The augmented system variant of IPMs in two--stage stochastic linear
programming computation
Csaba Meszaros
The application of interior point methods (IPM) to solve the deterministic
equivalent of two--stage stochastic linear programming problems is a known
and natural idea. Experiments show that among the interior point methods,
the augmented system approach gives the best performance on these problems.
However, most of their implementations encounter numerical difficulties in
certain cases, which can result in losing the efficiency. We present our
augmented system solver which ``automatically'' exploits the special
behavior of the problems. We investigate special properties of the augmented
system which make its use fast and numerically robust. We demonstrate our
method by solving a number of large--scale two--stage stochastic linear
programming problems, and we compare our solver with {\sf fo1aug}
\cite{fourer-mehrotra:93} which is considered as a state--of--the--art
augmented
system implementation of interior point methods.
Working Paper WP 95-11, Computer and Automation Institute,
Hungarian Academy of Sciences, Budapest.