Abstract: | A method is presented for solving a class of global optimization problems of the form (P): minimizef(x), subject toxD,g(x)0, whereD is a closed convex subset ofR
n
andf,g are convex finite functionsR
n
. Under suitable stability hypotheses, it is shown that a feasible point
is optimal if and only if 0=max{g(x):xD,f(x)f(
)}. On the basis of this optimality criterion, the problem is reduced to a sequence of subproblemsQ
k
,k=1, 2, ..., each of which consists in maximizing the convex functiong(x) over some polyhedronS
k
. The method is similar to the outer approximation method for maximizing a convex function over a compact convex set. |