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immun.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 immun.gms from GAMS Model Library

Point: p1
Best known point: p1 with value 0.0000

* NLP written by GAMS Convert at 07/30/01 09:57:36 * * Equation counts * Total E G L N X * 8 8 0 0 0 0 * * Variable counts * x b i s1s s2s sc si * Total cont binary integer sos1 sos2 scont sint * 22 22 0 0 0 0 0 0 * FX 0 0 0 0 0 0 0 0 * * Nonzero counts * Total const NL DLL * 64 58 6 0 * * Solve m using NLP minimizing objvar; Variables x1,x2,x3,x4,x5,x6,x7,x8,x9,x10,x11,x12,x13,x14,x15,x16,x17,x18,x19 ,x20,x21,objvar; Negative Variables x1; Positive Variables x2,x3,x4,x5,x6,x7,x8,x9,x10,x11,x12,x13,x14,x15,x16,x17,x18 ,x19,x20,x21; Equations e1,e2,e3,e4,e5,e6,e7,e8; e1.. - (sqr(-x16) + sqr(50000 - x17) + sqr(42000 - x18) + sqr(40000 - x19) + sqr(40000 - x20) + sqr(45000 - x21)) + objvar =E= 0; e2.. - x10 - x16 =E= 0; e3.. 1044.80727456326*x2 + 1079.40354193291*x3 + 74.5442033113223*x4 + 36.3324688408125*x5 + 41.3438438533384*x6 + 43.2231094830356*x7 + 43.8495313596014*x8 + 59.5100782737447*x9 + 1.00940093153723*x10 - x11 - x17 =E= 0; e4.. 75.57763951196*x4 + 36.8361604344007*x5 + 41.9170101494904*x6 + 43.8223287926491*x7 + 44.4574350070353*x8 + 60.3350903666908*x9 + 1.0391091639109*x11 - x12 - x18 =E= 0; e5.. 75.456505608033*x4 + 36.7771203803858*x5 + 41.8498266397494*x6 + 43.7520914870108*x7 + 44.3861797694312*x8 + 60.2383868299423*x9 + 1.02284761238063*x12 - x13 - x19 =E= 0; e6.. 1167.30216560492*x4 + 74.4548991299823*x5 + 84.7245403892903*x6 + 88.5756558615307*x7 + 89.8593610189442*x8 + 121.951989954281*x9 + 1.05*x13 - x14 - x20 =E= 0; e7.. 1115.8195763046*x5 + 1126.3428356729*x6 + 134.503508270593*x7 + 136.452834477414*x8 + 185.185989647919*x9 + 1.07600174350434*x14 - x15 - x21 =E= 0; e8.. x1 - 40.9351218608642*x2 - 43.2018652628815*x3 - 45.3473311101868*x4 - 39.805625287987*x5 - 41.3125769494053*x6 - 41.8781498541141*x7 - 42.1403213448084*x8 - 46.6038914670337*x9 =E= 0; * set non default bounds x1.up = 187217.324724184; * set non default levels x1.l = 187217.324724184; x2.l = 956.904106888036; x4.l = 45.5987315339227; x9.l = 40.6641597628654; x11.l = 66834.2651808549; x12.l = 33347.8176607291; x14.l = 18186.5732712855; x15.l = 27099.21721716; x17.l = 938765.155199853; x18.l = 42000; x19.l = 40000; x20.l = 40000; * set non default marginals e2.m = 1; e3.m = 1; e4.m = 1; e5.m = 1; e6.m = 1; e7.m = 1; e8.m = 1; x3.m = 1; x5.m = 1; x6.m = 1; x7.m = 1; x8.m = 1; x10.m = 1; x13.m = 1; x18.m = 1; x19.m = 1; x20.m = 1; x21.m = 1; Model m / all /; m.limrow=0; m.limcol=0; \$if NOT '%gams.u1%' == '' \$include '%gams.u1%' Solve m using NLP minimizing objvar;