  The paper concerns the study of new classes of nonlinear and nonconvex
optimization problems of the so-called infinite programming that are generally
defined on infinite-dimensional spaces of decision variables and contain
infinitely many of equality and inequality constraints with arbitrary (may not
be compact) index sets. These problems reduce to semi-infinite programs in the
case of finite-dimensional spaces of decision variables. We extend the
classical Mangasarian-Fromovitz and Farkas-Minkowski constraint qualifications
to such infinite and semi-infinite programs. The new qualification conditions
are used for efficient computing the appropriate normal cones to sets of
feasible solutions for these programs by employing advanced tools of
variational analysis and generalized differentiation. In the further
development we derive first-order necessary optimality conditions for infinite
and semi-infinite programs, which are new in both finite-dimensional and
infinite-dimensional settings.
