New Inexact Line Search Method for Unconstrained Optimization |
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| Authors: | Z J Shi J Shen |
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| Institution: | (1) College of Operations Research and Management, Qufu Normal University, Rizhao, Shandong, PRC;(2) Institute of Computational Mathematics and Scientific/Engineering Computing, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Rizhao, Beijing, Shandong, PRC;(3) Department of Computer and Information Science, University of Michigan, Dearborn, Michigan |
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| Abstract: | We propose a new inexact line search rule and analyze the global convergence and convergence rate of related descent methods.
The new line search rule is similar to the Armijo line-search rule and contains it as a special case. We can choose a larger
stepsize in each line-search procedure and maintain the global convergence of related line-search methods. This idea can make
us design new line-search methods in some wider sense. In some special cases, the new descent method can reduce to the Barzilai
and Borewein method. Numerical results show that the new line-search methods are efficient for solving unconstrained optimization
problems.
The work was supported by NSF of China Grant 10171054, Postdoctoral Fund of China, and K. C. Wong Postdoctoral Fund of CAS
Grant 6765700.
The authors thank the anonymous referees for constructive comments and suggestions that greatly improved the paper. |
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| Keywords: | Unconstrained optimization inexact line search global convergence convergence rate |
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