Global optimization is concerned with the characterization and
computation of global minima or maxima of nonlinear functions. Such
problems are widespread in mathematical modeling of real world systems
for a very broad range of applications. The applications include
economies of scale, fixed charges, allocation and location problems,
quadratic assignment and a number of other combinatorial optimization
problems. More recently it has been shown that certain aspects of VLSI
chip design and database problems can be formulated as constrained
global optimization problems with a quadratic objective function.
Although standard nonlinear programming algorithms will usually obtain a
local minimum to the problem, such a local minimum will only be global
when certain conditions are satisfied (such as f and K being
convex).