A Class of Approximate Inverse Preconditioners Based on Krylov-Subspace Methods for Large-Scale Nonconvex Optimization

Abstract

We introduce a class of positive definite preconditioners for the solution of large symmetric indefinite linear systems or sequences of such systems, in optimization frameworks. The preconditioners are iteratively constructed by collecting information on a reduced eigenspace of the indefinite matrix by means of a Krylov-subspace solver. A spectral analysis of the preconditioned matrix shows the clustering of some eigenvalues and possibly the nonexpansion of its spectrum. Extensive numerical experimentation is carried out on standard difficult linear systems and by embedding the class of preconditioners within truncated Newton methods for large-scale unconstrained optimization (the issue of major interest). Although the Krylov-based method may provide modest information on matrix eigenspaces, the results obtained show that the proposed preconditioners lead to substantial improvements in terms of efficiency and robustness, particularly on very large nonconvex problems.

A Descent Four-Term Conjugate Gradient Method with Global Convergence Properties for Large-Scale Unconstrained Optimisation Problems

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