A linear optimization problem is the task of minimizing a linear
real-valued function of finitely many variables subject to linear con-
straints; in general there may be infinitely many constraints. This book
is devoted to such problems. Their mathematical properties are investi-
gated and algorithms for their computational solution are presented.
Applications are discussed in detail. Linear optimization problems are
encountered in many areas of appli- cations. They have therefore been
subject to mathematical analysis for a long time. We mention here only
two classical topics from this area: the so-called uniform approximation
of functions which was used as a mathematical tool by Chebyshev in 1853
when he set out to design a crane, and the theory of systems of linear
inequalities which has already been studied by Fourier in 1823. We will
not treat the historical development of the theory of linear
optimization in detail. However, we point out that the decisive break-
through occurred in the middle of this century. It was urged on by the
need to solve complicated decision problems where the optimal deployment
of military and civilian resources had to be determined. The
availability of electronic computers also played an important role. The
principal computational scheme for the solution of linear optimization
problems, the simplex algorithm, was established by Dantzig about 1950.
In addi- tion, the fundamental theorems on such problems were rapidly
developed, based on earlier published results on the properties of
systems of linear inequalities.