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An Inertia-Free Filter Line-Search Algorithm for Large-Scale Nonlinear Programming

Victor M Zavala (vzavala***at***mcs.anl.gov)
Naiyuan Chiang (nychiang***at***mcs.anl.gov)

Abstract: We present a filter line-search algorithm that does not require inertia information about the linear system to ensure global convergence. The proposed approach performs curvature tests along the search step to ensure descent. This feature permits more modularity in the linear algebra, enabling the use of a wider range of iterative and decomposition strategies. We use the inertia-free approach in an interior-point framework and provide numerical evidence that this is as robust as inertia detection via symmetric indefinite factorizations and can lead to important reductions in solution time.

Keywords: inertia, nonlinear programming, filter line-search, nonconvex, large-scale

Category 1: Nonlinear Optimization

Category 2: Stochastic Programming

Category 3: Applications -- Science and Engineering (Optimization of Systems modeled by PDEs )


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Entry Submitted: 09/17/2014
Entry Accepted: 09/17/2014
Entry Last Modified: 12/23/2015

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