An Interior-Point Algorithm for Large-Scale Nonlinear Optimization with Inexact Step Computations

We present a line-search interior-point algorithm for large-scale continuous optimization. The algorithm is matrix-free in that it does not require the factorization of derivative matrices and instead uses iterative linear system solvers. Inexact step computations are supported in order to save computational expense during each iteration. The algorithm is an interior-point approach derived from the inexact Newton method for equality constrained optimization described by Curtis, Nocedal, and Wächter in [9], with additional functionality for handling inequality constraints. The algorithm is shown to be globally convergent under standard assumptions. Numerical results are presented on partial differential equation constrained model problems.

By: Frank E. Curtis; Olaf Schenk; Andreas Waechter

Published in: RC24736 in 2009


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