Commit fee81ae9 authored by Miroslav Shaltev's avatar Miroslav Shaltev
Browse files

q# modified: tools/SENSITIVITY/problems/styrene/black-box/truth/thermolib.hpp

parent aeb74cce
2015-03: NOMAD 3.7
-Anisotropic scaling
-Block evaluations
2013-03: NOMAD 3.6
-ortho n+1
2011-01: NOMAD 3.5
......
#######################################################################################
# #
# README #
# #
#######################################################################################
# #
# NOMAD - Nonsmooth Optimization by Mesh Adaptive Direct search #
# V 3.6.1 #
# 2013/05 #
# #
# Copyright (C) 2001-2013 #
# #
# Mark Abramson - the Boeing Company, Seattle #
# Charles Audet - Ecole Polytechnique, Montreal #
# Gilles Couture - Ecole Polytechnique, Montreal #
# John Dennis - Rice University, Houston #
# Sebastien Le Digabel - Ecole Polytechnique, Montreal #
# Christophe Tribes - Ecole Polytechnique, Montreal #
# #
#-------------------------------------------------------------------------------------#
# #
# Contact information: #
# Ecole Polytechnique de Montreal - GERAD #
# C.P. 6079, Succ. Centre-ville, Montreal (Quebec) H3C 3A7 Canada #
# e-mail: nomad@gerad.ca #
# phone : 1-514-340-6053 #6928 #
# fax : 1-514-340-5665 #
# #
# This program is free software: you can redistribute it and/or modify it under the #
# terms of the GNU Lesser General Public License as published by the Free Software #
# Foundation, either version 3 of the License, or (at your option) any later #
# version. #
# #
# This program 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 Lesser General Public License for more details. #
# #
#
# You should have received a copy of the GNU Lesser General Public License along #
# with this program. If not, see <http://www.gnu.org/licenses/>. #
# #
# You can find information on the NOMAD software at www.gerad.ca/nomad #
#######################################################################################
AUTHORS :
* Mark A. Abramson (Mark.A.Abramson@boeing.com), The Boeing Company.
* Charles Audet (www.gerad.ca/Charles.Audet), GERAD and Departement de
mathematiques et de genie industriel, ecole Polytechnique de Montreal.
* J.E. Dennis Jr. (www.caam.rice.edu/~dennis), Computational and Applied
Mathematics Department, Rice University.
* Sebastien Le Digabel (www.gerad.ca/Sebastien.Le.Digabel), GERAD and Departement
de mathematiques et de genie industriel, ecole Polytechnique de Montreal.
* Christophe Tribes, GERAD, Departement
de mathematiques et de genie industriel, Department of mechanical engineering, ecole Polytechnique de Montreal.
DESCRIPTION :
NOMAD is a C++ implementation of the Mesh Adaptive Direct Search (MADS) algorithm,
designed for constrained optimization of black-box functions.
The project started in 2001, and was funded in part by AFOSR, CRIAQ, FQRNT, LANL,
NSERC, the Boeing Company, and ExxonMobil Upstream Research Company.
WEB PAGE :
http://www.gerad.ca/nomad/
FURTHER INSTRUCTIONS :
Please visit the web page for futher instruction on the following:
* Downloading, configuring, compiling, and installing NOMAD
* Using NOMAD and setting the parameters
* Reports on NOMAD
* How to report bugs and make enhancement requests
* And more...
BATCH OR LIBRARY MODE :
NOMAD is designed to be used in two different modes : batch and library.
The batch mode is intended for a basic ans simple usage of the MADS method,
while the library mode allows more flexibility.
For example, in batch mode, users must define their separate black-box program,
that will be called with system calls by NOMAD.
In library mode, users can define their black-box function as C++ code
that will be directly called by NOMAD, without system calls and temporary files.
TYPES OF USE :
There are two ways of using NOMAD, one can directly use an executable or compile
the source code.
NOMAD batch mode executable is located in directory $NOMAD_HOME/bin or %NOMAD_HOME%\bin.
In order to avoid compiling the code, you can simply use this executable.
HOW TO EXECUTE NOMAD :
For informations about the execution of NOMAD, please read the user guide :
$NOMAD_HOME/doc/user_guide.pdf or %NOMAD_HOME%\doc\user_guide.pdf
or
http://www.gerad.ca/NOMAD/Downloads/user_guide.pdf
#######################################################################################
# #
# README #
# #
#######################################################################################
# #
# NOMAD - Nonsmooth Optimization by Mesh Adaptive Direct search #
# V 3.7.2 #
# #
# Copyright (C) 2001-2015 #
# #
# Mark Abramson - the Boeing Company, Seattle #
# Charles Audet - Ecole Polytechnique, Montreal #
# Gilles Couture - Ecole Polytechnique, Montreal #
# John Dennis - Rice University, Houston #
# Sebastien Le Digabel - Ecole Polytechnique, Montreal #
# Christophe Tribes - Ecole Polytechnique, Montreal #
# #
#-------------------------------------------------------------------------------------#
# #
# Contact information: #
# Ecole Polytechnique de Montreal - GERAD #
# C.P. 6079, Succ. Centre-ville, Montreal (Quebec) H3C 3A7 Canada #
# e-mail: nomad@gerad.ca #
# phone : 1-514-340-6053 #6928 #
# fax : 1-514-340-5665 #
# #
# This program is free software: you can redistribute it and/or modify it under the #
# terms of the GNU Lesser General Public License as published by the Free Software #
# Foundation, either version 3 of the License, or (at your option) any later #
# version. #
# #
# This program 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 Lesser General Public License for more details. #
# #
#
# You should have received a copy of the GNU Lesser General Public License along #
# with this program. If not, see <http://www.gnu.org/licenses/>. #
# #
# You can find information on the NOMAD software at www.gerad.ca/nomad #
#######################################################################################
AUTHORS :
* Mark A. Abramson (Mark.A.Abramson@boeing.com), The Boeing Company.
* Charles Audet (www.gerad.ca/Charles.Audet), GERAD and Departement de
mathematiques et de genie industriel, ecole Polytechnique de Montreal.
* J.E. Dennis Jr. (www.caam.rice.edu/~dennis), Computational and Applied
Mathematics Department, Rice University.
* Sebastien Le Digabel (www.gerad.ca/Sebastien.Le.Digabel), GERAD and Departement
de mathematiques et de genie industriel, ecole Polytechnique de Montreal.
* Christophe Tribes, GERAD, Departement
de mathematiques et de genie industriel, Department of mechanical engineering, ecole Polytechnique de Montreal.
DESCRIPTION :
NOMAD is a C++ implementation of the Mesh Adaptive Direct Search (MADS) algorithm,
designed for constrained optimization of black-box functions.
The project started in 2001, and was funded in part by AFOSR, CRIAQ, FQRNT, LANL,
NSERC, the Boeing Company, and ExxonMobil Upstream Research Company.
WEB PAGE :
http://www.gerad.ca/nomad/
FURTHER INSTRUCTIONS :
Please visit the web page for futher instruction on the following:
* Downloading, configuring, compiling, and installing NOMAD
* Using NOMAD and setting the parameters
* Reports on NOMAD
* How to report bugs and make enhancement requests
* And more...
BATCH OR LIBRARY MODE :
NOMAD is designed to be used in two different modes : batch and library.
The batch mode is intended for a basic ans simple usage of the MADS method,
while the library mode allows more flexibility.
For example, in batch mode, users must define their separate black-box program,
that will be called with system calls by NOMAD.
In library mode, users can define their black-box function as C++ code
that will be directly called by NOMAD, without system calls and temporary files.
TYPES OF USE :
There are two ways of using NOMAD, one can directly use an executable or compile
the source code.
NOMAD batch mode executable is located in directory $NOMAD_HOME/bin or %NOMAD_HOME%\bin.
In order to avoid compiling the code, you can simply use this executable.
HOW TO EXECUTE NOMAD :
For informations about the execution of NOMAD, please read the user guide :
$NOMAD_HOME/doc/user_guide.pdf or %NOMAD_HOME%\doc\user_guide.pdf
or
http://www.gerad.ca/NOMAD/Downloads/user_guide.pdf
AC_INIT(nomad, 3.6.1, miroslav.shaltev@shaltev.de)
AC_INIT(nomad, 3.7.2, miroslav.shaltev@shaltev.de)
AC_PREREQ([2.63])
AC_CONFIG_HEADERS([config.h])
......
No preview for this file type
......@@ -79,8 +79,8 @@ int main ( int argc , char ** argv ) {
in.close();
}
}
cout << g1 << " " << g2 << " " << f << endl;
cout.precision(18);
cout << g1 << " " << g2 << " " << f << endl;
return 0;
}
Warning: {
Model use is disabled for problem with categorical variables.
}
Warning: {
Anisotropic mesh are not supported with categorical and binary variables.
}
NOMAD - version 3.6.1 - www.gerad.ca/nomad
NOMAD - version 3.7.1 - www.gerad.ca/nomad
Copyright (C) 2001-2013 {
Copyright (C) 2001-2015 {
Mark A. Abramson - The Boeing Company
Charles Audet - Ecole Polytechnique de Montreal
Gilles Couture - Ecole Polytechnique de Montreal
......@@ -26,31 +29,54 @@ MADS run {
BBE ( SOL ) OBJ
1 ( 0 100.0000000000 1 100.0000000000 ) 159.6460000000
3 ( 0 100.0000000000 1 1100.0000000000 ) 150.4540000000
4 ( 0 100.0000000000 1 4100.0000000000 ) 111.1390000000
6 ( 0 100.0000000000 1 6100.0000000000 ) 85.9544000000
14 ( 0 100.0000000000 1 8100.0000000000 ) 64.1532000000
24 ( 0 100.0000000000 1 9100.0000000000 ) 55.8612000000
32 ( 0 1100.0000000000 1 8100.0000000000 ) 52.5820000000
47 ( 0 1600.0000000000 1 8100.0000000000 ) 47.6450000000
55 ( 0 1850.0000000000 1 8100.0000000000 ) 45.3357000000
72 ( 0 1853.4179687500 1 8111.7187500000 ) 45.2005000000
75 ( 0 1833.8867187500 1 8132.2265625000 ) 45.1958000000
76 ( 0 1775.2929687500 1 8193.7500000000 ) 45.1911000000
80 ( 0 1900.2929687500 1 8068.7500000000 ) 45.1620000000
89 ( 0 1923.7304687500 1 8058.9843750000 ) 45.0398000000
98 ( 0 1899.3164062500 1 8083.3984375000 ) 45.0394000000
105 ( 0 1892.4804687500 1 8106.8359375000 ) 44.8919000000
114 ( 0 1923.7304687500 1 8075.5859375000 ) 44.8905000000
137 ( 0 1922.9360580444 1 8076.6168594360 ) 44.8884000000
146 ( 0 1922.1416473389 1 8077.6477813721 ) 44.8863000000
162 ( 0 1922.1003413200 1 8077.7624607086 ) 44.8856000000
169 ( 0 1922.0590353012 1 8077.8771400452 ) 44.8850000000
171 ( 0 1921.9208717346 1 8078.0774116516 ) 44.8844000000
294 ( 0 1921.9208717346 1 8078.0774116516 ) 44.8844000000
} end of run (mesh index limits (+/- 50))
blackbox evaluations : 294
best feasible solution : ( 0 1921.920872 1 8078.077412 ) h=0 f=44.8844
1 ( 0 100.0000000000 1 100.0000000000 ) 159.6461296475
5+ 2 ( 0 1100.0000000000 2 100.0000000000 ) 152.8902369239 (ExtendedPoll)
5+ 3 ( 0 4100.0000000000 2 100.0000000000 ) 134.5175377184 (ExtendedPoll)
5+ 6 ( 0 8100.0000000000 2 1100.0000000000 ) 93.9098509400 (ExtendedPoll)
5+ 13 ( 0 3600.0000000000 2 4600.0000000000 ) 72.2375927442 (ExtendedPoll)
5+ 23 ( 0 4600.0000000000 2 4600.0000000000 ) 63.9850651149 (ExtendedPoll)
5+ 29 ( 0 4100.0000000000 2 5100.0000000000 ) 62.0438640124 (ExtendedPoll)
41+ 5 ( 0 3975.0000000000 1 5725.0000000000 ) 55.6086573370 (ExtendedPoll)
66+ 6 ( 2 3881.2500000000 1 5975.0000000000 ) 58.7730353554 (ExtendedPoll)
85+ 7 ( 0 3943.7500000000 2 5975.0000000000 ) 54.5634971325 (ExtendedPoll)
110 ( 0 3943.7500000000 1 5975.0000000000 ) 52.5611211856
114+ 12 ( 0 3914.4531250000 2 6029.6875000000 ) 54.3636860906 (ExtendedPoll)
114+ 14 ( 0 3836.3281250000 2 6131.2500000000 ) 54.2565794588 (ExtendedPoll)
114+ 26 ( 0 3863.6718750000 2 6115.6250000000 ) 54.1218203970 (ExtendedPoll)
114+ 28 ( 0 3908.5937500000 2 6072.6562500000 ) 54.0518889750 (ExtendedPoll)
114+ 29 ( 0 4043.3593750000 2 5943.7500000000 ) 53.9097569796 (ExtendedPoll)
114+ 42 ( 0 4017.9687500000 2 5979.8828125000 ) 53.8237647156 (ExtendedPoll)
166+ 12 ( 0 3945.7031250000 2 6037.5000000000 ) 53.9997469058 (ExtendedPoll)
166+ 23 ( 0 3945.2148437500 2 6053.1250000000 ) 53.8701209592 (ExtendedPoll)
199+ 8 ( 0 4100.0000000000 2 5881.2500000000 ) 53.9564900102 (ExtendedPoll)
199+ 24 ( 0 4107.8125000000 2 5881.4331054688 ) 53.8832486375 (ExtendedPoll)
199+ 26 ( 0 4121.8505859375 2 5874.7192382812 ) 53.8169487483 (ExtendedPoll)
238+ 10 ( 0 4006.2500000000 2 5980.8593750000 ) 53.9245869941 (ExtendedPoll)
238+ 18 ( 0 4030.6640625000 2 5961.3281250000 ) 53.8703341236 (ExtendedPoll)
238+ 30 ( 0 4028.8024902344 2 5968.9270019531 ) 53.8199810273 (ExtendedPoll)
238+ 38 ( 0 4026.6967773438 2 5972.2152709961 ) 53.8103680832 (ExtendedPoll)
286+ 8 ( 0 4006.2500000000 2 5973.0468750000 ) 53.9937648079 (ExtendedPoll)
286+ 19 ( 0 4006.3720703125 2 5988.6718750000 ) 53.8544733742 (ExtendedPoll)
286+ 24 ( 0 4017.6025390625 2 5977.8076171875 ) 53.8455562804 (ExtendedPoll)
286+ 25 ( 0 4051.2939453125 2 5945.2148437500 ) 53.8230307642 (ExtendedPoll)
286+ 40 ( 0 4049.3560791016 2 5948.6022949219 ) 53.8105786370 (ExtendedPoll)
286+ 48 ( 0 4049.1615295410 2 5950.5458831787 ) 53.7949509311 (ExtendedPoll)
344+ 11 ( 0 4092.1875000000 2 5889.0625000000 ) 53.9562156297 (ExtendedPoll)
344+ 26 ( 0 4082.9101562500 2 5901.6357421875 ) 53.9261842233 (ExtendedPoll)
344+ 27 ( 0 4055.0781250000 2 5939.3554687500 ) 53.8406875009 (ExtendedPoll)
344+ 41 ( 0 4054.7653198242 2 5943.2464599609 ) 53.8085676438 (ExtendedPoll)
344+ 52 ( 0 4055.7056427002 2 5943.5101509094 ) 53.7974893949 (ExtendedPoll)
344+ 55 ( 0 4057.1723937988 2 5942.2207832336 ) 53.7955084222 (ExtendedPoll)
344+ 56 ( 0 4061.5726470947 2 5938.3526802063 ) 53.7896289020 (ExtendedPoll)
416+ 8 ( 0 4092.1875000000 2 5873.4375000000 ) 54.1002366323 (ExtendedPoll)
416+24 ( 0 4104.6386718750 2 5891.7480468750 ) 53.8173417481 (ExtendedPoll)
416+33 ( 0 4111.5356445312 2 5888.0859375000 ) 53.7878645675 (ExtendedPoll)
416+47 ( 0 4112.3014450073 2 5887.4798774719 ) 53.7864363345 (ExtendedPoll)
475 ( 0 4004.2968750000 1 5986.7187500000 ) 51.9855654455
481+19 ( 0 3994.9005859375 2 5999.2932421875 ) 53.8684879252 (ExtendedPoll)
500 ( 0 4004.2968750000 1 5986.7187500000 ) 51.9855654455
} end of run (max number of blackbox evaluations)
blackbox evaluations : 500
best feasible solution : ( 0 4004.296875 1 5986.71875 ) h=0 f=51.98556545
......@@ -46,10 +46,6 @@ class My_Evaluator : public Multi_Obj_Evaluator {
public:
My_Evaluator ( const Parameters & p ) :
Multi_Obj_Evaluator ( p ) {
}
~My_Evaluator ( void ) {}
......@@ -263,12 +259,10 @@ My_Extended_Poll::My_Extended_Poll ( Parameters & p )
bbit_1[0] = bbit_1[1] = CATEGORICAL;
bbit_1[2] = CONTINUOUS;
const Point & d0_1 = p.get_initial_mesh_size();
const Point & d0_1 = p.get_initial_poll_size();
const Point & lb_1 = p.get_lb();
const Point & ub_1 = p.get_ub();
int halton_seed = p.get_halton_seed();
_s1 = new Signature ( 3 ,
bbit_1 ,
d0_1 ,
......@@ -276,7 +270,6 @@ My_Extended_Poll::My_Extended_Poll ( Parameters & p )
ub_1 ,
p.get_direction_types () ,
p.get_sec_poll_dir_types() ,
halton_seed++ ,
_p.out() );
// signature for 2 assets:
......@@ -302,7 +295,6 @@ My_Extended_Poll::My_Extended_Poll ( Parameters & p )
ub_2 ,
p.get_direction_types () ,
p.get_sec_poll_dir_types() ,
halton_seed++ ,
_p.out() );
}
......@@ -329,7 +321,6 @@ My_Extended_Poll::My_Extended_Poll ( Parameters & p )
ub_3 ,
p.get_direction_types () ,
p.get_sec_poll_dir_types() ,
halton_seed ,
_p.out() );
}
}
......
Warning: {
Model use is disabled for problem with categorical variables.
}
Warning: {
Default anisotropic mesh is disabled with categorical and binary variables.
}
Warning: {
Model use is disabled in parallel mode (MPI).
}
Warning: {
Asynchronous mode is disabled in parallel mode (MPI) when dynamic directions (ortho n+1) are used.
}
multi-MADS run {
MADS run 1/30 ...... OK [bb eval= 60] [overall bb eval= 60] [# dominant pts= 5] [# new pts= 5] [f1=52.02241823 f2=52.95541823]
MADS run 2/30 ...... OK [bb eval= 66] [overall bb eval= 126] [# dominant pts= 5] [# new pts= 0] [f1=52.02241823 f2=51.05441823]
MADS run 3/30 ...... OK [bb eval= 57] [overall bb eval= 183] [# dominant pts= 6] [# new pts= 1] [f1=52.02241823 f2=51.56141823]
MADS run 4/30 ...... OK [bb eval= 49] [overall bb eval= 232] [# dominant pts= 6] [# new pts= 0] [f1=52.02241823 f2=51.70341823]
MADS run 5/30 ...... OK [bb eval=181] [overall bb eval= 413] [# dominant pts= 5] [# new pts= -1] [f1=44.95823059 f2=43.96223059]
MADS run 6/30 ...... OK [bb eval=203] [overall bb eval= 616] [# dominant pts= 5] [# new pts= 0] [f1=44.90686591 f2=43.90786591]
MADS run 7/30 ...... OK [bb eval=138] [overall bb eval= 754] [# dominant pts= 8] [# new pts= 3] [f1=44.90641676 f2=43.90741676]
MADS run 8/30 ...... OK [bb eval=124] [overall bb eval= 878] [# dominant pts= 8] [# new pts= 0] [f1=44.90640745 f2=43.91240745]
MADS run 9/30 ...... OK [bb eval=180] [overall bb eval= 1058] [# dominant pts= 8] [# new pts= 0] [f1=44.90640744 f2=43.91440744]
MADS run 10/30 ...... OK [bb eval= 96] [overall bb eval= 1154] [# dominant pts= 8] [# new pts= 0] [f1=44.90640744 f2=43.91340744]
MADS run 11/30 ...... OK [bb eval=168] [overall bb eval= 1322] [# dominant pts= 11] [# new pts= 3] [f1=44.90640744 f2=43.97340744]
MADS run 12/30 ...... OK [bb eval=201] [overall bb eval= 1523] [# dominant pts= 10] [# new pts= -1] [f1=44.89376891 f2=43.89876891]
MADS run 13/30 ...... OK [bb eval=204] [overall bb eval= 1727] [# dominant pts= 12] [# new pts= 2] [f1=44.88950881 f2=43.90550881]
MADS run 14/30 ...... OK [bb eval=202] [overall bb eval= 1929] [# dominant pts= 10] [# new pts= -2] [f1=44.88548533 f2=43.90048533]
MADS run 15/30 ...... OK [bb eval=212] [overall bb eval= 2141] [# dominant pts= 10] [# new pts= 0] [f1=44.88519731 f2=43.88619731]
MADS run 16/30 ...... OK [bb eval=209] [overall bb eval= 2350] [# dominant pts= 9] [# new pts= -1] [f1=44.88510307 f2=43.91210307]
MADS run 17/30 ...... OK [bb eval=203] [overall bb eval= 2553] [# dominant pts= 9] [# new pts= 0] [f1=44.88508265 f2=43.90208265]
MADS run 18/30 ...... OK [bb eval=239] [overall bb eval= 2792] [# dominant pts= 9] [# new pts= 0] [f1=44.88508168 f2=43.90108168]
MADS run 19/30 ...... OK [bb eval=222] [overall bb eval= 3014] [# dominant pts= 9] [# new pts= 0] [f1=44.8850816 f2=43.9070816]
MADS run 20/30 ...... OK [bb eval=207] [overall bb eval= 3221] [# dominant pts= 8] [# new pts= -1] [f1=44.8850816 f2=43.9610816]
MADS run 21/30 ...... OK [bb eval=180] [overall bb eval= 3401] [# dominant pts= 6] [# new pts= -2] [f1=44.8850816 f2=43.9010816]
MADS run 22/30 ...... OK [bb eval=174] [overall bb eval= 3575] [# dominant pts= 11] [# new pts= 5] [f1=44.8850816 f2=44.0440816]
MADS run 23/30 ...... OK [bb eval=176] [overall bb eval= 3751] [# dominant pts= 9] [# new pts= -2] [f1=44.8850816 f2=43.9800816]
MADS run 24/30 ...... OK [bb eval=187] [overall bb eval= 3938] [# dominant pts= 8] [# new pts= -1] [f1=44.8850816 f2=44.1020816]
MADS run 25/30 ...... OK [bb eval=166] [overall bb eval= 4104] [# dominant pts= 10] [# new pts= 2] [f1=44.8850816 f2=43.9460816]
MADS run 26/30 ...... OK [bb eval=176] [overall bb eval= 4280] [# dominant pts= 11] [# new pts= 1] [f1=44.8850816 f2=43.9870816]
MADS run 27/30 ...... OK [bb eval= 2] [overall bb eval= 4282] [# dominant pts= 11] [# new pts= 0] [f1=44.8850816 f2=43.9460816]
MADS run 28/30 ...... OK [bb eval= 0] [overall bb eval= 4282] [# dominant pts= 11] [# new pts= 0] [f1=44.8850816 f2=43.9870816]
MADS run 29/30 ...... OK [bb eval= 0] [overall bb eval= 4282] [# dominant pts= 11] [# new pts= 0] [f1=44.8850816 f2=43.9460816]
MADS run 30/30 ...... OK [bb eval= 0] [overall bb eval= 4282] [# dominant pts= 11] [# new pts= 0] [f1=44.8850816 f2=43.9870816]
} end of run (max number of MADS runs)
blackbox evaluations : 4282
number of MADS runs : 30
MADS run 1 ...... OK [bb eval= 78] [overall bb eval= 78] [# dominant pts= 2] [# new pts= 2] [f1=52.02241823 f2=51.31041823]
MADS run 2 ...... OK [bb eval= 39] [overall bb eval= 117] [# dominant pts= 3] [# new pts= 1] [f1=52.0224221 f2=51.0754221]
MADS run 3 ...... OK [bb eval= 39] [overall bb eval= 156] [# dominant pts= 3] [# new pts= 0] [f1=52.02242186 f2=51.23342186]
MADS run 4 ...... OK [bb eval= 38] [overall bb eval= 194] [# dominant pts= 4] [# new pts= 1] [f1=52.02242186 f2=51.06242186]
MADS run 5 ...... OK [bb eval= 39] [overall bb eval= 233] [# dominant pts= 5] [# new pts= 1] [f1=52.02242186 f2=51.19342186]
MADS run 6 ...... OK [bb eval= 7] [overall bb eval= 240] [# dominant pts= 5] [# new pts= 0] [f1=52.02242186 f2=51.19342186]
MADS run 7 ...... OK [bb eval= 39] [overall bb eval= 279] [# dominant pts= 2] [# new pts= -3] [f1=45.36506988 f2=44.51306988]
MADS run 8 ...... OK [bb eval= 39] [overall bb eval= 318] [# dominant pts= 3] [# new pts= 1] [f1=45.36506988 f2=44.51306988]
MADS run 9 ...... OK [bb eval= 39] [overall bb eval= 357] [# dominant pts= 7] [# new pts= 4] [f1=44.99982416 f2=44.55082416]
MADS run 10 ...... OK [bb eval= 39] [overall bb eval= 396] [# dominant pts= 4] [# new pts= -3] [f1=44.97647101 f2=44.00347101]
MADS run 11 ...... OK [bb eval= 39] [overall bb eval= 435] [# dominant pts= 4] [# new pts= 0] [f1=44.9661848 f2=44.1371848]
MADS run 12 ...... OK [bb eval= 39] [overall bb eval= 474] [# dominant pts= 5] [# new pts= 1] [f1=44.9661848 f2=44.1371848]
MADS run 13 ...... OK [bb eval= 26] [overall bb eval= 500] [# dominant pts= 5] [# new pts= 0] [f1=44.96576761 f2=45.17076761]
} end of run (max number of bb evaluations)
blackbox evaluations : 500
number of MADS runs : 13
Pareto front {
44.8849906075 45.5549906075
44.8850816013 45.5000816013
44.8850816013 45.2800816013
44.8850816013 44.2430816013
44.8850816013 43.9800816013
44.8850816013 43.9260816013
44.8850816013 43.9020816013
44.8850816013 43.9010816013
44.8850816075 43.8900816075
44.8850816076 43.8890816076
44.8851973070 43.8861973070
44.9348019953 45.4168019953
44.9657676108 45.1707676108
44.9661847951 44.1371847951
44.9738637269 44.0708637269
44.9764710086 44.0034710086
}
number of Pareto points: 11
number of Pareto points: 5
......@@ -44,404 +44,415 @@
using namespace std;
using namespace NOMAD;
#define USE_SURROGATE false
#define USE_SURROGATE false
/*----------------------------------------*/
/* the problem */
/*----------------------------------------*/
class My_Evaluator : public Evaluator {
class My_Evaluator : public Evaluator
{
public:
My_Evaluator ( const Parameters & p ) :
My_Evaluator ( const Parameters & p ) :
Evaluator ( p ) {}
~My_Evaluator ( void ) {}
bool eval_x ( Eval_Point & x ,
const Double & h_max ,
bool & count_eval ) const;
~My_Evaluator ( void ) {}
bool eval_x ( Eval_Point & x ,
const Double & h_max ,
bool & count_eval ) const;
};
/*--------------------------------------------------*/
/* user class to define categorical neighborhoods */
/*--------------------------------------------------*/
class My_Extended_Poll : public Extended_Poll {
class My_Extended_Poll : public Extended_Poll
{
private:
// signatures for 1, 2, and 3 assets:
Signature * _s1 , * _s2 , * _s3;
// signatures for 1, 2, and 3 assets:
Signature * _s1 , * _s2 , * _s3;
public:
// constructor:
My_Extended_Poll ( Parameters & );
// destructor:
virtual ~My_Extended_Poll ( void ) { delete _s1; delete _s2; delete _s3; }
// construct the extended poll points:
virtual void construct_extended_points ( const Eval_Point & );
// constructor:
My_Extended_Poll ( Parameters & );
// destructor:
virtual ~My_Extended_Poll ( void ) { delete _s1; delete _s2; delete _s3; }
// construct the extended poll points:
virtual void construct_extended_points ( const Eval_Point & );
};
/*------------------------------------------*/
/* NOMAD main function */
/*------------------------------------------*/
int main ( int argc , char ** argv ) {
// NOMAD initializations:
begin ( argc , argv );
// display:
Display out ( cout );
out.precision ( DISPLAY_PRECISION_STD );
// check the number of processess:
int main ( int argc , char ** argv )
{
// NOMAD initializations:
begin ( argc , argv );
// display:
Display out ( cout );
out.precision ( DISPLAY_PRECISION_STD );
// check the number of processess:
#ifdef USE_MPI
if ( Slave::get_nb_processes() < 2 ) {
if ( Slave::is_master() )
cerr << "usage: \'mpirun -np p ./categorical\' with p>1"
<< endl;
end();
return EXIT_FAILURE;
}
if ( Slave::get_nb_processes() < 2 )
{
if ( Slave::is_master() )
cerr << "usage: \'mpirun -np p ./categorical\' with p>1"
<< endl;
end();
return EXIT_FAILURE;
}
#endif
try {
// parameters creation:
Parameters p ( out );
if ( USE_SURROGATE )
p.set_HAS_SGTE ( true );
// p.set_DISPLAY_DEGREE ( FULL_DISPLAY );
p.set_MAX_BB_EVAL ( 200 );
p.set_DIMENSION (3);
vector<bb_output_type> bbot (3);
bbot[0] = EB; // budget constraint
bbot[1] = EB; // total value >= 1$