like.gms:
Reference:
- Bracken, J, and McCormick, G P, Chapter 8.5. In Selected Applications of Nonlinear Programming. John Wiley and Sons, New York, 1968, pp. 90-92.
- Original source: GAMS Model of like.gms from GAMS Model Library
Point:
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* NLP written by GAMS Convert at 07/30/01 17:04:28
*
* Equation counts
* Total E G L N X
* 4 2 2 0 0 0
*
* Variable counts
* x b i s1s s2s sc si
* Total cont binary integer sos1 sos2 scont sint
* 10 10 0 0 0 0 0 0
* FX 0 0 0 0 0 0 0 0
*
* Nonzero counts
* Total const NL DLL
* 17 8 9 0
*
* Solve m using NLP minimizing objvar;
Variables x1,x2,x3,x4,x5,x6,x7,x8,x9,objvar;
Positive Variables x4,x5,x6,x7,x8,x9;
Equations e1,e2,e3,e4;
e1.. - (log(0.398942448887604*(x1/x7*exp(-0.5*sqr((95 - x4)/x7)) + x2/x8*exp(
-0.5*sqr((95 - x5)/x8)) + x3/x9*exp(-0.5*sqr((95 - x6)/x9)))) + log(
0.398942448887604*(x1/x7*exp(-0.5*sqr((105 - x4)/x7)) + x2/x8*exp(-0.5*
sqr((105 - x5)/x8)) + x3/x9*exp(-0.5*sqr((105 - x6)/x9)))) + 4*log(
0.398942448887604*(x1/x7*exp(-0.5*sqr((110 - x4)/x7)) + x2/x8*exp(-0.5*
sqr((110 - x5)/x8)) + x3/x9*exp(-0.5*sqr((110 - x6)/x9)))) + 4*log(
0.398942448887604*(x1/x7*exp(-0.5*sqr((115 - x4)/x7)) + x2/x8*exp(-0.5*
sqr((115 - x5)/x8)) + x3/x9*exp(-0.5*sqr((115 - x6)/x9)))) + 15*log(
0.398942448887604*(x1/x7*exp(-0.5*sqr((120 - x4)/x7)) + x2/x8*exp(-0.5*
sqr((120 - x5)/x8)) + x3/x9*exp(-0.5*sqr((120 - x6)/x9)))) + 15*log(
0.398942448887604*(x1/x7*exp(-0.5*sqr((125 - x4)/x7)) + x2/x8*exp(-0.5*
sqr((125 - x5)/x8)) + x3/x9*exp(-0.5*sqr((125 - x6)/x9)))) + 15*log(
0.398942448887604*(x1/x7*exp(-0.5*sqr((130 - x4)/x7)) + x2/x8*exp(-0.5*
sqr((130 - x5)/x8)) + x3/x9*exp(-0.5*sqr((130 - x6)/x9)))) + 13*log(
0.398942448887604*(x1/x7*exp(-0.5*sqr((135 - x4)/x7)) + x2/x8*exp(-0.5*
sqr((135 - x5)/x8)) + x3/x9*exp(-0.5*sqr((135 - x6)/x9)))) + 21*log(
0.398942448887604*(x1/x7*exp(-0.5*sqr((140 - x4)/x7)) + x2/x8*exp(-0.5*
sqr((140 - x5)/x8)) + x3/x9*exp(-0.5*sqr((140 - x6)/x9)))) + 12*log(
0.398942448887604*(x1/x7*exp(-0.5*sqr((145 - x4)/x7)) + x2/x8*exp(-0.5*
sqr((145 - x5)/x8)) + x3/x9*exp(-0.5*sqr((145 - x6)/x9)))) + 17*log(
0.398942448887604*(x1/x7*exp(-0.5*sqr((150 - x4)/x7)) + x2/x8*exp(-0.5*
sqr((150 - x5)/x8)) + x3/x9*exp(-0.5*sqr((150 - x6)/x9)))) + 4*log(
0.398942448887604*(x1/x7*exp(-0.5*sqr((155 - x4)/x7)) + x2/x8*exp(-0.5*
sqr((155 - x5)/x8)) + x3/x9*exp(-0.5*sqr((155 - x6)/x9)))) + 20*log(
0.398942448887604*(x1/x7*exp(-0.5*sqr((160 - x4)/x7)) + x2/x8*exp(-0.5*
sqr((160 - x5)/x8)) + x3/x9*exp(-0.5*sqr((160 - x6)/x9)))) + 8*log(
0.398942448887604*(x1/x7*exp(-0.5*sqr((165 - x4)/x7)) + x2/x8*exp(-0.5*
sqr((165 - x5)/x8)) + x3/x9*exp(-0.5*sqr((165 - x6)/x9)))) + 17*log(
0.398942448887604*(x1/x7*exp(-0.5*sqr((170 - x4)/x7)) + x2/x8*exp(-0.5*
sqr((170 - x5)/x8)) + x3/x9*exp(-0.5*sqr((170 - x6)/x9)))) + 8*log(
0.398942448887604*(x1/x7*exp(-0.5*sqr((175 - x4)/x7)) + x2/x8*exp(-0.5*
sqr((175 - x5)/x8)) + x3/x9*exp(-0.5*sqr((175 - x6)/x9)))) + 6*log(
0.398942448887604*(x1/x7*exp(-0.5*sqr((180 - x4)/x7)) + x2/x8*exp(-0.5*
sqr((180 - x5)/x8)) + x3/x9*exp(-0.5*sqr((180 - x6)/x9)))) + 6*log(
0.398942448887604*(x1/x7*exp(-0.5*sqr((185 - x4)/x7)) + x2/x8*exp(-0.5*
sqr((185 - x5)/x8)) + x3/x9*exp(-0.5*sqr((185 - x6)/x9)))) + 7*log(
0.398942448887604*(x1/x7*exp(-0.5*sqr((190 - x4)/x7)) + x2/x8*exp(-0.5*
sqr((190 - x5)/x8)) + x3/x9*exp(-0.5*sqr((190 - x6)/x9)))) + 4*log(
0.398942448887604*(x1/x7*exp(-0.5*sqr((195 - x4)/x7)) + x2/x8*exp(-0.5*
sqr((195 - x5)/x8)) + x3/x9*exp(-0.5*sqr((195 - x6)/x9)))) + 3*log(
0.398942448887604*(x1/x7*exp(-0.5*sqr((200 - x4)/x7)) + x2/x8*exp(-0.5*
sqr((200 - x5)/x8)) + x3/x9*exp(-0.5*sqr((200 - x6)/x9)))) + 3*log(
0.398942448887604*(x1/x7*exp(-0.5*sqr((205 - x4)/x7)) + x2/x8*exp(-0.5*
sqr((205 - x5)/x8)) + x3/x9*exp(-0.5*sqr((205 - x6)/x9)))) + 8*log(
0.398942448887604*(x1/x7*exp(-0.5*sqr((210 - x4)/x7)) + x2/x8*exp(-0.5*
sqr((210 - x5)/x8)) + x3/x9*exp(-0.5*sqr((210 - x6)/x9)))) + log(
0.398942448887604*(x1/x7*exp(-0.5*sqr((215 - x4)/x7)) + x2/x8*exp(-0.5*
sqr((215 - x5)/x8)) + x3/x9*exp(-0.5*sqr((215 - x6)/x9)))) + 6*log(
0.398942448887604*(x1/x7*exp(-0.5*sqr((220 - x4)/x7)) + x2/x8*exp(-0.5*
sqr((220 - x5)/x8)) + x3/x9*exp(-0.5*sqr((220 - x6)/x9)))) + 5*log(
0.398942448887604*(x1/x7*exp(-0.5*sqr((230 - x4)/x7)) + x2/x8*exp(-0.5*
sqr((230 - x5)/x8)) + x3/x9*exp(-0.5*sqr((230 - x6)/x9)))) + log(
0.398942448887604*(x1/x7*exp(-0.5*sqr((235 - x4)/x7)) + x2/x8*exp(-0.5*
sqr((235 - x5)/x8)) + x3/x9*exp(-0.5*sqr((235 - x6)/x9)))) + 7*log(
0.398942448887604*(x1/x7*exp(-0.5*sqr((240 - x4)/x7)) + x2/x8*exp(-0.5*
sqr((240 - x5)/x8)) + x3/x9*exp(-0.5*sqr((240 - x6)/x9)))) + log(
0.398942448887604*(x1/x7*exp(-0.5*sqr((245 - x4)/x7)) + x2/x8*exp(-0.5*
sqr((245 - x5)/x8)) + x3/x9*exp(-0.5*sqr((245 - x6)/x9)))) + 2*log(
0.398942448887604*(x1/x7*exp(-0.5*sqr((260 - x4)/x7)) + x2/x8*exp(-0.5*
sqr((260 - x5)/x8)) + x3/x9*exp(-0.5*sqr((260 - x6)/x9))))) - objvar
=E= 0;
e2.. x1 + x2 + x3 =E= 1;
e3.. - x4 + x5 =G= 0;
e4.. - x5 + x6 =G= 0;
* set non default bounds
x1.lo = 0.1;
x2.lo = 0.1;
x3.lo = 0.1;
* set non default levels
x1.l = 0.333333333333333;
x2.l = 0.333333333333333;
x3.l = 0.333333333333333;
x4.l = 130;
x5.l = 160;
x6.l = 190;
x7.l = 15;
x8.l = 15;
x9.l = 15;
* 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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