 'WARNING: max error in R.R^T > 1d-12'
print*, 'Enforce the step cancellation'
enforce_step_cancellation = .True.
endif
! Matrix elements of R must by non-NaN
do j = 1,n
do i = 1,LDR
if (disnan(R(i,j))) then
info = 666
print*, 'NaN in rotation matrix'
call ABORT
endif
enddo
enddo
! Display
!if (debug) then
! print*,'Rotation matrix :'
! do i = 1, n
! write(*,'(100(F10.5))') R(i,:)
! enddo
!endif
! Deallocation, end
deallocate(B)
deallocate(m_diag,cos_tau,sin_tau,tau_m1)
deallocate(W,e_val)
deallocate(part_1,part_1a)
deallocate(part_2,part_2a,part_2b,part_2c)
deallocate(RR_t)
call wall_time(t2)
t3 = t2-t1
print*,'Time in rotation matrix:', t3
print*,'---End rotation_matrix---'
end subroutine

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! Rotation matrix in a subspace to rotation matrix in the full space
! Usually, we are using a list of MOs, for exemple the active ones. When
! we compute a rotation matrix to rotate the MOs, we just compute a
! rotation matrix for these MOs in order to reduce the size of the
! matrix which has to be computed. Since the computation of a rotation
! matrix scale in $O(N^3)$ with $N$ the number of MOs, it's better to
! reuce the number of MOs involved.
! After that we replace the rotation matrix in the full space by
! building the elements of the rotation matrix in the full space from
! the elements of the rotation matrix in the subspace and adding some 0
! on the extradiagonal elements and some 1 on the diagonal elements,
! for the MOs that are not involved in the rotation.
! Provided:
! | mo_num | integer | Number of MOs |
! Input:
! | m | integer | Size of tmp_list, m <= mo_num |
! | tmp_list(m) | integer | List of MOs |
! | tmp_R(m,m) | double precision | Rotation matrix in the space of |
! | | | the MOs containing by tmp_list |
! Output:
! | R(mo_num,mo_num | double precision | Rotation matrix in the space |
! | | | of all the MOs |
! Internal:
! | i,j | integer | indexes in the full space |
! | tmp_i,tmp_j | integer | indexes in the subspace |
subroutine sub_to_full_rotation_matrix(m,tmp_list,tmp_R,R)
!BEGIN_DOC
! Compute the full rotation matrix from a smaller one
!END_DOC
implicit none
! in
integer, intent(in) :: m, tmp_list(m)
double precision, intent(in) :: tmp_R(m,m)
! out
double precision, intent(out) :: R(mo_num,mo_num)
! internal
integer :: i,j,tmp_i,tmp_j
! tmp_R to R, subspace to full space
R = 0d0
do i = 1, mo_num
R(i,i) = 1d0 ! 1 on the diagonal because it is a rotation matrix, 1 = nothing change for the corresponding orbital
enddo
do tmp_j = 1, m
j = tmp_list(tmp_j)
do tmp_i = 1, m
i = tmp_list(tmp_i)
R(i,j) = tmp_R(tmp_i,tmp_j)
enddo
enddo
end

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! Predicted energy : e_model
! *Compute the energy predicted by the Taylor series*
! The energy is predicted using a Taylor expansion truncated at te 2nd
! order :
! \begin{align*}
! E_{k+1} = E_{k} + \textbf{g}_k^{T} \cdot \textbf{x}_{k+1} + \frac{1}{2} \cdot \textbf{x}_{k+1}^T \cdot \textbf{H}_{k} \cdot \textbf{x}_{k+1} + \mathcal{O}(\textbf{x}_{k+1}^2)
! \end{align*}
! Input:
! | n | integer | m*(m-1)/2 |
! | v_grad(n) | double precision | gradient |
! | H(n,n) | double precision | hessian |
! | x(n) | double precision | Step in the trust region |
! | prev_energy | double precision | previous energy |
! Output:
! | e_model | double precision | predicted energy after the rotation of the MOs |
! Internal:
! | part_1 | double precision | v_grad^T.x |
! | part_2 | double precision | 1/2 . x^T.H.x |
! | part_2a | double precision | H.x |
! | i,j | integer | indexes |
! Function:
! | ddot | double precision | dot product (Lapack) |
subroutine trust_region_expected_e(n,v_grad,H,x,prev_energy,e_model)
include 'pi.h'
!BEGIN_DOC
! Compute the expected criterion/energy after the application of the step x
!END_DOC
implicit none
! Variables
! in
integer, intent(in) :: n
double precision, intent(in) :: v_grad(n),H(n,n),x(n)
double precision, intent(in) :: prev_energy
! out
double precision, intent(out) :: e_model
! internal
double precision :: part_1, part_2, t1,t2,t3
double precision, allocatable :: part_2a(:)
integer :: i,j
!Function
double precision :: ddot
print*,''
print*,'---Trust_e_model---'
call wall_time(t1)
! Allocation
allocate(part_2a(n))
! Calculations
! part_1 corresponds to the product g.x
! part_2a corresponds to the product H.x
! part_2 corresponds to the product 0.5*(x^T.H.x)
! TODO: remove the dot products
! Product v_grad.x
part_1 = ddot(n,v_grad,1,x,1)
!if (debug) then
print*,'g.x : ', part_1
!endif
! Product H.x
call dgemv('N',n,n,1d0,H,size(H,1),x,1,0d0,part_2a,1)
! Product 1/2 . x^T.H.x
part_2 = 0.5d0 * ddot(n,x,1,part_2a,1)
!if (debug) then
print*,'1/2*x^T.H.x : ', part_2
!endif
print*,'prev_energy', prev_energy
! Sum
e_model = prev_energy + part_1 + part_2
! Writing the predicted energy
print*, 'Predicted energy after the rotation : ', e_model
print*, 'Previous energy - predicted energy:', prev_energy - e_model
! Can be deleted, already in another subroutine
if (DABS(prev_energy - e_model) < 1d-12 ) then
print*,'WARNING: ABS(prev_energy - e_model) < 1d-12'
endif
! Deallocation
deallocate(part_2a)
call wall_time(t2)
t3 = t2 - t1
print*,'Time in trust e model:', t3
print*,'---End trust_e_model---'
print*,''
end subroutine

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! Agreement with the model: Rho
! *Compute the ratio : rho = (prev_energy - energy) / (prev_energy - e_model)*
! Rho represents the agreement between the model (the predicted energy
! by the Taylor expansion truncated at the 2nd order) and the real
! energy :
! \begin{equation}
! \rho^{k+1} = \frac{E^{k} - E^{k+1}}{E^{k} - m^{k+1}}
! \end{equation}
! With :
! $E^{k}$ the energy at the previous iteration
! $E^{k+1}$ the energy at the actual iteration
! $m^{k+1}$ the predicted energy for the actual iteration
! (cf. trust_e_model)
! If $\rho \approx 1$, the agreement is good, contrary to $\rho \approx 0$.
! If $\rho \leq 0$ the previous energy is lower than the actual
! energy. We have to cancel the last step and use a smaller trust
! region.
! Here we cancel the last step if $\rho < 0.1$, because even if
! the energy decreases, the agreement is bad, i.e., the Taylor expansion
! truncated at the second order doesn't represent correctly the energy
! landscape. So it's better to cancel the step and restart with a
! smaller trust region.
! Provided in qp_edit:
! | thresh_rho |
! Input:
! | prev_energy | double precision | previous energy (energy before the rotation) |
! | e_model | double precision | predicted energy after the rotation |
! Output:
! | rho | double precision | the agreement between the model (predicted) and the real energy |
! | prev_energy | double precision | if rho >= 0.1 the actual energy becomes the previous energy |
! | | | else the previous energy doesn't change |
! Internal:
! | energy | double precision | energy (real) after the rotation |
! | i | integer | index |
! | t* | double precision | time |
subroutine trust_region_rho(prev_energy, energy,e_model,rho)
include 'pi.h'
!BEGIN_DOC
! Compute rho, the agreement between the predicted criterion/energy and the real one
!END_DOC
implicit none
! Variables
! In
double precision, intent(inout) :: prev_energy
double precision, intent(in) :: e_model, energy
! Out
double precision, intent(out) :: rho
! Internal
double precision :: t1, t2, t3
integer :: i
print*,''
print*,'---Rho_model---'
call wall_time(t1)
! Rho
! \begin{equation}
! \rho^{k+1} = \frac{E^{k} - E^{k+1}}{E^{k} - m^{k+1}}
! \end{equation}
! In function of $\rho$ th step can be accepted or cancelled.
! If we cancel the last step (k+1), the previous energy (k) doesn't
! change!
! If the step (k+1) is accepted, then the "previous energy" becomes E(k+1)
! Already done in an other subroutine
!if (ABS(prev_energy - e_model) < 1d-12) then
! print*,'WARNING: prev_energy - e_model < 1d-12'
! print*,'=> rho will tend toward infinity'
! print*,'Check you convergence criterion !'
!endif
rho = (prev_energy - energy) / (prev_energy - e_model)
print*, 'previous energy, prev_energy :', prev_energy
print*, 'predicted energy, e_model :', e_model
print*, 'real energy, energy :', energy
print*, 'prev_energy - energy :', prev_energy - energy
print*, 'prev_energy - e_model :', prev_energy - e_model
print*, 'Rho :', rho
print*, 'Threshold for rho:', thresh_rho
! Modification of prev_energy in function of rho
if (rho < thresh_rho) then !0.1) then
! the step is cancelled
print*, 'Rho <', thresh_rho,', the previous energy does not changed'
print*, 'prev_energy :', prev_energy
else
! the step is accepted
prev_energy = energy
print*, 'Rho >=', thresh_rho,', energy -> prev_energy :', energy
endif
call wall_time(t2)
t3 = t2 - t1
print*,'Time in rho model:', t3
print*,'---End rho_model---'
print*,''
end subroutine

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! Trust region
! *Compute the next step with the trust region algorithm*
! The Newton method is an iterative method to find a minimum of a given
! function. It uses a Taylor series truncated at the second order of the
! targeted function and gives its minimizer. The minimizer is taken as
! the new position and the same thing is done. And by doing so
! iteratively the method find a minimum, a local or global one depending
! of the starting point and the convexity/nonconvexity of the targeted
! function.
! The goal of the trust region is to constrain the step size of the
! Newton method in a certain area around the actual position, where the
! Taylor series is a good approximation of the targeted function. This
! area is called the "trust region".
! In addition, in function of the agreement between the Taylor
! development of the energy and the real energy, the size of the trust
! region will be updated at each iteration. By doing so, the step sizes
! are not too larges. In addition, since we add a criterion to cancel the
! step if the energy increases (more precisely if rho < 0.1), so it's
! impossible to diverge. \newline
! References: \newline
! Nocedal & Wright, Numerical Optimization, chapter 4 (1999), \newline
! https://link.springer.com/book/10.1007/978-0-387-40065-5, \newline
! ISBN: 978-0-387-40065-5 \newline
! By using the first and the second derivatives, the Newton method gives
! a step:
! \begin{align*}
! \textbf{x}_{(k+1)}^{\text{Newton}} = - \textbf{H}_{(k)}^{-1} \cdot
! \textbf{g}_{(k)}
! \end{align*}
! which leads to the minimizer of the Taylor series.
! !!! Warning: the Newton method gives the minimizer if and only if
! $\textbf{H}$ is positive definite, else it leads to a saddle point !!!
! But we want a step $\textbf{x}_{(k+1)}$ with a constraint on its (euclidian) norm:
! \begin{align*}
! ||\textbf{x}_{(k+1)}|| \leq \Delta_{(k+1)}
! \end{align*}
! which is equivalent to
! \begin{align*}
! \textbf{x}_{(k+1)}^T \cdot \textbf{x}_{(k+1)} \leq \Delta_{(k+1)}^2
! \end{align*}
! with: \newline
! $\textbf{x}_{(k+1)}$ is the step for the k+1-th iteration (vector of
! size n) \newline
! $\textbf{H}_{(k)}$ is the hessian at the k-th iteration (n by n
! matrix) \newline
! $\textbf{g}_{(k)}$ is the gradient at the k-th iteration (vector of
! size n) \newline
! $\Delta_{(k+1)}$ is the trust radius for the (k+1)-th iteration
! \newline
! Thus we want to constrain the step size $\textbf{x}_{(k+1)}$ into a
! hypersphere of radius $\Delta_{(k+1)}$.\newline
! So, if $||\textbf{x}_{(k+1)}^{\text{Newton}}|| \leq \Delta_{(k)}$ and
! $\textbf{H}$ is positive definite, the
! solution is the step given by the Newton method
! $\textbf{x}_{(k+1)} = \textbf{x}_{(k+1)}^{\text{Newton}}$.
! Else we have to constrain the step size. For simplicity we will remove
! the index $_{(k)}$ and $_{(k+1)}$. To restict the step size, we have
! to put a constraint on $\textbf{x}$ with a Lagrange multiplier.
! Starting from the Taylor series of a function E (here, the energy)
! truncated at the 2nd order, we have:
! \begin{align*}
! E(\textbf{x}) = E +\textbf{g}^T \cdot \textbf{x} + \frac{1}{2}
! \cdot \textbf{x}^T \cdot \textbf{H} \cdot \textbf{x} +
! \mathcal{O}(\textbf{x}^2)
! \end{align*}
! With the constraint on the norm of $\textbf{x}$ we can write the
! Lagrangian
! \begin{align*}
! \mathcal{L}(\textbf{x},\lambda) = E + \textbf{g}^T \cdot \textbf{x}
! + \frac{1}{2} \cdot \textbf{x}^T \cdot \textbf{H} \cdot \textbf{x}
! + \frac{1}{2} \lambda (\textbf{x}^T \cdot \textbf{x} - \Delta^2)
! \end{align*}
! Where: \newline
! $\lambda$ is the Lagrange multiplier \newline
! $E$ is the energy at the k-th iteration $\Leftrightarrow
! E(\textbf{x} = \textbf{0})$ \newline
! To solve this equation, we search a stationary point where the first
! derivative of $\mathcal{L}$ with respect to $\textbf{x}$ becomes 0, i.e.
! \begin{align*}
! \frac{\partial \mathcal{L}(\textbf{x},\lambda)}{\partial \textbf{x}}=0
! \end{align*}
! The derivative is:
! \begin{align*}
! \frac{\partial \mathcal{L}(\textbf{x},\lambda)}{\partial \textbf{x}}
! = \textbf{g} + \textbf{H} \cdot \textbf{x} + \lambda \cdot \textbf{x}
! \end{align*}
! So, we search $\textbf{x}$ such as:
! \begin{align*}
! \frac{\partial \mathcal{L}(\textbf{x},\lambda)}{\partial \textbf{x}}
! = \textbf{g} + \textbf{H} \cdot \textbf{x} + \lambda \cdot \textbf{x} = 0
! \end{align*}
! We can rewrite that as:
! \begin{align*}
! \textbf{g} + \textbf{H} \cdot \textbf{x} + \lambda \cdot \textbf{x}
! = \textbf{g} + (\textbf{H} +\textbf{I} \lambda) \cdot \textbf{x} = 0
! \end{align*}
! with $\textbf{I}$ is the identity matrix.
! By doing so, the solution is:
! \begin{align*}
! (\textbf{H} +\textbf{I} \lambda) \cdot \textbf{x}= -\textbf{g}
! \end{align*}
! \begin{align*}
! \textbf{x}= - (\textbf{H} + \textbf{I} \lambda)^{-1} \cdot \textbf{g}
! \end{align*}
! with $\textbf{x}^T \textbf{x} = \Delta^2$.
! We have to solve this previous equation to find this $\textbf{x}$ in the
! trust region, i.e. $||\textbf{x}|| = \Delta$. Now, this problem is
! just a one dimension problem because we can express $\textbf{x}$ as a
! function of $\lambda$:
! \begin{align*}
! \textbf{x}(\lambda) = - (\textbf{H} + \textbf{I} \lambda)^{-1} \cdot \textbf{g}
! \end{align*}
! We start from the fact that the hessian is diagonalizable. So we have:
! \begin{align*}
! \textbf{H} = \textbf{W} \cdot \textbf{h} \cdot \textbf{W}^T
! \end{align*}
! with: \newline
! $\textbf{H}$, the hessian matrix \newline
! $\textbf{W}$, the matrix containing the eigenvectors \newline
! $\textbf{w}_i$, the i-th eigenvector, i.e. i-th column of $\textbf{W}$ \newline
! $\textbf{h}$, the matrix containing the eigenvalues in ascending order \newline
! $h_i$, the i-th eigenvalue in ascending order \newline
! Now we use the fact that adding a constant on the diagonal just shifts
! the eigenvalues:
! \begin{align*}
! \textbf{H} + \textbf{I} \lambda = \textbf{W} \cdot (\textbf{h}
! +\textbf{I} \lambda) \cdot \textbf{W}^T
! \end{align*}
! By doing so we can express $\textbf{x}$ as a function of $\lambda$
! \begin{align*}
! \textbf{x}(\lambda) = - \sum_{i=1}^n \frac{\textbf{w}_i^T \cdot
! \textbf{g}}{h_i + \lambda} \cdot \textbf{w}_i
! \end{align*}
! with $\lambda \neq - h_i$.
! An interesting thing in our case is the norm of $\textbf{x}$,
! because we want $||\textbf{x}|| = \Delta$. Due to the orthogonality of
! the eigenvectors $\left\{\textbf{w} \right\} _{i=1}^n$ we have:
! \begin{align*}
! ||\textbf{x}(\lambda)||^2 = \sum_{i=1}^n \frac{(\textbf{w}_i^T \cdot
! \textbf{g})^2}{(h_i + \lambda)^2}
! \end{align*}
! So the $||\textbf{x}(\lambda)||^2$ is just a function of $\lambda$.
! And if we study the properties of this function we see that:
! \begin{align*}
! \lim_{\lambda\to\infty} ||\textbf{x}(\lambda)|| = 0
! \end{align*}
! and if $\textbf{w}_i^T \cdot \textbf{g} \neq 0$:
! \begin{align*}
! \lim_{\lambda\to -h_i} ||\textbf{x}(\lambda)|| = + \infty
! \end{align*}
! From these limits and knowing that $h_1$ is the lowest eigenvalue, we
! can conclude that $||\textbf{x}(\lambda)||$ is a continuous and
! strictly decreasing function on the interval $\lambda \in
! (-h_1;\infty)$. Thus, there is one $\lambda$ in this interval which
! gives $||\textbf{x}(\lambda)|| = \Delta$, consequently there is one
! solution.
! Since $\textbf{x} = - (\textbf{H} + \lambda \textbf{I})^{-1} \cdot
! \textbf{g}$ and we want to reduce the norm of $\textbf{x}$, clearly,
! $\lambda > 0$ ($\lambda = 0$ is the unconstraint solution). But the
! Newton method is only defined for a positive definite hessian matrix,
! so $(\textbf{H} + \textbf{I} \lambda)$ must be positive
! definite. Consequently, in the case where $\textbf{H}$ is not positive
! definite, to ensure the positive definiteness, $\lambda$ must be
! greater than $- h_1$.
! \begin{align*}
! \lambda > 0 \quad \text{and} \quad \lambda \geq - h_1
! \end{align*}
! From that there are five cases:
! - if $\textbf{H}$ is positive definite, $-h_1 < 0$, $\lambda \in (0,\infty)$
! - if $\textbf{H}$ is not positive definite and $\textbf{w}_1^T \cdot
! \textbf{g} \neq 0$, $(\textbf{H} + \textbf{I}
! \lambda)$
! must be positve definite, $-h_1 > 0$, $\lambda \in (-h_1, \infty)$
! - if $\textbf{H}$ is not positive definite , $\textbf{w}_1^T \cdot
! \textbf{g} = 0$ and $||\textbf{x}(-h_1)|| > \Delta$ by removing
! $j=1$ in the sum, $(\textbf{H} + \textbf{I} \lambda)$ must be
! positive definite, $-h_1 > 0$, $\lambda \in (-h_1, \infty$)
! - if $\textbf{H}$ is not positive definite , $\textbf{w}_1^T \cdot
! \textbf{g} = 0$ and $||\textbf{x}(-h_1)|| \leq \Delta$ by removing
! $j=1$ in the sum, $(\textbf{H} + \textbf{I} \lambda)$ must be
! positive definite, $-h_1 > 0$, $\lambda = -h_1$). This case is
! similar to the case where $\textbf{H}$ and $||\textbf{x}(\lambda =
! 0)|| \leq \Delta$
! but we can also add to $\textbf{x}$, the first eigenvector $\textbf{W}_1$
! time a constant to ensure the condition $||\textbf{x}(\lambda =
! -h_1)|| = \Delta$ and escape from the saddle point
! Thus to find the solution, we can write:
! \begin{align*}
! ||\textbf{x}(\lambda)|| = \Delta
! \end{align*}
! \begin{align*}
! ||\textbf{x}(\lambda)|| - \Delta = 0
! \end{align*}
! Taking the square of this equation
! \begin{align*}
! (||\textbf{x}(\lambda)|| - \Delta)^2 = 0
! \end{align*}
! we have a function with one minimum for the optimal $\lambda$.
! Since we have the formula of $||\textbf{x}(\lambda)||^2$, we solve
! \begin{align*}
! (||\textbf{x}(\lambda)||^2 - \Delta^2)^2 = 0
! \end{align*}
! But in practice, it is more effective to solve:
! \begin{align*}
! (\frac{1}{||\textbf{x}(\lambda)||^2} - \frac{1}{\Delta^2})^2 = 0
! \end{align*}
! To do that, we just use the Newton method with "trust_newton" using
! first and second derivative of $(||\textbf{x}(\lambda)||^2 -
! \Delta^2)^2$ with respect to $\textbf{x}$.
! This will give the optimal $\lambda$ to compute the
! solution $\textbf{x}$ with the formula seen previously:
! \begin{align*}
! \textbf{x}(\lambda) = - \sum_{i=1}^n \frac{\textbf{w}_i^T \cdot
! \textbf{g}}{h_i + \lambda} \cdot \textbf{w}_i
! \end{align*}
! The solution $\textbf{x}(\lambda)$ with the optimal $\lambda$ is our
! step to go from the (k)-th to the (k+1)-th iteration, is noted $\textbf{x}^*$.
! Evolution of the trust region
! We initialize the trust region at the first iteration using a radius
! \begin{align*}
! \Delta = ||\textbf{x}(\lambda=0)||
! \end{align*}
! And for the next iteration the trust region will evolves depending of
! the agreement of the energy prediction based on the Taylor series
! truncated at the 2nd order and the real energy. If the Taylor series
! truncated at the 2nd order represents correctly the energy landscape
! the trust region will be extent else it will be reduced. In order to
! mesure this agreement we use the ratio rho cf. "rho_model" and
! "trust_e_model". From that we use the following values:
! - if $\rho \geq 0.75$, then $\Delta = 2 \Delta$,
! - if $0.5 \geq \rho < 0.75$, then $\Delta = \Delta$,
! - if $0.25 \geq \rho < 0.5$, then $\Delta = 0.5 \Delta$,
! - if $\rho < 0.25$, then $\Delta = 0.25 \Delta$.
! In addition, if $\rho < 0.1$ the iteration is cancelled, so it
! restarts with a smaller trust region until the energy decreases.
! Summary
! To summarize, knowing the hessian (eigenvectors and eigenvalues), the
! gradient and the radius of the trust region we can compute the norm of
! the Newton step
! \begin{align*}
! ||\textbf{x}(\lambda = 0)||^2 = ||- \textbf{H}^{-1} \cdot \textbf{g}||^2 = \sum_{i=1}^n
! \frac{(\textbf{w}_i^T \cdot \textbf{g})^2}{(h_i + \lambda)^2}, \quad h_i \neq 0
! \end{align*}
! - if $h_1 \geq 0$, $||\textbf{x}(\lambda = 0)|| \leq \Delta$ and
! $\textbf{x}(\lambda=0)$ is in the trust region and it is not
! necessary to put a constraint on $\textbf{x}$, the solution is the
! unconstrained one, $\textbf{x}^* = \textbf{x}(\lambda = 0)$.
! - else if $h_1 < 0$, $\textbf{w}_1^T \cdot \textbf{g} = 0$ and
! $||\textbf{x}(\lambda = -h_1)|| \leq \Delta$ (by removing $j=1$ in
! the sum), the solution is $\textbf{x}^* = \textbf{x}(\lambda =
! -h_1)$, similarly to the previous case.
! But we can add to $\textbf{x}$, the first eigenvector $\textbf{W}_1$
! time a constant to ensure the condition $||\textbf{x}(\lambda =
! -h_1)|| = \Delta$ and escape from the saddle point
! - else if $h_1 < 0$ and $\textbf{w}_1^T \cdot \textbf{g} \neq 0$ we
! have to search $\lambda \in (-h_1, \infty)$ such as
! $\textbf{x}(\lambda) = \Delta$ by solving with the Newton method
! \begin{align*}
! (||\textbf{x}(\lambda)||^2 - \Delta^2)^2 = 0
! \end{align*}
! or
! \begin{align*}
! (\frac{1}{||\textbf{x}(\lambda)||^2} - \frac{1}{\Delta^2})^2 = 0
! \end{align*}
! which is numerically more stable. And finally compute
! \begin{align*}
! \textbf{x}^* = \textbf{x}(\lambda) = - \sum_{i=1}^n \frac{\textbf{w}_i^T \cdot
! \textbf{g}}{h_i + \lambda} \cdot \textbf{w}_i
! \end{align*}
! - else if $h_1 \geq 0$ and $||\textbf{x}(\lambda = 0)|| > \Delta$ we
! do exactly the same thing that the previous case but we search
! $\lambda \in (0, \infty)$
! - else if $h_1 < 0$ and $\textbf{w}_1^T \cdot \textbf{g} = 0$ and
! $||\textbf{x}(\lambda = -h_1)|| > \Delta$ (by removing $j=1$ in the
! sum), again we do exactly the same thing that the previous case
! searching $\lambda \in (-h_1, \infty)$.
! For the cases where $\textbf{w}_1^T \cdot \textbf{g} = 0$ it is not
! necessary in fact to remove the $j = 1$ in the sum since the term
! where $h_i - \lambda < 10^{-6}$ are not computed.
! After that, we take this vector $\textbf{x}^*$, called "x", and we do
! the transformation to an antisymmetric matrix $\textbf{X}$, called
! m_x. This matrix $\textbf{X}$ will be used to compute a rotation
! matrix $\textbf{R}= \exp(\textbf{X})$ in "rotation_matrix".
! NB:
! An improvement can be done using a elleptical trust region.
! Code
! Provided:
! | mo_num | integer | number of MOs |
! Cf. qp_edit in orbital optimization section, for some constants/thresholds
! Input:
! | m | integer | number of MOs |
! | n | integer | m*(m-1)/2 |
! | H(n, n) | double precision | hessian |
! | v_grad(n) | double precision | gradient |
! | e_val(n) | double precision | eigenvalues of the hessian |
! | W(n, n) | double precision | eigenvectors of the hessian |
! | rho | double precision | agreement between the model and the reality, |
! | | | represents the quality of the energy prediction |
! | nb_iter | integer | number of iteration |
! Input/Ouput:
! | delta | double precision | radius of the trust region |
! Output:
! | x(n) | double precision | vector containing the step |
! Internal:
! | accu | double precision | temporary variable to compute the step |
! | lambda | double precision | lagrange multiplier |
! | trust_radius2 | double precision | square of the radius of the trust region |
! | norm2_x | double precision | norm^2 of the vector x |
! | norm2_g | double precision | norm^2 of the vector containing the gradient |
! | tmp_wtg(n) | double precision | tmp_wtg(i) = w_i^T . g |
! | i, j, k | integer | indexes |
! Function:
! | dnrm2 | double precision | Blas function computing the norm |
! | f_norm_trust_region_omp | double precision | compute the value of norm(x(lambda)^2) |
subroutine trust_region_step(n,nb_iter,v_grad,rho,e_val,w,x,delta)
include 'pi.h'
!BEGIN_DOC
! Compuet the step in the trust region
!END_DOC
implicit none
! Variables
! in
integer, intent(in) :: n
double precision, intent(in) :: v_grad(n), rho
integer, intent(inout) :: nb_iter
double precision, intent(in) :: e_val(n), w(n,n)
! inout
double precision, intent(inout) :: delta
! out
double precision, intent(out) :: x(n)
! Internal
double precision :: accu, lambda, trust_radius2
double precision :: norm2_x, norm2_g
double precision, allocatable :: tmp_wtg(:)
integer :: i,j,k
double precision :: t1,t2,t3
integer :: n_neg_eval
! Functions
double precision :: ddot, dnrm2
double precision :: f_norm_trust_region_omp
print*,''
print*,'=================='
print*,'---Trust_region---'
print*,'=================='
call wall_time(t1)
! Allocation
allocate(tmp_wtg(n))
! Initialization and norm
! The norm of the step size will be useful for the trust region
! algorithm. We start from a first guess and the radius of the trust
! region will evolve during the optimization.
! avoid_saddle is actually a test to avoid saddle points
! Initialization of the Lagrange multiplier
lambda = 0d0
! List of w^T.g, to avoid the recomputation
tmp_wtg = 0d0
do j = 1, n
do i = 1, n
tmp_wtg(j) = tmp_wtg(j) + w(i,j) * v_grad(i)
enddo
enddo
! Replacement of the small tmp_wtg corresponding to a negative eigenvalue
! in the case of avoid_saddle
if (avoid_saddle .and. e_val(1) < - thresh_eig) then
i = 2
! Number of negative eigenvalues
do while (e_val(i) < - thresh_eig)
if (tmp_wtg(i) < thresh_wtg2) then
if (version_avoid_saddle == 1) then
tmp_wtg(i) = 1d0
elseif (version_avoid_saddle == 2) then
tmp_wtg(i) = DABS(e_val(i))
elseif (version_avoid_saddle == 3) then
tmp_wtg(i) = dsqrt(DABS(e_val(i)))
else
tmp_wtg(i) = thresh_wtg2
endif
endif
i = i + 1
enddo
! For the fist one it's a little bit different
if (tmp_wtg(1) < thresh_wtg2) then
tmp_wtg(1) = 0d0
endif
endif
! Norm^2 of x, ||x||^2
norm2_x = f_norm_trust_region_omp(n,e_val,tmp_wtg,0d0)
! We just use this norm for the nb_iter = 0 in order to initialize the trust radius delta
! We don't care about the sign of the eigenvalue we just want the size of the step in a normal Newton-Raphson algorithm
! Anyway if the step is too big it will be reduced
print*,'||x||^2 :', norm2_x
! Norm^2 of the gradient, ||v_grad||^2
norm2_g = (dnrm2(n,v_grad,1))**2
print*,'||grad||^2 :', norm2_g
! Trust radius initialization
! At the first iteration (nb_iter = 0) we initialize the trust region
! with the norm of the step generate by the Newton's method ($\textbf{x}_1 =
! (\textbf{H}_0)^{-1} \cdot \textbf{g}_0$,
! we compute this norm using f_norm_trust_region_omp as explain just
! below)
! trust radius
if (nb_iter == 0) then
trust_radius2 = norm2_x
! To avoid infinite loop of cancellation of this first step
! without changing delta
nb_iter = 1
! Compute delta, delta = sqrt(trust_radius)
delta = dsqrt(trust_radius2)
endif
! Modification of the trust radius
! In function of rho (which represents the agreement between the model
! and the reality, cf. rho_model) the trust region evolves. We update
! delta (the radius of the trust region).
! To avoid too big trust region we put a maximum size.
! Modification of the trust radius in function of rho
if (rho >= 0.75d0) then
delta = 2d0 * delta
elseif (rho >= 0.5d0) then
delta = delta
elseif (rho >= 0.25d0) then
delta = 0.5d0 * delta
else
delta = 0.25d0 * delta
endif
! Maximum size of the trust region
!if (delta > 0.5d0 * n * pi) then
! delta = 0.5d0 * n * pi
! print*,'Delta > delta_max, delta = 0.5d0 * n * pi'
!endif
if (delta > 1d10) then
delta = 1d10
endif
print*, 'Delta :', delta
! Calculation of the optimal lambda
! We search the solution of $(||x||^2 - \Delta^2)^2 = 0$
! - If $||\textbf{x}|| > \Delta$ or $h_1 < 0$ we have to add a constant
! $\lambda > 0 \quad \text{and} \quad \lambda > -h_1$
! - If $||\textbf{x}|| \leq \Delta$ and $h_1 \geq 0$ the solution is the
! unconstrained one, $\lambda = 0$
! You will find more details at the beginning
! By giving delta, we search (||x||^2 - delta^2)^2 = 0
! and not (||x||^2 - delta)^2 = 0
! Research of lambda to solve ||x(lambda)|| = Delta
! Display
print*, 'e_val(1) = ', e_val(1)
print*, 'w_1^T.g =', tmp_wtg(1)
! H positive definite
if (e_val(1) > - thresh_eig) then
norm2_x = f_norm_trust_region_omp(n,e_val,tmp_wtg,0d0)
print*, '||x(0)||=', dsqrt(norm2_x)
print*, 'Delta=', delta
! H positive definite, ||x(lambda = 0)|| <= Delta
if (dsqrt(norm2_x) <= delta) then
print*, 'H positive definite, ||x(lambda = 0)|| <= Delta'
print*, 'lambda = 0, no lambda optimization'
lambda = 0d0
! H positive definite, ||x(lambda = 0)|| > Delta
else
! Constraint solution
print*, 'H positive definite, ||x(lambda = 0)|| > Delta'
print*,'Computation of the optimal lambda...'
call trust_region_optimal_lambda(n,e_val,tmp_wtg,delta,lambda)
endif
! H indefinite
else
if (DABS(tmp_wtg(1)) < thresh_wtg) then
norm2_x = f_norm_trust_region_omp(n,e_val,tmp_wtg, - e_val(1))
print*, 'w_1^T.g <', thresh_wtg,', ||x(lambda = -e_val(1))|| =', dsqrt(norm2_x)
endif
! H indefinite, w_1^T.g = 0, ||x(lambda = -e_val(1))|| <= Delta
if (dsqrt(norm2_x) <= delta .and. DABS(tmp_wtg(1)) < thresh_wtg) then
! Add e_val(1) in order to have (H - e_val(1) I) positive definite
print*, 'H indefinite, w_1^T.g = 0, ||x(lambda = -e_val(1))|| <= Delta'
print*, 'lambda = -e_val(1), no lambda optimization'
lambda = - e_val(1)
! H indefinite, w_1^T.g = 0, ||x(lambda = -e_val(1))|| > Delta
! and
! H indefinite, w_1^T.g =/= 0
else
! Constraint solution/ add lambda
if (DABS(tmp_wtg(1)) < thresh_wtg) then
print*, 'H indefinite, w_1^T.g = 0, ||x(lambda = -e_val(1))|| > Delta'
else
print*, 'H indefinite, w_1^T.g =/= 0'
endif
print*, 'Computation of the optimal lambda...'
call trust_region_optimal_lambda(n,e_val,tmp_wtg,delta,lambda)
endif
endif
! Recomputation of the norm^2 of the step x
norm2_x = f_norm_trust_region_omp(n,e_val,tmp_wtg,lambda)
print*,''
print*,'Summary after the trust region:'
print*,'lambda:', lambda
print*,'||x||:', dsqrt(norm2_x)
print*,'delta:', delta
! Calculation of the step x
! x refers to $\textbf{x}^*$
! We compute x in function of lambda using its formula :
! \begin{align*}
! \textbf{x}^* = \textbf{x}(\lambda) = - \sum_{i=1}^n \frac{\textbf{w}_i^T \cdot \textbf{g}}{h_i
! + \lambda} \cdot \textbf{w}_i
! \end{align*}
! Initialisation
x = 0d0
! Calculation of the step x
! Normal version
if (.not. absolute_eig) then
do i = 1, n
if (DABS(e_val(i)) > thresh_eig .and. DABS(e_val(i)+lambda) > thresh_eig) then
do j = 1, n
x(j) = x(j) - tmp_wtg(i) * W(j,i) / (e_val(i) + lambda)
enddo
endif
enddo
! Version to use the absolute value of the eigenvalues
else
do i = 1, n
if (DABS(e_val(i)) > thresh_eig) then
do j = 1, n
x(j) = x(j) - tmp_wtg(i) * W(j,i) / (DABS(e_val(i)) + lambda)
enddo
endif
enddo
endif
double precision :: beta, norm_x
! Test
! If w_1^T.g = 0, the lim of ||x(lambda)|| when lambda tend to -e_val(1)
! is not + infinity. So ||x(lambda=-e_val(1))|| < delta, we add the first
! eigenvectors multiply by a constant to ensure the condition
! ||x(lambda=-e_val(1))|| = delta and escape the saddle point
if (avoid_saddle .and. e_val(1) < - thresh_eig) then
if (tmp_wtg(1) < 1d-15 .and. (1d0 - dsqrt(norm2_x)/delta) > 1d-3 ) then
! norm of x
norm_x = dnrm2(n,x,1)
! Computes the coefficient for the w_1
beta = delta**2 - norm_x**2
! Updates the step x
x = x + W(:,1) * dsqrt(beta)
! Recomputes the norm to check
norm_x = dnrm2(n,x,1)
print*, 'Add w_1 * dsqrt(delta^2 - ||x||^2):'
print*, '||x||', norm_x
endif
endif
! Transformation of x
! x is a vector of size n, so it can be write as a m by m
! antisymmetric matrix m_x cf. "mat_to_vec_index" and "vec_to_mat_index".
! ! Step transformation vector -> matrix
! ! Vector with n element -> mo_num by mo_num matrix
! do j = 1, m
! do i = 1, m
! if (i>j) then
! call mat_to_vec_index(i,j,k)
! m_x(i,j) = x(k)
! else
! m_x(i,j) = 0d0
! endif
! enddo
! enddo
!
! ! Antisymmetrization of the previous matrix
! do j = 1, m
! do i = 1, m
! if (i<j) then
! m_x(i,j) = - m_x(j,i)
! endif
! enddo
! enddo
! Deallocation, end
deallocate(tmp_wtg)
call wall_time(t2)
t3 = t2 - t1
print*,'Time in trust_region:', t3
print*,'======================'
print*,'---End trust_region---'
print*,'======================'
print*,''
end

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! Vect to antisymmetric matrix using mat_to_vec_index
! Vector to antisymmetric matrix transformation using mat_to_vec_index
! subroutine.
! Can be done in OMP (for the first part and with omp critical for the second)
subroutine vec_to_mat_v2(n,m,v_x,m_x)
!BEGIN_DOC
! Vector to antisymmetric matrix
!END_DOC
implicit none
integer, intent(in) :: n,m
double precision, intent(in) :: v_x(n)
double precision, intent(out) :: m_x(m,m)
integer :: i,j,k
! 1D -> 2D lower diagonal
m_x = 0d0
do j = 1, m - 1
do i = j + 1, m
call mat_to_vec_index(i,j,k)
m_x(i,j) = v_x(k)
enddo
enddo
! Antisym
do i = 1, m - 1
do j = i + 1, m
m_x(i,j) = - m_x(j,i)
enddo
enddo
end