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

We present a line-search algorithm for large-scale continuous optimization. The algorithm is matrix-free in that it does not require the factorization of derivative matrices. Instead, it 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 an inexact Newton method for equality constrained optimization proposed by Curtis, Nocedal, and W

• Type: Journal
• Year: 2010
• Where: SIAMSC
• Authors: Frank E. Curtis, Olaf Schenk, Andreas Wächter
• Keywords: Algorithm, Large-scale Continuous Optimization, Linear System Solvers, SIAMSC 2010