Higher Order Iteration Schemes for Unconstrained Optimization

Yangyang Shi, Pingqi Pan

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DOI: 10.4236/ajor.2011.13011                

Abstract

Using a predictor-corrector tactic, this paper derives new iteration schemes for unconstrained optimization. It yields a point (predictor) by some line search from the current point; then with the two points it constructs a quadratic interpolation curve to approximate some ODE trajectory; it finally determines a new point (corrector) by searching along the quadratic curve. In particular, this paper gives a global convergence analysis for schemes associated with the quasi-Newton updates. In our computational experiments, the new schemes using DFP and BFGS updates outperformed their conventional counterparts on a set of standard test problems.

Keywords

Unconstrained Optimization, Iteration Scheme, ODE Method, Quasi-Newton Update, Convergence Analysis

Conflicts of Interest

The authors declare no conflicts of interest.

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