Damped Techniques for the Limited Memory BFGS Method for Large-Scale Optimization
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DOI: 10.1007/s10957-013-0448-8
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References listed on IDEAS
- Vincent Malmedy & Philippe Toint, 2011. "Approximating Hessians in unconstrained optimization arising from discretized problems," Computational Optimization and Applications, Springer, vol. 50(1), pages 1-22, September.
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Cited by:
- S. Bojari & M. R. Eslahchi, 2020. "Global convergence of a family of modified BFGS methods under a modified weak-Wolfe–Powell line search for nonconvex functions," 4OR, Springer, vol. 18(2), pages 219-244, June.
- Fahimeh Biglari & Farideh Mahmoodpur, 2016. "Scaling Damped Limited-Memory Updates for Unconstrained Optimization," Journal of Optimization Theory and Applications, Springer, vol. 170(1), pages 177-188, July.
- Mehiddin Al-Baali & Andrea Caliciotti & Giovanni Fasano & Massimo Roma, 2017.
"Exploiting damped techniques for nonlinear conjugate gradient methods,"
Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 86(3), pages 501-522, December.
- Mehiddin Al-Baali & Andrea Caliciotti & Giovanni Fasano & Massimo Roma, 2017. "Exploiting damped techniques for nonlinear conjugate gradient methods," DIAG Technical Reports 2017-05, Department of Computer, Control and Management Engineering, Universita' degli Studi di Roma "La Sapienza".
- D. Tarzanagh & M. Peyghami, 2015. "A new regularized limited memory BFGS-type method based on modified secant conditions for unconstrained optimization problems," Journal of Global Optimization, Springer, vol. 63(4), pages 709-728, December.
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Keywords
Large-scale optimization; The limited memory BFGS method; Damped technique; Line search framework;All these keywords.
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