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Changed util_minimize_bfgs to use the lambda object type instead of a function argument
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@daminton
daminton committed May 13, 2021
1 parent a530a80 commit 37a5250
Showing 1 changed file with 15 additions and 57 deletions.
72 changes: 15 additions & 57 deletions src/util/util_minimize_bfgs.f90
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Original file line number Diff line number Diff line change
Expand Up @@ -3,7 +3,7 @@ function util_minimize_bfgs(f, N, x1, eps) result(fnum)
!! - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
!! This function implements the Broyden-Fletcher-Goldfarb-Shanno method to determine the minimum of a function of N variables.
!! It recieves as input:
!! f(x) : function of one real(DP) array variable as input
!! f%eval(x) : lambda function object containing the objective function as the eval metho
!! N : Number of variables of function f
!! x1 : Initial starting value of x
!! eps : Accuracy of 1 - dimensional minimization at each step
Expand All @@ -19,13 +19,7 @@ function util_minimize_bfgs(f, N, x1, eps) result(fnum)
implicit none
! Arguments
integer(I4B), intent(in) :: N
interface
pure function f(x) ! Objective function template
import DP
real(DP), dimension(:), intent(in) :: x
real(DP) :: f
end function
end interface
class(lambda_obj), intent(in) :: f
real(DP), dimension(:), intent(inout) :: x1
real(DP), intent(in) :: eps
! Result
Expand Down Expand Up @@ -106,7 +100,7 @@ function gradf(f, N, x1, grad, dx) result(fnum)
!! Purpose: Estimates the gradient of a function using a central difference
!! approximation
!! Inputs:
!! f(x) : function of a real array of x(N) as input
!! f%eval(x) : lambda function object containing the objective function as the eval metho
!! N : number of variables N
!! x1 : x value array
!! dx : step size to use when calculating derivatives
Expand All @@ -118,13 +112,7 @@ function gradf(f, N, x1, grad, dx) result(fnum)
implicit none
! Arguments
integer(I4B), intent(in) :: N
interface
pure function f(x)
import DP
real(DP), dimension(:), intent(in) :: x
real(DP) :: f
end function f
end interface
class(lambda_obj), intent(in) :: f
real(DP), dimension(:), intent(in) :: x1
real(DP), dimension(:), intent(out) :: grad
real(DP), intent(in) :: dx
Expand All @@ -138,7 +126,7 @@ end function f
do i = 1, N
xp(:) = [((x1(j), j=1, k-1), x1(j) + dx, (x1(j), j=k+1, N), k=1, N)]
xm(:) = [((x1(j), j=1, k-1), x1(j) - dx, (x1(j), j=k+1, N), k=1, N)]
grad(i) = (f(xp) - f(xm)) / (2 * dx)
grad(i) = (f%eval(xp) - f%eval(xm)) / (2 * dx)
fnum = fnum + 2
end do
return
Expand All @@ -152,7 +140,7 @@ function minimize1D(f, x0, S, N, eps, astar) result(fnum)
!! 2. The golden section method
!! 3. A quadratic polynomial fit
!! Inputs
!! f(x) : function of one real(DP) array variable as input
!! f%eval(x) : lambda function object containing the objective function as the eval metho
!! x0 : Array of size N of initial x values
!! S : Array of size N that determines the direction of minimization
!! N : Number of variables of function f
Expand All @@ -166,13 +154,7 @@ function minimize1D(f, x0, S, N, eps, astar) result(fnum)
implicit none
! Arguments
integer(I4B), intent(in) :: N
interface
pure function f(x) ! Objective function template
import DP
real(DP), dimension(:), intent(in) :: x
real(DP) :: f
end function f
end interface
class(lambda_obj), intent(in) :: f
real(DP), dimension(:), intent(in) :: x0, S
real(DP), intent(in) :: eps
real(DP), intent(out) :: astar
Expand Down Expand Up @@ -226,13 +208,7 @@ function n2one(f, x0, S, N, a) result(fnew)
implicit none
! Arguments
integer(I4B), intent(in) :: N
interface
pure function f(x) ! Objective function template
import DP
real(DP), dimension(:), intent(in) :: x
real(DP) :: f
end function f
end interface
class(lambda_obj), intent(in) :: f
real(DP), dimension(:), intent(in) :: x0, S
real(DP), intent(in) :: a
! Return
Expand All @@ -242,7 +218,7 @@ end function f
integer(I4B) :: i

xnew(:) = x0(:) + a * S(:)
fnew = f(xnew(:))
fnew = f%eval(xnew(:))
return
end function n2one

Expand All @@ -252,7 +228,7 @@ end function n2one
function bracket(f, x0, S, N, lo, hi, gam, step) result(fnum)
! - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
!! This function brackets the minimum. It recieves as input:
!! f(x) : function of one real(DP) array variable as input
!! f%eval(x) : lambda function object containing the objective function as the eval metho
!! x0 : Array of size N of initial x values
!! S : Array of size N that determines the direction of minimization
!! lo : initial guess of lo bracket value
Expand All @@ -268,13 +244,7 @@ function bracket(f, x0, S, N, lo, hi, gam, step) result(fnum)
implicit none
! Arguments
integer(I4B), intent(in) :: N
interface
pure function f(x) ! Objective function template
import DP
real(DP), dimension(:), intent(in) :: x
real(DP) :: f
end function f
end interface
class(lambda_obj), intent(in) :: f
real(DP), dimension(:), intent(in) :: x0, S
real(DP), intent(inout) :: lo, hi
real(DP), intent(in) :: gam, step
Expand Down Expand Up @@ -377,7 +347,7 @@ function golden(f, x0, S, N, lo, hi, eps) result(fnum)
! - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
!! This function uses the golden section method to reduce the starting interval lo, hi by some amount sigma.
!! It recieves as input:
!! f(x) : function of one real(DP) array variable as input
!! f%eval(x) : lambda function object containing the objective function as the eval metho
!! x0 : Array of size N of initial x values
!! S : Array of size N that determines the direction of minimization
!! lo : initial guess of lo bracket value
Expand All @@ -394,13 +364,7 @@ function golden(f, x0, S, N, lo, hi, eps) result(fnum)
implicit none
! Arguments
integer(I4B), intent(in) :: N
interface
pure function f(x) ! Objective function template
import DP
real(DP), dimension(:), intent(in) :: x
real(DP) :: f
end function f
end interface
class(lambda_obj), intent(in) :: f
real(DP), dimension(:), intent(in) :: x0, S
real(DP), intent(inout) :: lo, hi
real(DP), intent(in) :: eps
Expand Down Expand Up @@ -450,7 +414,7 @@ function quadfit(f, x0, S, N, lo, hi, eps) result(fnum)
! - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
!! This function uses a quadratic polynomial fit to * locate the minimum of a function
!! to some accuracy eps. It recieves as input:
!! f(x) : function of one real(DP) :: variable as input
!! f%eval(x) : lambda function object containing the objective function as the eval metho
!! lo : low bracket value
!! hi : high bracket value
!! eps : desired accuracy of final minimum location
Expand All @@ -464,13 +428,7 @@ function quadfit(f, x0, S, N, lo, hi, eps) result(fnum)
implicit none
! Arguments
integer(I4B), intent(in) :: N
interface
pure function f(x) ! Objective function template
import DP
real(DP), dimension(:), intent(in) :: x
real(DP) :: f
end function f
end interface
class(lambda_obj), intent(in) :: f
real(DP), dimension(:), intent(in) :: x0, S
real(DP), intent(inout) :: lo, hi
real(DP), intent(in) :: eps
Expand Down

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