# Abstract Convexity and Global Optimization by Alexander M. Rubinov

By Alexander M. Rubinov

Special instruments are required for analyzing and fixing optimization difficulties. the most instruments within the examine of neighborhood optimization are classical calculus and its sleek generalizions which shape nonsmooth research. The gradient and numerous forms of generalized derivatives let us ac complish a neighborhood approximation of a given functionality in a neighbourhood of a given element. this type of approximation is particularly valuable within the research of neighborhood extrema. despite the fact that, neighborhood approximation by myself can't support to resolve many difficulties of world optimization, so there's a transparent have to increase specific international instruments for fixing those difficulties. the easiest and so much famous region of world and at the same time neighborhood optimization is convex programming. the elemental instrument within the examine of convex optimization difficulties is the subgradient, which actu best friend performs either an area and international position. First, a subgradient of a convex functionality f at some degree x contains out a neighborhood approximation of f in a neigh bourhood of x. moment, the subgradient allows the development of an affine functionality, which doesn't exceed f over the complete house and coincides with f at x. This affine functionality h is named a help func tion. on account that f(y) ~ h(y) for best friend, the second one position is international. unlike a neighborhood approximation, the functionality h can be referred to as an international affine support.

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Let x ¢ U. Since U is closed-alongrays there exists c > 0 such that (1- c)x ¢ U. Let l= Then (l,x} = 1 . 8) min liXi = -1 1 > 1. - c iEI+(x) Let y E U. Since U is normal and (1-c)x ¢ U, the inequality y;;::: (1-c)x does not hold. Hence there exists an index io such that Yio < (1- c)Xio· Then Yi < Yio . 1 (l ) _ ,y - iE~~) (1- c)xi - (1- c)Xio < · Thus (l, x) > sup(l, y). 15 Let U be a closed-along-rays and normal set. 19 that for each x ¢ U there exists l' E IR++ such that (l',x) > SUPyeu(l',y).

1; • if x,y E ffi. : Yi for all i E I; • if x, y E ffi. 1 Xi : x » 0}. If I = { 1, ... ~+' respectively. +. m. +. n : Xi > 0 for all i 19 E I}. 2. Recall some definitions from the geometry of vector spaces. Let X be a vector space and x E X. x : >. x : >. ~ 0}) is called the open ray (closed ray) starting from zero and passing through x. 8) x E Q ===? Rx C Q. Occasionally we shall use a term cone instead of a conic set. lt is easy to see that a conic set Q is a convex cone if and only if the following holds: (xl, X2 E Q) ===?

Normal subsets of the cone 1Rf-+ can be defined in the same way. Normal sets are closely connected to the so-called IPH (increasing positively homogeneous of degree one) functions. There is a clear analogy between the class of IPH functions and the class of sublinear functions, and between normal sets and convex sets. ) One of the main tools for the study of sublinearity of functions and the convexity of sets are linear functions. For example, a function p defined on 1Rn is lower semicontinuous and sublinear if and only if this function is abstract convex with respect to the set of all linear functions, in other words, there exists a set of linear functions U such that p(x) = sup{u(x): u E U}.