On exact linesearch quasi-Newton methods for minimizing a quadratic function

Anders Forsgren () and Tove Odland ()
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Anders Forsgren: KTH Royal Institute of Technology
Tove Odland: KTH Royal Institute of Technology

Computational Optimization and Applications, 2018, vol. 69, issue 1, No 9, 225-241

Abstract: Abstract This paper concerns exact linesearch quasi-Newton methods for minimizing a quadratic function whose Hessian is positive definite. We show that by interpreting the method of conjugate gradients as a particular exact linesearch quasi-Newton method, necessary and sufficient conditions can be given for an exact linesearch quasi-Newton method to generate a search direction which is parallel to that of the method of conjugate gradients. We also analyze update matrices and give a complete description of the rank-one update matrices that give search direction parallel to those of the method of conjugate gradients. In particular, we characterize the family of such symmetric rank-one update matrices that preserve positive definiteness of the quasi-Newton matrix. This is in contrast to the classical symmetric-rank-one update where there is no freedom in choosing the matrix, and positive definiteness cannot be preserved. The analysis is extended to search directions that are parallel to those of the preconditioned method of conjugate gradients in a straightforward manner.

Keywords: Method of conjugate gradients; Quasi-Newton method; Unconstrained quadratic program; Exact linesearch method (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/s10589-017-9940-7

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