doc

src/ForTimize.f90

@@ -1,4 +1,29 @@
 module ForTimize
+  !! # ForTimize: A Fortran optimization library
+  !!
+  !! ## Description
+  !! ForTimize focuses on providing a simple and easy-to-use interface for
+  !! optimization algorithms in Fortran. It is designed to be modular and
+  !! extensible, allowing users to easily add new optimization algorithms.
+  !!
+  !! ## Usage
+  !! To use ForTimize, simply include the `ForTimize` module in your program.
+  !! 
+  !! ### Objective Function
+  !! The function that you want to minimize should have the following signature:
+  !! ```fortran
+  !! subroutine foo(X, F, dF, data)
+  !!  real(pr), intent(in) :: x(:)
+  !!  real(pr), intent(out) :: F
+  !!  real(pr), optional, intent(out) :: dF(:)
+  !!  class(*), optional, intent(in out) :: data
+  !! end subroutine foo
+  !! ```
+  !! ### Basic calling
+  !! ```fortran
+  !! use ForTimize, only: minimize
+  !! call minimize(foo, x, y)
+  !! ```
   use ForTimize__constants, only: pr
   use ForTimize__base
   use ForTimize__minimize, only: minimize

src/minimize.f90

@@ -8,11 +8,16 @@ subroutine minimize(f, x, y, optim, data)
       !! Minimize the objective function f.
       !! If no optimizer is provided, the Nelder-Mead algorithm will be used
       !! as default.
-      procedure(objective_function) :: f !! Objective function
-      real(pr), intent(in out) :: x(:) !! Vector of parameters
-      real(pr), intent(out) :: y !! Function value after optimization
-      class(Optimizer), optional :: optim !! Optimizing algorithm
-      class(*), optional, target, intent(in out) :: data !! Optional data to be passed to the objective function
+      procedure(objective_function) :: f 
+         !! Objective function
+      real(pr), intent(in out) :: x(:) 
+         !! Vector of parameters
+      real(pr), intent(out) :: y 
+         !! Function value after optimization
+      class(Optimizer), optional :: optim 
+         !! Optimizing algorithm
+      class(*), optional, target, intent(in out) :: data 
+         !! Optional data to be passed to the objective function
 
       type(NelderMead) :: nmopt
 

test/check.f90

@@ -42,15 +42,15 @@ subroutine foo(X, F, dF, data)
    end subroutine foo
 end module fixture_problem
 
-program grad_descent
+program test
    use ForTimize, only: GradientDescent, minimize, NelderMead
    use fixture_problem, only: foo
 
    type(GradientDescent) :: optimizer
    type(NelderMead) :: nmopt
    real(8) :: yold, y, x(3), x0(3)
 
-   x0 = [ 1.3, -2.5,  1.]
+   x0 = [1.3, -2.5,  1.]
    optimizer%step = 0.001
    optimizer%max_iters = 1000
 
@@ -61,4 +61,4 @@ program grad_descent
 
    call minimize(foo, x0, y, nmopt)
    if (yold < y) error stop 1
-end program grad_descent
+end program test