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## 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:

* 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;