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A computational study of the homogeneous algorithm for large-scale convex optimization.

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Erling D. Andersen and Yinyu Ye.

Recently the authors have proposed a homogeneous and
self-dual algorithm for solving the monotone
complementarity problem (MCP) \cite{ANDERSEN:95:A}.
The algorithm is a single phase interior-point type
method, it nevertheless either yields an approximate
optimal solution or detects possible infeasibility
of the problem. In this paper we specialize the algorithm
to solution of general smooth convex optimization problems
that also possess nonlinear equality constraints and free
variables. We discuss an implementation of the algorithm for
large-scale sparse convex optimization. Moreover, we present
computational results for solving quadratically constrained
quadratic programming and geometric programming problems,
where some of the problems contain more than 100000
constraints and variables. The results indicate that the
proposed algorithm is also practically efficient.
Contact:eda@busieco.ou.dk

Publications from Department of Management no. 3/1996,
Odense University, Denmark.