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## st_e22.gms:

#### References:

• Tawarmalani, M, and Sahinidis, N, Convexification and Global Optimization in Continuous and Mixed-Integer Nonlinear Programming: Theory, Algorithms, Software, and Applications. Kluwer, 2002.
• Kalantari, B, and Rosen, J B, An algorithm for global minimization of linearly constrained convex quadratic functions. Mathematics of Operations Research 12 (1987), 544--561.

Point:

* NLP written by GAMS Convert at 08/29/02 12:49:54 * * Equation counts * Total E G L N X C * 6 1 0 5 0 0 0 * * Variable counts * x b i s1s s2s sc si * Total cont binary integer sos1 sos2 scont sint * 3 3 0 0 0 0 0 0 * FX 0 0 0 0 0 0 0 0 * * Nonzero counts * Total const NL DLL * 13 11 2 0 * * Solve m using NLP minimizing objvar; Variables x1,x2,objvar; Positive Variables x1,x2; Equations e1,e2,e3,e4,e5,e6; e1.. x1 + x2 =L= 10; e2.. x1 + 5*x2 =L= 22; e3.. - 3*x1 + 2*x2 =L= 2; e4.. - x1 - 4*x2 =L= -4; e5.. x1 - 2*x2 =L= 4; e6.. - (-sqr(x1) - 4*sqr(x2)) + objvar =E= 0; * set non default bounds x1.up = 8; x2.up = 4; * set non default levels * 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;