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## st_e26.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.
• Thakur, L S, Domain contraction in nonlinear programming: Minimizing a quadratic concave function over a polyhedron. Mathematics of Operations Research 16 (1990), 390-407.

Point:

* NLP written by GAMS Convert at 08/29/02 12:49:54 * * Equation counts * Total E G L N X C * 5 1 0 4 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 * 11 9 2 0 * * Solve m using NLP minimizing objvar; Variables x1,x2,objvar; Positive Variables x1,x2; Equations e1,e2,e3,e4,e5; e1.. 0.7*x1 + x2 =L= 6.3; e2.. 0.5*x1 + 0.8333*x2 =L= 6; e3.. x1 + 0.6*x2 =L= 7.08; e4.. 0.1*x1 + 0.25*x2 =L= 1.35; e5.. - (-3*sqr(x1) - 5*x1 - 3*sqr(x2) - 5*x2) + objvar =E= 0; * set non default bounds x1.up = 10; x2.up = 30; * 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;