52 lines
2.1 KiB
BibTeX
52 lines
2.1 KiB
BibTeX
@Book{Numerical-Optimization-2006,
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author = {Jorge Nocedal and Stephen J. Wright},
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publisher = {Springer},
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title = {Numerical Optimization},
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year = {2006},
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address = {New York, NY, USA},
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edition = {2e},
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}
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@article{convergence_lbfgs,
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title = {On the limited memory BFGS method for large scale optimization},
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volume = {45},
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DOI = {10.1007/bf01589116},
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number = {1–3},
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journal = {Mathematical Programming},
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author = {Liu, Dong C. and Nocedal, Jorge},
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year = {1989},
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month = {8},
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pages = {503–528}
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}
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@ARTICLE{BenchmarkTools,
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author = {{Chen}, Jiahao and {Revels}, Jarrett},
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title = "{Robust benchmarking in noisy environments}",
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journal = {arXiv e-prints},
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keywords = {Computer Science - Performance, 68N30, B.8.1, D.2.5},
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year = 2016,
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month = 8,
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eid = {arXiv:1608.04295},
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archivePrefix = {arXiv},
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eprint = {1608.04295},
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primaryClass = {cs.PF},
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adsurl = {https://ui.adsabs.harvard.edu/abs/2016arXiv160804295C},
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adsnote = {Provided by the SAO/NASA Astrophysics Data System}
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}
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@INPROCEEDINGS {Dogleg,
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author = {N. Ampazis and S. Spirou and S. Perantonis},
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booktitle = {Neural Networks, IEEE - INNS - ENNS International Joint Conference on},
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title = {Training Feedforward Neural Networks with the Dogleg Method and BFGS Hessian Updates},
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year = {2000},
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volume = {2},
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issn = {1098-7576},
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pages = {1138},
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abstract = {In this paper, we introduce an advanced optimization algorithm for training feedforward neural networks. The algorithm combines the BFGS Hessian update formula with a special case of trust region techniques, called the Dogleg method, as an altenative technique to line search methods. Simulations regarding classification and function approximation problems are presented which reveal a clear improvement both in convergence and success rates over standard BFGS implementations.},
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doi = {10.1109/IJCNN.2000.857827},
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url = {https://doi.ieeecomputersociety.org/10.1109/IJCNN.2000.857827},
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publisher = {IEEE Computer Society},
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address = {Los Alamitos, CA, USA},
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month = 7
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}
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