worst.gms:
Reference:
- Dahl, H, Meeraus, A, and Zenios, S A, Some Financial Optimization Models: Risk Management. In Zenios, S A, Ed, Financial Optimization. Cambridge University Press, New York, NY, 1993.
- Original source: GAMS Model of worst.gms from GAMS Model Library
Point:
<![CDATA[
* NLP written by GAMS Convert at 07/30/01 09:56:12
*
* Equation counts
* Total E G L N X
* 30 30 0 0 0 0
*
* Variable counts
* x b i s1s s2s sc si
* Total cont binary integer sos1 sos2 scont sint
* 35 35 0 0 0 0 0 0
* FX 0 0 0 0 0 0 0 0
*
* Nonzero counts
* Total const NL DLL
* 112 59 53 0
*
* Solve m using NLP minimizing objvar;
Variables objvar,x2,x3,x4,x5,x6,x7,x8,x9,x10,x11,x12,x13,x14,x15,x16,x17,x18
,x19,x20,x21,x22,x23,x24,x25,x26,x27,x28,x29,x30,x31,x32,x33,x34,x35;
Positive Variables x23,x24,x25,x26,x27,x28,x29,x30;
Equations e1,e2,e3,e4,e5,e6,e7,e8,e9,e10,e11,e12,e13,e14,e15,e16,e17,e18,e19
,e20,e21,e22,e23,e24,e25,e26,e27,e28,e29,e30;
e1.. objvar - x18 - x19 - x20 - x21 - x22 + 30000*x23 - 25000*x24
+ 30000*x25 + 50000*x26 - 25000*x27 - 5000*x28 - 15000*x29 - 50000*x30
=E= 20682900;
e2.. - 95.54*exp(0.09167*x31) + x18 =E= 0;
e3.. - 93.27*exp(0.33889*x32) + x19 =E= 0;
e4.. - 95.54*exp(0.09167*x31) + x20 =E= 0;
e5.. - 93.27*exp(0.33889*x32) + x21 =E= 0;
e6.. - 91.03*exp(0.58889*x33) + x22 =E= 0;
e7.. - exp(-0.33889*x32)*(x21*errorf(x2) - 95*errorf(x10)) + x23 =E= 0;
e8.. - exp(-0.33889*x32)*(x21*errorf(x3) - 97*errorf(x11)) + x25 =E= 0;
e9.. - exp(-0.58889*x33)*(x22*errorf(x6) - 95*errorf(x14)) + x24 =E= 0;
e10.. - exp(-0.58889*x33)*(x22*errorf(x7) - 97*errorf(x15)) + x26 =E= 0;
e11.. - exp(-0.58889*x33)*(x22*errorf(x8) - 99*errorf(x16)) + x27 =E= 0;
e12.. - exp(-0.33889*x32)*(95*errorf(-x12) - x21*errorf(-x4)) + x28 =E= 0;
e13.. - exp(-0.33889*x32)*(97*errorf(-x13) - x21*errorf(-x5)) + x29 =E= 0;
e14.. - exp(-0.58889*x33)*(99*errorf(-x17) - x22*errorf(-x9)) + x30 =E= 0;
e15.. - 1.71779218689115*(log(0.0105263157894737*x21) + 0.169445*sqr(x34))/x34
+ x2 =E= 0;
e16.. - 1.71779218689115*(log(0.0103092783505155*x21) + 0.169445*sqr(x34))/x34
+ x3 =E= 0;
e17.. - 1.71779218689115*(log(0.0105263157894737*x21) + 0.169445*sqr(x34))/x34
+ x4 =E= 0;
e18.. - 1.71779218689115*(log(0.0103092783505155*x21) + 0.169445*sqr(x34))/x34
+ x5 =E= 0;
e19.. - 1.30311549893554*(log(0.0105263157894737*x22) + 0.294445*sqr(x35))/x35
+ x6 =E= 0;
e20.. - 1.30311549893554*(log(0.0103092783505155*x22) + 0.294445*sqr(x35))/x35
+ x7 =E= 0;
e21.. - 1.30311549893554*(log(0.0101010101010101*x22) + 0.294445*sqr(x35))/x35
+ x8 =E= 0;
e22.. - 1.30311549893554*(log(0.0101010101010101*x22) + 0.294445*sqr(x35))/x35
+ x9 =E= 0;
e23.. - x2 + x10 + 0.582142594215541*x34 =E= 0;
e24.. - x3 + x11 + 0.582142594215541*x34 =E= 0;
e25.. - x4 + x12 + 0.582142594215541*x34 =E= 0;
e26.. - x5 + x13 + 0.582142594215541*x34 =E= 0;
e27.. - x6 + x14 + 0.767391686168152*x35 =E= 0;
e28.. - x7 + x15 + 0.767391686168152*x35 =E= 0;
e29.. - x8 + x16 + 0.767391686168152*x35 =E= 0;
e30.. - x9 + x17 + 0.767391686168152*x35 =E= 0;
* set non default bounds
x18.lo = 0.001;
x19.lo = 0.001;
x20.lo = 0.001;
x21.lo = 0.001;
x22.lo = 0.001;
x31.lo = 0.05245; x31.up = 0.0857;
x32.lo = 0.06175; x32.up = 0.095;
x33.lo = 0.0619; x33.up = 0.0939;
x34.lo = 0.0368; x34.up = 0.0768;
x35.lo = 0.0368; x35.up = 0.0768;
* set non default levels
x18.l = 96.1523975231246;
x19.l = 95.8007796007676;
x20.l = 96.1523975231246;
x21.l = 95.8007796007676;
x22.l = 95.303225278852;
x31.l = 0.069075;
x32.l = 0.078375;
x33.l = 0.0779;
x34.l = 0.0568;
x35.l = 0.0568;
* 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;
]]></plaintext></body>