ex14_2_6.gms:
Reference:
- Floudas, C A, Pardalos, P M, Adjiman, C S, Esposito, W R, Gumus, Z H, Harding, S T, Klepeis, J L, Meyer, C A, and Schweiger, C A, Handbook of Test Problems in Local and Global Optimization. Kluwer Academic Publishers, 1999.
- Original source: Global Model of Chapter 14 ex14.2.6.gms from Floudas e.a. Test Problems
Point:
p1
Best known point: p1 with value 0.0000
<![CDATA[
* NLP written by GAMS Convert at 07/19/01 13:40:29
*
* Equation counts
* Total E G L N X
* 8 2 0 6 0 0
*
* Variable counts
* x b i s1s s2s sc si
* Total cont binary integer sos1 sos2 scont sint
* 6 6 0 0 0 0 0 0
* FX 0 0 0 0 0 0 0 0
*
* Nonzero counts
* Total const NL DLL
* 35 11 24 0
*
* Solve m using NLP minimizing objvar;
Variables x1,x2,x3,x4,objvar,x6;
Positive Variables x6;
Equations e1,e2,e3,e4,e5,e6,e7,e8;
e1.. objvar - x6 =E= 0;
e2.. 8.85*log(2.11*x1 + 3.19*x2 + 0.92*x3) - 9.85*log(1.97*x1 + 2.4*x2 + 1.4*x3
) - (3.7136*x2 - 0.865100000000001*x1 - 4.8952*x3)/(2.11*x1 + 3.19*x2 +
0.92*x3) - 0.92*log(0.92*x1 + 0.120222883700913*x2 + 0.31896673275906*x3)
+ 0.92*log(0.92*x1 + 2.4*x2 + x3) - 0.92*(0.92*x1/(0.92*x1 +
0.120222883700913*x2 + 0.31896673275906*x3) + 3.53361528312402*x2/(
1.35455252519754*x1 + 2.4*x2 + 0.707809655896681*x3) + 1.21383720135623*x3
/(1.11673022524774*x1 + 0.00499065620537111*x2 + x3)) - 3803.98/(231.47 +
x4) - x6 =L= -12.8590236275375;
e3.. 11*log(2.11*x1 + 3.19*x2 + 0.92*x3) - 12*log(1.97*x1 + 2.4*x2 + 1.4*x3) -
(5.6144*x2 - 1.3079*x1 - 7.4008*x3)/(2.11*x1 + 3.19*x2 + 0.92*x3) - 2.4*
log(1.35455252519754*x1 + 2.4*x2 + 0.707809655896681*x3) + 2.4*log(0.92*x1
+ 2.4*x2 + x3) - 2.4*(0.0460854387520165*x1/(0.92*x1 + 0.120222883700913*
x2 + 0.31896673275906*x3) + 2.4*x2/(1.35455252519754*x1 + 2.4*x2 +
0.707809655896681*x3) + 0.0020794400855713*x3/(1.11673022524774*x1 +
0.00499065620537111*x2 + x3)) - 2788.51/(220.79 + x4) - x6
=L= -11.1728763302021;
e4.. 6*log(2.11*x1 + 3.19*x2 + 0.92*x3) - 7*log(1.97*x1 + 2.4*x2 + 1.4*x3) - (
1.6192*x2 - 0.3772*x1 - 2.1344*x3)/(2.11*x1 + 3.19*x2 + 0.92*x3) - log(
1.11673022524774*x1 + 0.00499065620537111*x2 + x3) + log(0.92*x1 + 2.4*x2
+ x3) - (0.293449394138336*x1/(0.92*x1 + 0.120222883700913*x2 +
0.31896673275906*x3) + 1.69874317415203*x2/(1.35455252519754*x1 + 2.4*x2
+ 0.707809655896681*x3) + x3/(1.11673022524774*x1 + 0.00499065620537111*
x2 + x3)) - 3816.44/(227.02 + x4) - x6 =L= -13.2058768767024;
e5.. 9.85*log(1.97*x1 + 2.4*x2 + 1.4*x3) - 8.85*log(2.11*x1 + 3.19*x2 + 0.92*x3
) + (3.7136*x2 - 0.865100000000001*x1 - 4.8952*x3)/(2.11*x1 + 3.19*x2 +
0.92*x3) + 0.92*log(0.92*x1 + 0.120222883700913*x2 + 0.31896673275906*x3)
- 0.92*log(0.92*x1 + 2.4*x2 + x3) + 0.92*(0.92*x1/(0.92*x1 +
0.120222883700913*x2 + 0.31896673275906*x3) + 3.53361528312402*x2/(
1.35455252519754*x1 + 2.4*x2 + 0.707809655896681*x3) + 1.21383720135623*x3
/(1.11673022524774*x1 + 0.00499065620537111*x2 + x3)) + 3803.98/(231.47 +
x4) - x6 =L= 12.8590236275375;
e6.. 12*log(1.97*x1 + 2.4*x2 + 1.4*x3) - 11*log(2.11*x1 + 3.19*x2 + 0.92*x3) +
(5.6144*x2 - 1.3079*x1 - 7.4008*x3)/(2.11*x1 + 3.19*x2 + 0.92*x3) + 2.4*
log(1.35455252519754*x1 + 2.4*x2 + 0.707809655896681*x3) - 2.4*log(0.92*x1
+ 2.4*x2 + x3) + 2.4*(0.0460854387520165*x1/(0.92*x1 + 0.120222883700913*
x2 + 0.31896673275906*x3) + 2.4*x2/(1.35455252519754*x1 + 2.4*x2 +
0.707809655896681*x3) + 0.0020794400855713*x3/(1.11673022524774*x1 +
0.00499065620537111*x2 + x3)) + 2788.51/(220.79 + x4) - x6
=L= 11.1728763302021;
e7.. 7*log(1.97*x1 + 2.4*x2 + 1.4*x3) - 6*log(2.11*x1 + 3.19*x2 + 0.92*x3) + (
1.6192*x2 - 0.3772*x1 - 2.1344*x3)/(2.11*x1 + 3.19*x2 + 0.92*x3) + log(
1.11673022524774*x1 + 0.00499065620537111*x2 + x3) - log(0.92*x1 + 2.4*x2
+ x3) + 0.293449394138336*x1/(0.92*x1 + 0.120222883700913*x2 +
0.31896673275906*x3) + 1.69874317415203*x2/(1.35455252519754*x1 + 2.4*x2
+ 0.707809655896681*x3) + x3/(1.11673022524774*x1 + 0.00499065620537111*
x2 + x3) + 3816.44/(227.02 + x4) - x6 =L= 13.2058768767024;
e8.. x1 + x2 + x3 =E= 1;
* set non default bounds
x1.lo = 1E-6; x1.up = 1;
x2.lo = 1E-6; x2.up = 1;
x3.lo = 1E-6; x3.up = 1;
x4.lo = 40; x4.up = 90;
* set non default levels
x1.l = 0.013;
x2.l = 0.604;
x3.l = 0.383;
x4.l = 61.583;
* set non default marginals
Model m / all /;
m.limrow=0; m.limcol=0;
$if NOT '%gams.u1%' == '' $include '%gams.u1%'
Solve m using NLP minimizing objvar;
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