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{\large Frank E. Curtis \\
{\bf An Interior-Point Method with Inexact Step Calculations for Large-Scale Nonlinear Optimization}}

200 W Packer Ave Rm 325 \\ Bethlehem \\ PA 18015
\\
{\tt frank.e.curtis@lehigh.edu}\\
Olaf Schenk\\
Andreas Waechter\end{center}

We present an interior-point algorithm for nonlinear constrained
optimization. The novel feature of the algorithm is that it allows the
inexact solution of the primal-dual system during every iteration,
meaning that iterative linear solvers present a viable and competitive
option in large-scale settings. Loose and practical termination
conditions for the iterative linear system solve are presented and shown
to provide global convergence guarantees under minimal assumptions. We
illustrate the practical performance of our approach on large
optimization test set collections and model PDE-constrained problems.


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