########################################################################################################
### GCMMA-MMA-Julia                                                                                  ### 
###                                                                                                  ###
### This file is part of GCMMA-MMA-Julia. GCMMA-MMA-Julia is licensed under the terms of GNU         ###
### General Public License as published by the Free Software Foundation. For more information and    ###
### the LICENSE file, see <https://github.com/pollinico/GCMMA-MMA-Julia/blob/main/LICENSE>.          ###
###                                                                                                  ###
### The orginal work is written by Krister Svanberg in MATLAB.                                       ###
### This is the Julia version of the code written by Nicol√≤ Pollini.                                 ###
### version 18-05-2023                                                                               ###
########################################################################################################

#-------------------------------------------------------------
#
#    Copyright (C) 2007 Krister Svanberg
#
#    This file, gctoymain.m, is part of GCMMA-MMA-code.
#    
#    GCMMA-MMA-code is free software; you can redistribute it and/or
#    modify it under the terms of the GNU General Public License as 
#    published by the Free Software Foundation; either version 3 of 
#    the License, or (at your option) any later version.
#    
#    This code is distributed in the hope that it will be useful,
#    but WITHOUT ANY WARRANTY; without even the implied warranty of
#    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
#    GNU General Public License for more details.
#    
#    You should have received a copy of the GNU General Public License
#    (file COPYING) along with this file.  If not, see 
#    <http://www.gnu.org/licenses/>.
#    
#    You should have received a file README along with this file,
#    containing contact information.  If not, see
#    <http://www.smoptit.se/> or e-mail mmainfo@smoptit.se or krille@math.kth.se.
#
#------
#
#  Version July 2007.
#
#  This file contains a main program for using GCMMA to solve
#  a problem defined by the users files gctoyinit.m
#  (which must be run before gctoymain.m), toy1.m and toy2.m.
#
#### If outeriter=0, the user should now calculate function values
#### and gradients of the objective- and constraint functions at xval.
#### The results should be put in f0val, df0dx, fval and dfdx:

include("gctoyinit.jl")
include("toy1.jl")
include("toy2.jl")
include("gcmmasub.jl")
include("subsolv.jl")
include("kktcheck.jl")
include("asymp.jl")
include("concheck.jl")

if outeriter < 0.5
    global f0val,df0dx,fval,dfdx, innerit
    f0val,df0dx,fval,dfdx = toy2(xval)
    innerit = 0
    outvector1 = [outeriter; innerit; xval]
    outvector2 = [f0val; fval]
end
#
#### The outer iterations start:
kktnorm = kkttol+10
outit = 0
while (kktnorm > kkttol) & (outit < maxoutit)
    global raa, raa0, raaeps, raa0eps, innerit
    global kktnorm, outit, outeriter, outvector1, outvector2, xmin, xmax, xval, low, upp, c, d, a0, a, xold1, xold2
    global f0val, df0dx, fval, dfdx
    outit   = outit+1
    outeriter = outeriter+1
    #### The parameters low, upp, raa0 and raa are calculated:
    low,upp,raa0,raa = asymp(outeriter,n,xval,xold1,xold2,xmin,xmax,low,upp,raa0,raa,raa0eps,raaeps,df0dx,dfdx)
    #### The GCMMA subproblem is solved at the point xval:
    xmma,ymma,zmma,lam,xsi,eta,mu,zet,s,f0app,fapp = gcmmasub(m,n,outeriter,epsimin,xval,xmin,xmax,low,upp,raa0,raa,f0val,df0dx,fval,dfdx,a0,a,c,d)
    #### The user should now calculate function values (no gradients)
    #### of the objective- and constraint functions at the point xmma
    #### ( = the optimal solution of the subproblem).
    #### The results should be put in f0valnew and fvalnew.
    f0valnew,fvalnew = toy1(xmma)
    #### It is checked if the approximations are conservative:
    conserv = concheck(m,epsimin,f0app,f0valnew,fapp,fvalnew)
    #### While the approximations are non-conservative (conserv=0),
    #### repeated inner iterations are made:
    innerit=0
    if conserv == 0
        while (conserv == 0) & (innerit <= 15)
        innerit = innerit+1;
    #### New values on the parameters raa0 and raa are calculated:
        raa0,raa = raaupdate(xmma,xval,xmin,xmax,low,upp,f0valnew,fvalnew,f0app,fapp,raa0,raa,raa0eps,raaeps,epsimin)
    #### The GCMMA subproblem is solved with these new raa0 and raa:
        xmma,ymma,zmma,lam,xsi,eta,mu,zet,s,f0app,fapp = gcmmasub(m,n,outeriter,epsimin,xval,xmin,xmax,low,upp,
                raa0,raa,f0val,df0dx,fval,dfdx,a0,a,c,d)
    #### The user should now calculate function values (no gradients)
    #### of the objective- and constraint functions at the point xmma
    #### ( = the optimal solution of the subproblem).
    #### The results should be put in f0valnew and fvalnew:
        f0valnew,fvalnew = toy1(xmma)
    #### It is checked if the approximations have become conservative:
        conserv = concheck(m,epsimin,f0app,f0valnew,fapp,fvalnew)
        end
    end
    #### No more inner iterations. Some vectors are updated:
    xold2 = xold1
    xold1 = xval
    xval  = xmma
    #### The user should now calculate function values and gradients
    #### of the objective- and constraint functions at xval.
    #### The results should be put in f0val, df0dx, fval and dfdx:
    f0val,df0dx,fval,dfdx = toy2(xval)
    #### The residual vector of the KKT conditions is calculated:
    residu,kktnorm,residumax = kktcheck(m,n,xmma,ymma,zmma,lam,xsi,eta,mu,zet,s,xmin,xmax,df0dx,fval,dfdx,a0,a,c,d)
    outvector1 = [outeriter; innerit; xval]
    outvector2 = [f0val; fval]
end
println("xval: ", round.(xval, digits=3))
println("f0val: ", round(f0val, digits=3))
println("fval: ", round.(fval, digits=3))
#---------------------------------------------------------------------

