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2004•Optimization methods & softwareRequires access

On the performance of switching BFGS/SR1 algorithms for unconstrained optimization

M. Al-Baali, Antonio Fuduli, Roberto Musmanno

Open publisher page 6 citations

Abstract

This paper studies some possible combinations of the best features of the quasi-Newton symmetric rank-one (SR1), BFGS and extra updating BFGS algorithms for solving nonlinear unconstrained optimization problems. These combinations depend on switching between the BFGS and SR1 updates so that certain desirable properties are imposed. The presented numerical results show that the proposed switching algorithm outperforms the robust BFGS method.

About this research paper

What this paper is about

This paper studies some possible combinations of the best features of the quasi-Newton symmetric rank-one (SR1), BFGS and extra updating BFGS algorithms for solving nonlinear unconstrained optimization problems. These combinations depend on switching between the BFGS and SR1 updates so that certain desirable properties are imposed. The presented numerical results show that the proposed switching algorithm outperforms the robust BFGS method.

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OpenAlex reports 6 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

This paper studies some possible combinations of the best features of the quasi-Newton symmetric rank-one (SR1), BFGS and extra updating BFGS algorithms for solving nonlinear unconstrained optimization problems. These combinations depend on switching between the BFGS and SR1 updates so that certain desirable properties are imposed. The presented numerical results show that the proposed switching algorithm outperforms the robust BFGS method.

Key concepts: Broyden–Fletcher–Goldfarb–Shanno algorithm, Rank (graph theory), Algorithm, Quasi-Newton method, Computer science, Mathematical optimization, Nonlinear system, Optimization algorithm

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