-
Notifications
You must be signed in to change notification settings - Fork 1
/
gctoymain.jl
123 lines (119 loc) · 6.04 KB
/
gctoymain.jl
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
########################################################################################################
### 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 Nicolò 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 [email protected] or [email protected].
#
#------
#
# 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))
#---------------------------------------------------------------------