Convexification and Global Optimization in Continuous and by Mohit Tawarmalani

By Mohit Tawarmalani

Interest in restricted optimization originated with the straightforward linear professional­ gramming version because it used to be sensible and maybe the one computationally tractable version on the time. restricted linear optimization versions have been quickly followed in several program parts and are probably the main typical mathematical types in operations learn and administration technological know-how on the time of this writing. Modelers have, even though, chanced on the idea of linearity to be overly restrictive in expressing the real-world phenomena and difficulties in economics, finance, company, communique, engineering layout, computational biology, and different components that often call for using nonlinear expressions and discrete variables in optimization types. either one of those extensions of the linear programming version are NP-hard, therefore representing very demanding difficulties. at the brighter aspect, contemporary advances in algorithmic and computing know-how give the opportunity to re­ stopover at those issues of the desire of fixing virtually suitable difficulties in average quantities of computational time. preliminary makes an attempt at fixing nonlinear courses focused on the de­ velopment of neighborhood optimization tools ensuring globality below the belief of convexity. however, the integer programming liter­ ature has targeting the improvement of tools that determine international optima. the purpose of this ebook is to marry the developments in fixing nonlinear and integer programming versions and to improve new leads to the extra basic framework of mixed-integer nonlinear courses (MINLPs) with the aim of devising essentially effective worldwide optimization algorithms for MINLPs.

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Additional info for Convexification and Global Optimization in Continuous and Mixed-Integer Nonlinear Programming: Theory, Algorithms, Software, and Applications

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3, we relate convex extensions and convex underestimators of multilinear functions. For certain nonlinear functions, we develop convexification techniques that make use of convex envelopes of multilinear functions which have been studied extensively by Crama (1993), Rikun (1997), and Sherali (1997). 4, we outline a constructive technique for building convex envelopes that uses convex extensions and disjunctive programming (Rockafellar 1970, Balas 1998, Ceria & Soares 1999). We illustrate the technique by developing the convex and concave envelope of x / y over a rectangular region.

Then the convex envelope of Lover P is the tightest convex extension of L, restricted to X, over P and the concave envelope of Lover P is the tightest concave extension of L, restricted to X, over P. Proof. Consider a point x E P but x ~ vert(P). Then, there exists an index i such that Xi ~ vert(Pi ). Consider Ps ~ P containing all x, such that X j = xj, j -I- i. Ps is a translate of Pc. and L is linear on it. ) , x ~ G't(L). 17, x ~ Gj:i(L). Hence, Gj:i(L) ~ vert(P). As vert(P) ~ Gj:i(L) , it follows Gj:i(L) = vert(P).

It follows that f(x) = rJ(x) for From the representation theorem of convex sets it follows that H. Hence rJ(x) :S f(x) :S ¢>(x) "Ix E X. ) Let us assume that x F E ~ at(¢». As rJ(x) = ¢>(x) for x E X, f(x) = ¢>(x) for x E X. It follows then that f is a convex extension of ¢> restricted to X over C and, since F ~ H, f(x) ~ rJ(x) for any arbitrary convex extension. Thus, f is the tightest convex extension. (-{=) If G6'i(¢» X, then there exists a point x E C\X such that (x, f(x)) cannot be expressed as a convex combination of (Xi, ¢>(Xi)), i = 1, ...

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