mirror of
https://github.com/pfloos/quack
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122 lines
4.0 KiB
Fortran
122 lines
4.0 KiB
Fortran
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subroutine MinMCMP2(nBas,nEl,nC,nO,nV,c,e,EcMP2, &
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nMC,nEq,nWalk,dt,nPrint, &
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nShell,CenterShell,TotAngMomShell,KShell,DShell,ExpShell, &
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TrialType,Norm,cTrial,gradient,hessian)
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! Minimize the variance of MC-MP2
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implicit none
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include 'parameters.h'
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! Input variables
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integer,intent(in) :: nBas,nEl,nC,nO,nV,nMC,nEq,nWalk,nPrint
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double precision,intent(in) :: EcMP2(3),dt
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double precision,intent(in) :: c(nBas,nBas),e(nBas)
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integer,intent(in) :: nShell
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integer,intent(in) :: TotAngMomShell(maxShell),KShell(maxShell)
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double precision,intent(in) :: CenterShell(maxShell,3),DShell(maxShell,maxK),ExpShell(maxShell,maxK)
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! Local variables
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logical :: debug,varmin,mincvg
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double precision :: thresh
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double precision,allocatable :: max_gradient(:),energy_MCMP2(:),variance_MCMP2(:),error_MCMP2(:),NormTr(:)
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double precision :: EcMCMP2(3),Err_EcMCMP2(3),Var_EcMCMP2(3)
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integer :: it,nIt,i
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! Output variables
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integer,intent(in) :: TrialType
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double precision,intent(inout):: Norm,cTrial(nBas),gradient(nBas),hessian(nBas,nBas)
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! Debuging mode
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! debug = .true.
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debug = .false.
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! Minimization parameters
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varmin = .true.
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mincvg = .false.
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nIt = 10
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thresh = 1d-5
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allocate(max_gradient(nIt),energy_MCMP2(nIt),variance_MCMP2(nIt),error_MCMP2(nIt),normTr(nIt))
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if(TrialType == 1) then
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! Use HOMO as guess
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cTrial(1:nBas) = c(1:nBas,nEl/2)
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! Normalization factor will be computed later
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endif
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!------------------------------------------------------------------------
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! Start MC-MP2 variance minimization
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!------------------------------------------------------------------------
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it = 0
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do while (it < nIt .and. .not.(mincvg))
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it = it + 1
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write(*,*) '**********************************************************************'
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write(*,*) ' Variance minimization of MC-MP2 energy '
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write(*,*) '**********************************************************************'
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write(*,*) ' Iteration n.',it
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write(*,*) '**********************************************************************'
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write(*,*)
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write(*,*) ' Trial wave function coefficients at iteration n.',it
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call matout(nBas,1,cTrial)
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write(*,*)
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call MCMP2(varmin,nBas,nEl,nC,nO,nV,c,e,EcMP2, &
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nMC,nEq,nWalk,dt,nPrint, &
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nShell,CenterShell,TotAngMomShell,KShell,DShell,ExpShell, &
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TrialType,Norm,cTrial,gradient,hessian, &
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EcMCMP2,Err_EcMCMP2,Var_EcMCMP2)
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! Newton update of the coefficients
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call Newton(nBas,gradient,hessian,cTrial)
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! Check for convergence
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max_gradient(it) = maxval(abs(gradient))
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energy_MCMP2(it) = EcMCMP2(1)
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variance_MCMP2(it) = Var_EcMCMP2(1)
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error_MCMP2(it) = Err_EcMCMP2(1)
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NormTr(it) = Norm
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write(*,*)
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write(*,*) 'Maximum gradient at iteration n.',it,':',max_gradient(it)
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write(*,*)
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if(max_gradient(it) < thresh) then
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write(*,*) ' Miracle! Variance minimization of MC-MP2 has converged!'
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mincvg = .true.
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endif
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enddo
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write(*,*)
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write(*,*) '********************************'
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write(*,*) 'Summary of variance minimization'
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write(*,*) '********************************'
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write(*,*)
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write(*,'(A3,A20,A20,A20,A20,A20,A20)') &
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'It.','Gradient','Ec(MC-MPC2)','Variance','Error','Ec(MC-MP2)-Ec(MP2)','Norm'
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write(*,'(I3,4X,F16.10,4X,F16.10,4X,F16.10,4X,F16.10,4X,F16.10,4X,F16.10)') &
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(i,max_gradient(i),energy_MCMP2(i),variance_MCMP2(i),error_MCMP2(i),energy_MCMP2(i)-EcMP2(1),NormTr(i),i=1,it)
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write(*,*)
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!------------------------------------------------------------------------
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! End MC-MP2 variance minimization
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!------------------------------------------------------------------------
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end subroutine MinMCMP2
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