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Two-Point Step Size Gradient Methods
Abstract
We derive two-point step sizes for the steepest-descent method by approximating the secant equation. At the cost of storage of an extra iterate and gradient, these algorithms achieve better performance and cheaper computation than the classical steepest-descent method. We indicate a convergence analysis of the method in the two-dimensional quadratic case. The behaviour is highly remarkable and the analysis entirely nonstandard.
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Cited by
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Optimization Methods for Designing Sequences With Low Autocorrelation Sidelobes
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Fast $\ell_{1}$-Minimization Algorithms for Robust Face Recognition
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Metadata
- Authors
- BARZILAI, J., & BORWEIN, J. M.
- Venue
- IMA Journal of Numerical Analysis
- Publisher
- Oxford University Press (OUP)
- Published
- 1988
- Volume
- 8
- Issue
- 1
- Pages
- 141-148
- ISSN
- 0272-4979 (ISSN-L)
- 1464-3642
- OpenAlex
- W2076605490
- Cited by
- 2,873
- References
- 0
- FWCI
- 0.53
- Type
- journal article
- Language
- en
- Open access
- closed
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- BARZILAI, J., & BORWEIN, J. M. (1988). Two-Point Step Size Gradient Methods. IMA Journal of Numerical Analysis. https://doi.org/10.1093/imanum/8.1.141
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