Skip to content
Snippets Groups Projects
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
No related branches found
No related tags found
No related merge requests found
Expand all Show whitespace changes
Inline Side-by-side
Showing
with 1635 additions and 812 deletions
ChangeLog
+ 3
− 0
View file @ fee81ae9
2015-03: NOMAD 3.7
-Anisotropic scaling
-Block evaluations
2013-03: NOMAD 3.6
-ortho n+1
2011-01: NOMAD 3.5
......
README
+ 114
− 115
View file @ fee81ae9
......@@ -5,10 +5,9 @@
#######################################################################################
# #
# NOMAD - Nonsmooth Optimization by Mesh Adaptive Direct search #
# V 3.6.1 #
# 2013/05 #
# V 3.7.2 #
# #
# Copyright (C) 2001-2013 #
# Copyright (C) 2001-2015 #
# #
# Mark Abramson - the Boeing Company, Seattle #
# Charles Audet - Ecole Polytechnique, Montreal #
......
configure.ac
+ 1
− 1
View file @ fee81ae9
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])
......
doc/user_guide.pdf
+ 0
− 0
View file @ fee81ae9
No preview for this file type
examples/advanced/categorical/batch/bb.cpp
+ 2
− 2
View file @ fee81ae9
......@@ -79,7 +79,7 @@ int main ( int argc , char ** argv ) {
in.close();
}
}
cout.precision(18);
cout << g1 << " " << g2 << " " << f << endl;
return 0;
......
examples/advanced/categorical/batch/runExample.log
+ 56
− 30
View file @ fee81ae9
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
examples/advanced/categorical/bi_obj/categorical.cpp
+ 1
− 10
View file @ fee81ae9
......@@ -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() );
}
}
......
examples/advanced/categorical/bi_obj/runExample.log
+ 34
− 48
View file @ fee81ae9
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
examples/advanced/categorical/single_obj/categorical.cpp
+ 370
− 359
View file @ fee81ae9
......@@ -49,7 +49,8 @@ using namespace NOMAD;
/*----------------------------------------*/
/* the problem */
/*----------------------------------------*/
class My_Evaluator : public Evaluator {
class My_Evaluator : public Evaluator
{
public:
My_Evaluator ( const Parameters & p ) :
Evaluator ( p ) {}
......@@ -64,7 +65,8 @@ public:
/*--------------------------------------------------*/
/* user class to define categorical neighborhoods */
/*--------------------------------------------------*/
class My_Extended_Poll : public Extended_Poll {
class My_Extended_Poll : public Extended_Poll
{
private:
......@@ -87,7 +89,8 @@ public:
/*------------------------------------------*/
/* NOMAD main function */
/*------------------------------------------*/
int main ( int argc , char ** argv ) {
int main ( int argc , char ** argv )
{
// NOMAD initializations:
begin ( argc , argv );
......@@ -98,7 +101,8 @@ int main ( int argc , char ** argv ) {
// check the number of processess:
#ifdef USE_MPI
if ( Slave::get_nb_processes() < 2 ) {
if ( Slave::get_nb_processes() < 2 )
{
if ( Slave::is_master() )
cerr << "usage: \'mpirun -np p ./categorical\' with p>1"
<< endl;
......@@ -107,7 +111,8 @@ int main ( int argc , char ** argv ) {
}
#endif
try {
try
{
// parameters creation:
Parameters p ( out );
......@@ -116,7 +121,6 @@ int main ( int argc , char ** argv ) {
p.set_HAS_SGTE ( true );
// p.set_DISPLAY_DEGREE ( FULL_DISPLAY );
p.set_MAX_BB_EVAL ( 200 );
p.set_DIMENSION (3);
......@@ -126,6 +130,7 @@ int main ( int argc , char ** argv ) {
bbot[2] = OBJ; // objective
p.set_BB_OUTPUT_TYPE ( bbot );
// categorical variables:
p.set_BB_INPUT_TYPE ( 0 , CATEGORICAL );
p.set_BB_INPUT_TYPE ( 1 , CATEGORICAL );
......@@ -181,15 +186,19 @@ int main ( int argc , char ** argv ) {
/*----------------------------------------------------*/
bool My_Evaluator::eval_x ( Eval_Point & x ,
const Double & h_max ,
bool & count_eval ) const {
bool & count_eval ) const
{
// number of assets:
int n = static_cast<int> ( x[0].value() );
count_eval=false;
// get the asset types and values:
Point v ( 3 , 0.0 );
Double vmin = 10000 , tmp;
for ( int i = 0 ; i < n ; ++i ) {
for ( int i = 0 ; i < n ; ++i )
{
tmp = v [ static_cast<int> ( x[2*i+1].value() ) ] = x[2*i+2];
if ( tmp < vmin )
vmin = tmp;
......@@ -201,7 +210,8 @@ bool My_Evaluator::eval_x ( Eval_Point & x ,
x.set_bb_output ( 0 , h );
x.set_bb_output ( 1 , 1-vmin );
if ( h <= 0 && vmin >= 1 ) {
if ( h <= 0 && vmin >= 1 )
{
// compute the risk and revenue:
Double vt2 = vt.pow2();
......@@ -226,7 +236,8 @@ bool My_Evaluator::eval_x ( Eval_Point & x ,
x.set_bb_output ( 2 , 145 );
// simulation of a surrogate:
if ( USE_SURROGATE && x.get_eval_type() == SGTE ) {
if ( USE_SURROGATE && x.get_eval_type() == SGTE )
{
Double f = x.get_bb_outputs()[2];
f.round();
f += ( (rand()%2) ? -1.0 : 1.0 ) * ( (rand()%1000) / 1000.0 );
......@@ -252,13 +263,10 @@ 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 ,
......@@ -266,7 +274,6 @@ 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:
......@@ -293,7 +300,6 @@ My_Extended_Poll::My_Extended_Poll ( Parameters & p )
ub_2 ,
p.get_direction_types () ,
p.get_sec_poll_dir_types() ,
halton_seed++ ,
_p.out() );
}
......@@ -321,7 +327,6 @@ My_Extended_Poll::My_Extended_Poll ( Parameters & p )
ub_3 ,
p.get_direction_types () ,
p.get_sec_poll_dir_types() ,
halton_seed ,
_p.out() );
}
}
......@@ -330,20 +335,23 @@ My_Extended_Poll::My_Extended_Poll ( Parameters & p )
/* construct the extended poll points */
/* (categorical neighborhoods) */
/*--------------------------------------*/
void My_Extended_Poll::construct_extended_points ( const Eval_Point & x ) {
void My_Extended_Poll::construct_extended_points ( const Eval_Point & x )
{
// number of assets:
int n = static_cast<int> ( x[0].value() );
// 1 asset:
// --------
if ( n==1 ) {
if ( n==1 )
{
int cur_type = static_cast<int> ( x[1].value() );
// this vector contains the types of the other assets:
vector<int> other_types;
switch ( cur_type ) {
switch ( cur_type )
{
case 0:
case 2:
other_types.push_back(1);
......@@ -354,7 +362,8 @@ void My_Extended_Poll::construct_extended_points ( const Eval_Point & x ) {
}
// add 1 asset (1 or 2 neighbors):
for ( size_t k = 0 ; k < other_types.size() ; ++k ) {
for ( size_t k = 0 ; k < other_types.size() ; ++k )
{
Point y (5);
y[0] = 2;
y[1] = cur_type;
......@@ -365,7 +374,8 @@ void My_Extended_Poll::construct_extended_points ( const Eval_Point & x ) {
}
// change the type of the asset to the other types (1 or 2 neighbors):
for ( size_t k = 0 ; k < other_types.size() ; ++k ) {
for ( size_t k = 0 ; k < other_types.size() ; ++k )
{
Point y = x ;
y[1] = other_types[k];
......@@ -375,7 +385,8 @@ void My_Extended_Poll::construct_extended_points ( const Eval_Point & x ) {
// 2 assets:
// ---------
else if ( n == 2 ) {
else if ( n == 2 )
{
int other_type = static_cast<int> ( (3 - x[1] - x[3]).value() );
......
examples/advanced/categorical/single_obj/runExample.log
+ 751
− 26
View file @ fee81ae9
This diff is collapsed.
examples/advanced/multi_start/best_x.txt
+ 1
− 1
View file @ fee81ae9
1.001496962026439119242127162579 0.00098876953124999995663191310057982 1.0009373918174639950251503250911 1.0017943110223916924894638214028 1.0001091153523589127871673554182
\ No newline at end of file
1.0065227779901828597530766273849 0.0061576008796691908409037807814457 0.99995827823866767491978180260048 1.0093326058145666301868459413527 1.0032484173774720570548879550188
\ No newline at end of file
examples/advanced/multi_start/param.txt
+ 1
− 3
View file @ fee81ae9
MAX_CACHE_MEMORY 750
seed none
#initial_mesh_size r0.1
initial_mesh_size r0.1
opportunistic_eval no
......
examples/advanced/multi_start/runExample.log
+ 16
− 13
View file @ fee81ae9
starting point # 0: ( 1.442943417 0.369140625 0.9823972059 0.8663984208 1.488629702 )
starting point # 1: ( 4.046907118 0.2179386209 0.3995457903 2.86002185 2.869872299 )
starting point # 2: ( 2.730496038 4.019688198 2.015450408 3.887235003 1.919237215 )
starting point # 3: ( 1.986593711 1.814325217 1.166098379 0.9517739273 0.4086947283 )
starting point # 4: ( 1.072412482 2.713920967 3.075009451 0.3310437183 1.032300187 )
starting point # 0: ( 1.311638583 0.3116009987 0.9520390845 0.8088161243 1.540724398 1.619263445 0.9519058983 )
starting point # 1: ( 4.046907118 0.2179386209 0.3995457903 2.86002185 2.869872299 1.805805862 0.9241232544 )
starting point # 2: ( 2.730496038 4.019688198 2.015450408 3.887235003 1.919237215 0.004176816764 2.788539635 )
starting point # 3: ( 1.986593711 1.814325217 1.166098379 0.9517739273 0.4086947283 3.9836116 2.239536416 )
starting point # 4: ( 1.072412482 2.713920967 3.075009451 0.3310437183 1.032300187 3.079123794 3.985637823 )
run # 0: f=1.506294281
run # 1: f=1.415934966
run # 2: f=2.001223577
run # 3: f=1.799944829
run # 4: f=2.61048503
Warning: {
Anisotropic mesh is disabled for direction types other than OrthoMads.
}
run # 0: f=1.619155361
run # 1: f=1.890750373
run # 2: f=2.267307514
run # 3: f=1.998968772
run # 4: f=2.24968126
bb eval : 5000
best : 1.415934966
worst : 2.61048503
solution: x = ( 1.001496962 0.0009887695312 1.000937392 1.001794311 1.000109115 ) f(x) = 1.415934966
best : 1.619155361
worst : 2.267307514
solution: x = ( 1.310592306 0.3106177009 0.9507953978 0.8088442339 1.538847818 1.618878923 0.951419131 ) f(x) = 1.619155361
examples/advanced/restart/restart.cpp
+ 24
− 33
View file @ fee81ae9
......@@ -11,31 +11,13 @@ using namespace NOMAD;
class My_Evaluator : public Evaluator {
private:
double _mesh_update_basis;
int _initial_mesh_index;
int _mesh_index;
Point _initial_mesh_size;
public:
My_Evaluator ( const Parameters & p ) :
Evaluator ( p ) ,
_mesh_update_basis ( p.get_mesh_update_basis().value() ) ,
_initial_mesh_index ( p.get_initial_mesh_index() ) ,
_mesh_index ( _initial_mesh_index ) ,
_initial_mesh_size ( p.get_initial_mesh_size() ) {}
~My_Evaluator ( void ) {}
My_Evaluator ( const Parameters & p ) : Evaluator ( p ) { }
int get_mesh_index ( void ) const { return _mesh_index; }
void get_mesh_size ( Point & mesh_size ) const
{
Mesh::get_delta_m ( mesh_size ,
_initial_mesh_size ,
_mesh_update_basis ,
_initial_mesh_index ,
_mesh_index );
}
~My_Evaluator ( void ) {}
virtual bool eval_x ( Eval_Point & x ,
const Double & h_max ,
......@@ -82,7 +64,7 @@ void My_Evaluator::update_iteration ( success_type success ,
const Pareto_Front & pareto_front ,
bool & stop )
{
_mesh_index = Mesh::get_mesh_index();
if ( success == UNSUCCESSFUL )
stop = true;
}
......@@ -112,7 +94,7 @@ int main ( int argc , char ** argv )
bbot[2] = EB;
p.set_BB_OUTPUT_TYPE ( bbot );
// p.set_DISPLAY_DEGREE ( FULL_DISPLAY );
p.set_DISPLAY_DEGREE ( 2 );
p.set_DISPLAY_STATS ( "bbe ( sol ) obj" );
......@@ -128,9 +110,13 @@ int main ( int argc , char ** argv )
p.set_MAX_BB_EVAL (100); // the algorithm terminates after
// 100 black-box evaluations
// parameters validation:
p.check();
OrthogonalMesh * oMesh=p.get_signature()->get_mesh();
// custom evaluator creation:
My_Evaluator ev ( p );
......@@ -140,8 +126,10 @@ int main ( int argc , char ** argv )
// algorithm creation:
Mads mads ( p , &ev );
// successive runs:
for ( int i = 0 ; i < 5 ; ++i ) {
for ( int i = 0 ; i < 5 ; ++i )
{
out << endl << open_block ( "MADS run #" + NOMAD::itos(i) );
......@@ -161,18 +149,21 @@ int main ( int argc , char ** argv )
else
p.set_LH_SEARCH(0,0);
// initial mesh:
p.set_INITIAL_MESH_INDEX ( ev.get_mesh_index() );
Point initial_mesh_size;
ev.get_mesh_size ( initial_mesh_size );
p.set_INITIAL_MESH_SIZE ( initial_mesh_size );
// Update the mesh for an unsuccessful iteration and put current
// mesh and poll sizes as initial mesh and poll sizes for the next start.
oMesh->update(UNSUCCESSFUL);
Point delta_0, Delta_0;
oMesh->get_delta(delta_0);
oMesh->set_delta_0(delta_0);
oMesh->get_Delta(Delta_0);
oMesh->set_Delta_0(Delta_0);
// parameters validation:
p.check();
// reset the Mads object:
mads.reset ( true , true );
}
// the run:
......
examples/advanced/restart/runExample.log
+ 28
− 23
View file @ fee81ae9
......@@ -5,15 +5,15 @@ MADS run {
BBE ( SOL ) OBJ
2 ( 1.1000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000 ) 275.2281000000 (PhaseOne)
3 ( 4.4000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000 ) 0.0000000000 (PhaseOne)
3 ( 4.4000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000 ) 0.0000000000
9 ( 4.4000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000 ) 0.0000000000
2 ( 0.0000000000 0.5366563146 0.5813776741 -0.2683281573 0.2683281573 ) 288.7937255854 (PhaseOne)
3 ( 0.0000000000 2.1466252584 2.3255106966 -1.0733126292 1.0733126292 ) 0.0000000000 (PhaseOne)
3 ( 0.0000000000 2.1466252584 2.3255106966 -1.0733126292 1.0733126292 ) 1.0733126292
9 ( 0.0000000000 2.1466252584 2.3255106966 -1.0733126292 1.0733126292 ) 1.0733126292
} end of run (terminated by the user inside Evaluator::update_iteration())
blackbox evaluations : 9
best feasible solution : ( 4.4 0 0 0 0 ) h=0 f=0
best feasible solution : ( 0 2.146625258 2.325510697 -1.073312629 1.073312629 ) h=0 f=1.073312629
}
MADS run #1 {
......@@ -22,13 +22,18 @@ MADS run {
BBE ( SOL ) OBJ
9 ( 4.4000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000 ) 0.0000000000
15 ( 4.4000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000 ) 0.0000000000
9 ( -0.4919349550 2.6832815730 2.9068883707 -0.5366563146 1.0733126292 ) 1.0733126292
11 ( -0.4919349550 3.0857738089 2.9068883707 -0.5366563146 0.8720665112 ) 0.8720665112
12 ( -0.4919349550 4.2932505168 2.9068883707 -0.5366563146 0.2683281573 ) 0.2683281573
20 ( 0.0000000000 3.6224301235 2.6161995337 -0.0670820393 -1.0733126292 ) -1.0733126292
27 ( -0.2459674775 4.1590864381 2.9068883707 -0.6037383539 -1.4087228258 ) -1.4087228258
29 ( 2.4596747752 1.4758048651 0.7267220927 -0.6708203932 -3.4211840056 ) -3.4211840056
36 ( 2.4596747752 1.4758048651 0.7267220927 -0.6708203932 -3.4211840056 ) -3.4211840056
} end of run (terminated by the user inside Evaluator::update_iteration())
blackbox evaluations : 15
best feasible solution : ( 4.4 0 0 0 0 ) h=0 f=0
blackbox evaluations : 36
best feasible solution : ( 2.459674775 1.475804865 0.7267220927 -0.6708203932 -3.421184006 ) h=0 f=-3.421184006
}
MADS run #2 {
......@@ -37,14 +42,13 @@ MADS run {
BBE ( SOL ) OBJ
15 ( 4.4000000000 -0.6000000000 0.0000000000 0.0000000000 0.0000000000 ) 0.0000000000
16 ( 4.5375000000 -0.1500000000 0.4875000000 0.7500000000 -1.5000000000 ) -1.5000000000
23 ( 4.5375000000 -0.1500000000 0.4875000000 0.7500000000 -1.5000000000 ) -1.5000000000
36 ( 2.4596747752 1.4758048651 0.7267220927 -0.6708203932 -3.4211840056 ) -3.4211840056
42 ( 2.4596747752 1.4758048651 0.7267220927 -0.6708203932 -3.4211840056 ) -3.4211840056
} end of run (terminated by the user inside Evaluator::update_iteration())
blackbox evaluations : 23
best feasible solution : ( 4.5375 -0.15 0.4875 0.75 -1.5 ) h=0 f=-1.5
blackbox evaluations : 42
best feasible solution : ( 2.459674775 1.475804865 0.7267220927 -0.6708203932 -3.421184006 ) h=0 f=-3.421184006
}
MADS run #3 {
......@@ -53,14 +57,13 @@ MADS run {
BBE ( SOL ) OBJ
23 ( 4.5375000000 -0.7500000000 0.4875000000 0.7500000000 -1.5000000000 ) -1.5000000000
30 ( 4.1250000000 -0.9750000000 0.3250000000 0.7500000000 -2.2500000000 ) -2.2500000000
38 ( 4.1250000000 -0.9750000000 0.3250000000 0.7500000000 -2.2500000000 ) -2.2500000000
42 ( 2.4596747752 1.4758048651 0.7267220927 -0.6708203932 -3.4211840056 ) -3.4211840056
48 ( 2.4596747752 1.4758048651 0.7267220927 -0.6708203932 -3.4211840056 ) -3.4211840056
} end of run (terminated by the user inside Evaluator::update_iteration())
blackbox evaluations : 38
best feasible solution : ( 4.125 -0.975 0.325 0.75 -2.25 ) h=0 f=-2.25
blackbox evaluations : 48
best feasible solution : ( 2.459674775 1.475804865 0.7267220927 -0.6708203932 -3.421184006 ) h=0 f=-3.421184006
}
MADS run #4 {
......@@ -69,11 +72,13 @@ MADS run {
BBE ( SOL ) OBJ
38 ( 3.5750000000 -0.9750000000 0.3250000000 0.7500000000 -2.2500000000 ) -2.2500000000
44 ( 3.5750000000 -0.9750000000 0.3250000000 0.7500000000 -2.2500000000 ) -2.2500000000
48 ( 2.4596747752 1.4758048651 0.7267220927 -0.6708203932 -3.4211840056 ) -3.4211840056
52 ( 2.4750477426 1.4674196102 0.8902345635 0.5282710597 -3.4505323978 ) -3.4505323978
54 ( 0.9915563938 0.9978453350 1.0174109298 1.0230010997 -3.9997665948 ) -3.9997665948
61 ( 0.9915563938 0.9978453350 1.0174109298 1.0230010997 -3.9997665948 ) -3.9997665948
} end of run (terminated by the user inside Evaluator::update_iteration())
blackbox evaluations : 44
best feasible solution : ( 3.575 -0.975 0.325 0.75 -2.25 ) h=0 f=-2.25
blackbox evaluations : 61
best feasible solution : ( 0.9915563938 0.997845335 1.01741093 1.0230011 -3.999766595 ) h=0 f=-3.999766595
}
examples/advanced/user_search/runExample.log
+ 72
− 18
View file @ fee81ae9
......@@ -6,10 +6,16 @@ MADS search {
list of points evaluation (user search) {
evaluation 1/1 {
submitted evaluation 1/1 {
point #1 ( 0.132741232 )
mesh indices: ( 0 )
}
evaluation 1/1 {
point #1
user search dominating point #1 {
x = ( 0.132741232 )
......@@ -33,10 +39,16 @@ MADS search {
list of points evaluation (user search) {
evaluation 1/1 {
submitted evaluation 1/1 {
point #2 ( 0.061929856 )
mesh indices: ( 0 )
}
evaluation 1/1 {
point #2
user search dominating point #2 {
x = ( 0.061929856 )
......@@ -60,10 +72,16 @@ MADS search {
list of points evaluation (user search) {
evaluation 1/1 {
submitted evaluation 1/1 {
point #3 ( 0.052807552 )
mesh indices: ( 0 )
}
evaluation 1/1 {
point #3
user search dominating point #3 {
x = ( 0.052807552 )
......@@ -87,10 +105,16 @@ MADS search {
list of points evaluation (user search) {
evaluation 1/1 {
submitted evaluation 1/1 {
point #4 ( 0.003343488002 )
mesh indices: ( 0 )
}
evaluation 1/1 {
point #4
user search dominating point #4 {
x = ( 0.003343488002 )
......@@ -114,10 +138,16 @@ MADS search {
list of points evaluation (user search) {
evaluation 1/1 {
submitted evaluation 1/1 {
point #5 ( 0.003046400592 )
mesh indices: ( 0 )
}
evaluation 1/1 {
point #5
user search dominating point #5 {
x = ( 0.003046400592 )
......@@ -141,10 +171,16 @@ MADS search {
list of points evaluation (user search) {
evaluation 1/1 {
submitted evaluation 1/1 {
point #6 ( 0.002265794838 )
mesh indices: ( 0 )
}
evaluation 1/1 {
point #6
user search dominating point #6 {
x = ( 0.002265794838 )
......@@ -168,10 +204,16 @@ MADS search {
list of points evaluation (user search) {
evaluation 1/1 {
submitted evaluation 1/1 {
point #7 ( 0.001481318181 )
mesh indices: ( 0 )
}
evaluation 1/1 {
point #7
user search dominating point #7 {
x = ( 0.001481318181 )
......@@ -195,10 +237,16 @@ MADS search {
list of points evaluation (user search) {
evaluation 1/1 {
submitted evaluation 1/1 {
point #8 ( 0.001371090082 )
mesh indices: ( 0 )
}
evaluation 1/1 {
point #8
user search dominating point #8 {
x = ( 0.001371090082 )
......@@ -222,10 +270,16 @@ MADS search {
list of points evaluation (user search) {
evaluation 1/1 {
submitted evaluation 1/1 {
point #9 ( 0.0008957599348 )
mesh indices: ( 0 )
}
evaluation 1/1 {
point #9
user search dominating point #9 {
x = ( 0.0008957599348 )
......
examples/advanced/user_search/user_search.cpp
+ 23
− 0
View file @ fee81ae9
......@@ -101,6 +101,29 @@ void My_Search::search ( Mads & mads ,
(*tk)[0] = static_cast<int>(ceil(1.0/xk)) * xk - 1.0;
// Projection maybe needed
// const NOMAD::Display & out= _p.out();
// NOMAD::dd_type display_degree = out.get_search_dd();
// if ( display_degree == NOMAD::FULL_DISPLAY )
// {
// out << "candidate";
// out << " (before projection)";
// out << ": ( " << *tk << " )" << std::endl;
// }
//
// // Project to the mesh
// tk->project_to_mesh(*feas_inc,signature->get_mesh()->get_delta(),signature->get_lb(),signature->get_ub() );
//
// if ( display_degree == NOMAD::FULL_DISPLAY )
// {
// out << "candidate";
// out << " (after projection)";
// out << ": ( " << *tk << " )" << std::endl;
// }
// Evaluator_Control:
Evaluator_Control & ev_control = mads.get_evaluator_control();
......
examples/basic/batch/bi_obj/param.txt
+ 1
− 1
View file @ fee81ae9
......@@ -17,7 +17,7 @@ upper_bound * 1.0
MULTI_OVERALL_BB_EVAL 500
# ADD_SEED_TO_FILE_NAMES no
# STATS_FILE front.txt OBJ
STATS_FILE front.txt OBJ
# HISTORY_FILE history.txt
DISPLAY_STATS BBE ( SOL ) OBJ
......
examples/basic/batch/bi_obj/runExample.log
+ 137
− 129
View file @ fee81ae9
NOMAD - version 3.6.1 - www.gerad.ca/nomad
NOMAD - version 3.7.2 - 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
......@@ -21,21 +21,21 @@ Please report bugs to nomad@gerad.ca
multi-MADS run {
MADS run 1 ...... OK [bb eval= 64] [overall bb eval= 64] [# dominant pts= 5] [# new pts= 5] [f1=0 f2=8.036326991]
MADS run 2 ...... OK [bb eval= 32] [overall bb eval= 96] [# dominant pts= 12] [# new pts= 7] [f1=0.5676487984 f2=0.1148721239]
MADS run 3 ...... OK [bb eval= 32] [overall bb eval= 128] [# dominant pts= 15] [# new pts= 3] [f1=0 f2=1]
MADS run 4 ...... OK [bb eval= 32] [overall bb eval= 160] [# dominant pts= 25] [# new pts= 10] [f1=0.08125 f2=0.9210041574]
MADS run 5 ...... OK [bb eval= 32] [overall bb eval= 192] [# dominant pts= 37] [# new pts= 12] [f1=0.5164769234 f2=0.5254339031]
MADS run 6 ...... OK [bb eval= 32] [overall bb eval= 224] [# dominant pts= 51] [# new pts= 14] [f1=0.4954991402 f2=0.8104114832]
MADS run 7 ...... OK [bb eval= 32] [overall bb eval= 256] [# dominant pts= 58] [# new pts= 7] [f1=0.4887608589 f2=0.8973446571]
MADS run 8 ...... OK [bb eval= 32] [overall bb eval= 288] [# dominant pts= 65] [# new pts= 7] [f1=0.4876866402 f2=0.9106878852]
MADS run 9 ...... OK [bb eval= 32] [overall bb eval= 320] [# dominant pts= 71] [# new pts= 6] [f1=0.08327636719 f2=0.9208860571]
MADS run 10 ...... OK [bb eval= 32] [overall bb eval= 352] [# dominant pts= 77] [# new pts= 6] [f1=0.08300170898 f2=0.9208857342]
MADS run 11 ...... OK [bb eval= 32] [overall bb eval= 384] [# dominant pts= 83] [# new pts= 6] [f1=0.08300170898 f2=0.9208857342]
MADS run 12 ...... OK [bb eval= 32] [overall bb eval= 416] [# dominant pts= 93] [# new pts= 10] [f1=0.4872792305 f2=0.9157055455]
MADS run 13 ...... OK [bb eval= 32] [overall bb eval= 448] [# dominant pts= 100] [# new pts= 7] [f1=0.487021357 f2=0.9188689839]
MADS run 14 ...... OK [bb eval= 32] [overall bb eval= 480] [# dominant pts= 108] [# new pts= 8] [f1=0.4869359077 f2=0.9202170697]
MADS run 15 ...... OK [bb eval= 20] [overall bb eval= 500] [# dominant pts= 112] [# new pts= 4] [f1=0.08319091797 f2=0.9208854025]
MADS run 1 ...... OK [bb eval= 64] [overall bb eval= 64] [# dominant pts= 10] [# new pts= 10] [f1=0 f2=6.231313115]
MADS run 2 ...... OK [bb eval= 32] [overall bb eval= 96] [# dominant pts= 15] [# new pts= 5] [f1=0.8169729819 f2=-0.479260869]
MADS run 3 ...... OK [bb eval= 32] [overall bb eval= 128] [# dominant pts= 14] [# new pts= -1] [f1=0 f2=1]
MADS run 4 ...... OK [bb eval= 32] [overall bb eval= 160] [# dominant pts= 25] [# new pts= 11] [f1=0.5612660076 f2=0.1239843648]
MADS run 5 ...... OK [bb eval= 32] [overall bb eval= 192] [# dominant pts= 33] [# new pts= 8] [f1=0.3137786342 f2=0.5879263396]
MADS run 6 ...... OK [bb eval= 32] [overall bb eval= 224] [# dominant pts= 47] [# new pts= 14] [f1=0.7805127885 f2=-0.1507861609]
MADS run 7 ...... OK [bb eval= 32] [overall bb eval= 256] [# dominant pts= 54] [# new pts= 7] [f1=0.5299157655 f2=0.357255587]
MADS run 8 ...... OK [bb eval= 32] [overall bb eval= 288] [# dominant pts= 66] [# new pts= 12] [f1=0.07954951288 f2=0.9213144638]
MADS run 9 ...... OK [bb eval= 32] [overall bb eval= 320] [# dominant pts= 76] [# new pts= 10] [f1=0.2748325185 f2=0.7638619206]
MADS run 10 ...... OK [bb eval= 32] [overall bb eval= 352] [# dominant pts= 85] [# new pts= 9] [f1=0.5181766881 f2=0.5029223883]
MADS run 11 ...... OK [bb eval= 32] [overall bb eval= 384] [# dominant pts= 89] [# new pts= 4] [f1=0.7680831771 f2=0.07286356753]
MADS run 12 ...... OK [bb eval= 32] [overall bb eval= 416] [# dominant pts= 99] [# new pts= 10] [f1=0.5151038119 f2=0.5437967797]
MADS run 13 ...... OK [bb eval= 32] [overall bb eval= 448] [# dominant pts= 111] [# new pts= 12] [f1=0.2592955042 f2=0.8727383732]
MADS run 14 ...... OK [bb eval= 32] [overall bb eval= 480] [# dominant pts= 117] [# new pts= 6] [f1=0.5684216658 f2=0.1147587127]
MADS run 15 ...... OK [bb eval= 20] [overall bb eval= 500] [# dominant pts= 120] [# new pts= 3] [f1=0.5684216658 f2=0.1147587127]
} end of run (max number of bb evaluations)
......@@ -46,117 +46,125 @@ number of MADS runs : 15
Pareto front {
BBE ( 0.0000000000 0.0000000000 ) 0.0000000000 1.0000000000
BBE ( 0.0000962840 0.0000000000 ) 0.0000962840 0.9999997577
BBE ( 0.0003906250 0.0000000000 ) 0.0003906250 0.9999960125
BBE ( 0.0031250000 0.0000000000 ) 0.0031250000 0.9997450497
BBE ( 0.0046875000 0.0000000000 ) 0.0046875000 0.9994270708
BBE ( 0.0250000000 0.0000000000 ) 0.0250000000 0.9846803687
BBE ( 0.0500000000 0.0000000000 ) 0.0500000000 0.9499471742
BBE ( 0.0546875000 0.0000000000 ) 0.0546875000 0.9433725823
BBE ( 0.0580017090 0.0000000000 ) 0.0580017090 0.9390043666
BBE ( 0.0582763672 0.0000000000 ) 0.0582763672 0.9386555226
BBE ( 0.0593750000 0.0000000000 ) 0.0593750000 0.9372826427
BBE ( 0.0625000000 0.0000000000 ) 0.0625000000 0.9335937500
BBE ( 0.0687500000 0.0000000000 ) 0.0687500000 0.9273698641
BBE ( 0.0705017090 0.0000000000 ) 0.0705017090 0.9259486581
BBE ( 0.0707763672 0.0000000000 ) 0.0707763672 0.9257399750
BBE ( 0.0718750000 0.0000000000 ) 0.0718750000 0.9249448963
BBE ( 0.0734375000 0.0000000000 ) 0.0734375000 0.9239266272
BBE ( 0.0750000000 0.0000000000 ) 0.0750000000 0.9230457613
BBE ( 0.0765625000 0.0000000000 ) 0.0765625000 0.9223079094
BBE ( 0.0767517090 0.0000000000 ) 0.0767517090 0.9222285264
BBE ( 0.0770263672 0.0000000000 ) 0.0770263672 0.9221171830
BBE ( 0.0781250000 0.0000000000 ) 0.0781250000 0.9217183959
BBE ( 0.0796875000 0.0000000000 ) 0.0796875000 0.9212822432
BBE ( 0.0798767090 0.0000000000 ) 0.0798767090 0.9212400670
BBE ( 0.0799804688 0.0000000000 ) 0.0799804688 0.9212179253
BBE ( 0.0800781250 0.0000000000 ) 0.0800781250 0.9211977263
BBE ( 0.0801513672 0.0000000000 ) 0.0801513672 0.9211829851
BBE ( 0.0812500000 0.0000000000 ) 0.0812500000 0.9210041574
BBE ( 0.0817016602 0.0000000000 ) 0.0817016602 0.9209538533
BBE ( 0.0818115234 0.0000000000 ) 0.0818115234 0.9209436812
BBE ( 0.0821777344 0.0000000000 ) 0.0821777344 0.9209156366
BBE ( 0.0825134277 0.0000000000 ) 0.0825134277 0.9208978855
BBE ( 0.0825927734 0.0000000000 ) 0.0825927734 0.9208948063
BBE ( 0.0827270508 0.0000000000 ) 0.0827270508 0.9208905705
BBE ( 0.0828674316 0.0000000000 ) 0.0828674316 0.9208874553
BBE ( 0.0830017090 0.0000000000 ) 0.0830017090 0.9208857342
BBE ( 0.0830135345 0.0000000000 ) 0.0830135345 0.9208856417
BBE ( 0.0830490112 0.0000000000 ) 0.0830490112 0.9208854215
BBE ( 0.0831909180 0.0000000000 ) 0.0831909180 0.9208854025
BBE ( 0.4868870796 0.0000000000 ) 0.4868870796 0.9205123238
BBE ( 0.4868992866 0.0000061035 ) 0.4868992866 0.9204385441
BBE ( 0.4869359077 0.0000244141 ) 0.4869359077 0.9202170697
BBE ( 0.4870213570 0.0000000000 ) 0.4870213570 0.9188689839
BBE ( 0.4870335640 0.0000061035 ) 0.4870335640 0.9187949686
BBE ( 0.4871007027 0.0000000000 ) 0.4871007027 0.9178966625
BBE ( 0.4871129097 0.0000000000 ) 0.4871129097 0.9177469920
BBE ( 0.4871617378 0.0000000000 ) 0.4871617378 0.9171480902
BBE ( 0.4872052254 0.0000177383 ) 0.4872052254 0.9168338789
BBE ( 0.4872288765 0.0000000000 ) 0.4872288765 0.9163240271
BBE ( 0.4872777046 0.0000000000 ) 0.4872777046 0.9157242928
BBE ( 0.4872792305 0.0000000000 ) 0.4872792305 0.9157055455
BBE ( 0.4872960152 0.0000000000 ) 0.4872960152 0.9154993024
BBE ( 0.4873021187 0.0000030518 ) 0.4873021187 0.9154620588
BBE ( 0.4873078407 0.0000000000 ) 0.4873078407 0.9153539700
BBE ( 0.4873631538 0.0000000000 ) 0.4873631538 0.9146739180
BBE ( 0.4874119820 0.0000000000 ) 0.4874119820 0.9140732253
BBE ( 0.4876378120 0.0000000000 ) 0.4876378120 0.9112905197
BBE ( 0.4876866402 0.0000000000 ) 0.4876866402 0.9106878852
BBE ( 0.4880589546 0.0003417969 ) 0.4880589546 0.9103109499
BBE ( 0.4880772652 0.0000000000 ) 0.4880772652 0.9058545422
BBE ( 0.4884251655 0.0000000000 ) 0.4884251655 0.9015317160
BBE ( 0.4887608589 0.0000000000 ) 0.4887608589 0.8973446571
BBE ( 0.4888585152 0.0000000000 ) 0.4888585152 0.8961237074
BBE ( 0.4892491402 0.0000000000 ) 0.4892491402 0.8912270369
BBE ( 0.4892796577 0.0000000000 ) 0.4892796577 0.8908436249
BBE ( 0.4895237984 0.0000000000 ) 0.4895237984 0.8877718960
BBE ( 0.4898655952 0.0000000000 ) 0.4898655952 0.8834583797
BBE ( 0.4899144234 0.0000000000 ) 0.4899144234 0.8828409301
BBE ( 0.4900120796 0.0000000000 ) 0.4900120796 0.8816051142
BBE ( 0.4901463570 0.0000000000 ) 0.4901463570 0.8799038821
BBE ( 0.4904210152 0.0000000000 ) 0.4904210152 0.8764169969
BBE ( 0.4908116402 0.0000000000 ) 0.4908116402 0.8714417071
BBE ( 0.4910862984 0.0000000000 ) 0.4910862984 0.8679322951
BBE ( 0.4915745796 0.0000000000 ) 0.4915745796 0.8616710988
BBE ( 0.4917088570 0.0000000000 ) 0.4917088570 0.8599443688
BBE ( 0.4919835152 0.0000000000 ) 0.4919835152 0.8564059578
BBE ( 0.4926487984 0.0000000000 ) 0.4926487984 0.8477999690
BBE ( 0.4927464546 0.0000000000 ) 0.4927464546 0.8465326082
BBE ( 0.4942112984 0.0000000000 ) 0.4942112984 0.8274026124
BBE ( 0.4946751655 0.0000000000 ) 0.4946751655 0.8213002814
BBE ( 0.4951146187 0.0002746582 ) 0.4951146187 0.8189185849
BBE ( 0.4954991402 0.0000000000 ) 0.4954991402 0.8104114832
BBE ( 0.4957737984 0.0000000000 ) 0.4957737984 0.8067685071
BBE ( 0.4961155952 0.0000000000 ) 0.4961155952 0.8022260697
BBE ( 0.4961644234 0.0000000000 ) 0.4961644234 0.8015763555
BBE ( 0.4969456734 0.0003417969 ) 0.4969456734 0.7954140614
BBE ( 0.4988987984 0.0000000000 ) 0.4988987984 0.7649058572
BBE ( 0.5001866402 0.0000000000 ) 0.5001866402 0.7474670688
BBE ( 0.5004612984 0.0000000000 ) 0.5004612984 0.7437364245
BBE ( 0.5035862984 0.0000000000 ) 0.5035862984 0.7010722736
BBE ( 0.5055394234 0.0000000000 ) 0.5055394234 0.6742753824
BBE ( 0.5098362984 0.0000000000 ) 0.5098362984 0.6153085893
BBE ( 0.5118870796 0.0000000000 ) 0.5118870796 0.5873076456
BBE ( 0.5120213570 0.0000000000 ) 0.5120213570 0.5854800910
BBE ( 0.5122960152 0.0000000000 ) 0.5122960152 0.5817445301
BBE ( 0.5129612984 0.0000000000 ) 0.5129612984 0.5727116847
BBE ( 0.5133519234 0.0000000000 ) 0.5133519234 0.5674189894
BBE ( 0.5145237984 0.0000000000 ) 0.5145237984 0.5515953642
BBE ( 0.5160862984 0.0000000000 ) 0.5160862984 0.5306427628
BBE ( 0.5164769234 0.0000000000 ) 0.5164769234 0.5254339031
BBE ( 0.5168675484 0.0000000000 ) 0.5168675484 0.5202377465
BBE ( 0.5176487984 0.0000000000 ) 0.5176487984 0.5098855127
BBE ( 0.5207737984 0.0000000000 ) 0.5207737984 0.4690828690
BBE ( 0.5289769234 0.0000000000 ) 0.5289769234 0.3681074167
BBE ( 0.5410862984 0.0000000000 ) 0.5410862984 0.2426270547
BBE ( 0.5457737984 0.0000000000 ) 0.5457737984 0.2038744975
BBE ( 0.5535862984 0.0000000000 ) 0.5535862984 0.1537894775
BBE ( 0.5551487984 0.0000000000 ) 0.5551487984 0.1461090174
BBE ( 0.5582737984 0.0000000000 ) 0.5582737984 0.1332027875
BBE ( 0.5645237984 0.0000000000 ) 0.5645237984 0.1175191667
BBE ( 0.5660862984 0.0000000000 ) 0.5660862984 0.1157579047
BBE ( 0.5676487984 0.0000000000 ) 0.5676487984 0.1148721239
BBE ( 0.0027621359 0.0000000000 ) 0.0027621359 0.9998007770
BBE ( 0.0088388348 0.0000000000 ) 0.0088388348 0.9979744889
BBE ( 0.0132582521 0.0000000000 ) 0.0132582521 0.9954876562
BBE ( 0.0353553391 0.0000000000 ) 0.0353553391 0.9713080680
BBE ( 0.0441941738 0.0000000000 ) 0.0441941738 0.9584480584
BBE ( 0.0574524260 0.0000000000 ) 0.0574524260 0.9397084727
BBE ( 0.0618718434 0.0000000000 ) 0.0618718434 0.9343077419
BBE ( 0.0707106781 0.0000000000 ) 0.0707106781 0.9257895286
BBE ( 0.0729203868 0.0000000000 ) 0.0729203868 0.9242486972
BBE ( 0.0734728140 0.0000000000 ) 0.0734728140 0.9239051795
BBE ( 0.0765111634 0.0000000000 ) 0.0765111634 0.9223298234
BBE ( 0.0783065517 0.0000000000 ) 0.0783065517 0.9216597507
BBE ( 0.0795495129 0.0000000000 ) 0.0795495129 0.9213144638
BBE ( 0.0801709935 0.0000000000 ) 0.0801709935 0.9211790945
BBE ( 0.0804989971 0.0000000000 ) 0.0804989971 0.9211178038
BBE ( 0.0814830080 0.0000000000 ) 0.0814830080 0.9209765043
BBE ( 0.0839689303 0.0000000000 ) 0.0839689303 0.9209099136
BBE ( 0.2546689267 0.0000000000 ) 0.2546689267 0.9053286734
BBE ( 0.2554285140 0.0000000000 ) 0.2554285140 0.9000152969
BBE ( 0.2559809412 0.0000000000 ) 0.2559809412 0.8961400979
BBE ( 0.2592955042 0.0000000000 ) 0.2592955042 0.8727383732
BBE ( 0.2601241450 0.0002762136 ) 0.2601241450 0.8698079048
BBE ( 0.2624029071 0.0011048543 ) 0.2624029071 0.8624679573
BBE ( 0.2633006012 0.0008286408 ) 0.2633006012 0.8531427520
BBE ( 0.2634387080 0.0001898968 ) 0.2634387080 0.8453357823
BBE ( 0.2635077614 0.0000000000 ) 0.2635077614 0.8428146572
BBE ( 0.2643364022 0.0000000000 ) 0.2643364022 0.8369299127
BBE ( 0.2673747516 0.0000000000 ) 0.2673747516 0.8154302217
BBE ( 0.2679271788 0.0000000000 ) 0.2679271788 0.8115410603
BBE ( 0.2695844603 0.0000000000 ) 0.2695844603 0.7999251464
BBE ( 0.2740038777 0.0022097087 ) 0.2740038777 0.7931862207
BBE ( 0.2748325185 0.0000000000 ) 0.2748325185 0.7638619206
BBE ( 0.2762135864 0.0002762136 ) 0.2762135864 0.7575848978
BBE ( 0.2773184407 0.0000000000 ) 0.2773184407 0.7473019205
BBE ( 0.2828427125 0.0000000000 ) 0.2828427125 0.7121565043
BBE ( 0.2872621299 0.0000000000 ) 0.2872621299 0.6860941024
BBE ( 0.2900242657 0.0000000000 ) 0.2900242657 0.6709156096
BBE ( 0.2961009646 0.0000000000 ) 0.2961009646 0.6410185691
BBE ( 0.3005203820 0.0000000000 ) 0.3005203820 0.6226855524
BBE ( 0.3016252363 0.0000000000 ) 0.3016252363 0.6185926969
BBE ( 0.3088067896 0.0000000000 ) 0.3088067896 0.5971609072
BBE ( 0.3093592168 0.0000000000 ) 0.3093592168 0.5959009619
BBE ( 0.3137786342 0.0000000000 ) 0.3137786342 0.5879263396
BBE ( 0.3165407700 0.0000000000 ) 0.3165407700 0.5848920973
BBE ( 0.3167479302 0.0000000000 ) 0.3167479302 0.5847262751
BBE ( 0.3176456244 0.0000000000 ) 0.3176456244 0.5841081818
BBE ( 0.3181980515 0.0000000000 ) 0.3181980515 0.5838092475
BBE ( 0.3226174689 0.0000000000 ) 0.3226174689 0.5836743517
BBE ( 0.5127214698 0.0000000000 ) 0.5127214698 0.5759653136
BBE ( 0.5137572707 0.0002762136 ) 0.5137572707 0.5654252540
BBE ( 0.5140334843 0.0000000000 ) 0.5140334843 0.5582055541
BBE ( 0.5150347585 0.0000172633 ) 0.5150347585 0.5449424207
BBE ( 0.5151038119 0.0000000000 ) 0.5151038119 0.5437967797
BBE ( 0.5153886572 0.0000000000 ) 0.5153886572 0.5399756398
BBE ( 0.5165194066 0.0000000000 ) 0.5165194066 0.5248681601
BBE ( 0.5169337269 0.0000000000 ) 0.5169337269 0.5193587219
BBE ( 0.5173135206 0.0000000000 ) 0.5173135206 0.5143215129
BBE ( 0.5179004745 0.0002762136 ) 0.5179004745 0.5100634344
BBE ( 0.5181076347 0.0000690534 ) 0.5181076347 0.5047074913
BBE ( 0.5181766881 0.0000000000 ) 0.5181766881 0.5029223883
BBE ( 0.5183147949 0.0000000000 ) 0.5183147949 0.5011051160
BBE ( 0.5192124890 0.0000000000 ) 0.5192124890 0.4893395607
BBE ( 0.5203173434 0.0002762136 ) 0.5203173434 0.4784843660
BBE ( 0.5211459841 0.0010358009 ) 0.5211459841 0.4774378717
BBE ( 0.5214912511 0.0000000000 ) 0.5214912511 0.4598694955
BBE ( 0.5228723191 0.0000000000 ) 0.5228723191 0.4423168011
BBE ( 0.5261868821 0.0000000000 ) 0.5261868821 0.4012836645
BBE ( 0.5272917364 0.0000000000 ) 0.5272917364 0.3879862090
BBE ( 0.5292252315 0.0000000000 ) 0.5292252315 0.3652213546
BBE ( 0.5299157655 0.0000000000 ) 0.5299157655 0.3572555870
BBE ( 0.5303300859 0.0000000000 ) 0.5303300859 0.3525194343
BBE ( 0.5317111538 0.0000000000 ) 0.5317111538 0.3369744453
BBE ( 0.5350257169 0.0000000000 ) 0.5350257169 0.3012827434
BBE ( 0.5358543576 0.0000000000 ) 0.5358543576 0.2927387374
BBE ( 0.5361305712 0.0000000000 ) 0.5361305712 0.2899259708
BBE ( 0.5391689207 0.0000000000 ) 0.5391689207 0.2601947270
BBE ( 0.5480077554 0.0000000000 ) 0.5480077554 0.1876299330
BBE ( 0.5513223185 0.0000000000 ) 0.5513223185 0.1663336683
BBE ( 0.5535320271 0.0000000000 ) 0.5535320271 0.1540707094
BBE ( 0.5557417358 0.0000000000 ) 0.5557417358 0.1434067848
BBE ( 0.5568465902 0.0000000000 ) 0.5568465902 0.1386867395
BBE ( 0.5590562989 0.0000000000 ) 0.5590562989 0.1304923502
BBE ( 0.5604373668 0.0000000000 ) 0.5604373668 0.1262254672
BBE ( 0.5612660076 0.0000000000 ) 0.5612660076 0.1239843648
BBE ( 0.5620687533 0.0000000000 ) 0.5620687533 0.1220429764
BBE ( 0.5640022484 0.0000000000 ) 0.5640022484 0.1183011565
BBE ( 0.5645805706 0.0000000000 ) 0.5645805706 0.1174398964
BBE ( 0.5652020512 0.0000000000 ) 0.5652020512 0.1166473780
BBE ( 0.5654092114 0.0000000000 ) 0.5654092114 0.1164138826
BBE ( 0.5656854249 0.0000000000 ) 0.5656854249 0.1161264470
BBE ( 0.5659616385 0.0000000000 ) 0.5659616385 0.1158663392
BBE ( 0.5666866992 0.0000000000 ) 0.5666866992 0.1153137493
BBE ( 0.5669024911 0.0000000000 ) 0.5669024911 0.1151857400
BBE ( 0.5674462866 0.0000000000 ) 0.5674462866 0.1149374124
BBE ( 0.5684216658 0.0000000000 ) 0.5684216658 0.1147587127
BBE ( 0.7661496820 0.0000000000 ) 0.7661496820 0.1105136967
BBE ( 0.7669783228 0.0000000000 ) 0.7669783228 0.0943075051
BBE ( 0.7680831771 0.0000000000 ) 0.7680831771 0.0728635675
BBE ( 0.7691880314 0.0000000000 ) 0.7691880314 0.0516220389
BBE ( 0.7694642450 0.0000000000 ) 0.7694642450 0.0463451418
BBE ( 0.7704309926 0.0007941141 ) 0.7704309926 0.0406045540
BBE ( 0.7705690994 0.0000000000 ) 0.7705690994 0.0253788803
BBE ( 0.7738836624 0.0000000000 ) 0.7738836624 -0.0360307769
BBE ( 0.7741598760 0.0000000000 ) 0.7741598760 -0.0410385581
BBE ( 0.7758171575 0.0000000000 ) 0.7758171575 -0.0706993622
BBE ( 0.7760933711 0.0000000000 ) 0.7760933711 -0.0755763386
BBE ( 0.7766457983 0.0000000000 ) 0.7766457983 -0.0852712175
BBE ( 0.7771982255 0.0000000000 ) 0.7771982255 -0.0948858036
BBE ( 0.7805127885 0.0000000000 ) 0.7805127885 -0.1507861609
BBE ( 0.7816176428 0.0004488471 ) 0.7816176428 -0.1614780263
BBE ( 0.7849322059 0.0000000000 ) 0.7849322059 -0.2200658533
BBE ( 0.7854846330 0.0000000000 ) 0.7854846330 -0.2282658189
BBE ( 0.7882467689 0.0000000000 ) 0.7882467689 -0.2676209257
BBE ( 0.7937710406 0.0000000000 ) 0.7937710406 -0.3375181059
BBE ( 0.7948758950 0.0000000000 ) 0.7948758950 -0.3499937714
BBE ( 0.7970856037 0.0000000000 ) 0.7970856037 -0.3733607572
BBE ( 0.8059244384 0.0000000000 ) 0.8059244384 -0.4444581650
BBE ( 0.8103438558 0.0000000000 ) 0.8103438558 -0.4658115097
BBE ( 0.8169729819 0.0000000000 ) 0.8169729819 -0.4792608690
}
number of Pareto points: 112
number of Pareto points: 120
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Please register or to comment

AEI Privacy Policy Imprint