Added Project and Report

2024-07-30 14:43:25 +02:00
parent c828453e94
commit 3ad6f7f86f
311 changed files with 13490 additions and 3280 deletions
--- a/Report/document.bib
+++ b/Report/document.bib
@ -0,0 +1,51 @@
+@Book{Numerical-Optimization-2006,
+  author    = {Jorge Nocedal and Stephen J. Wright},
+  publisher = {Springer},
+  title     = {Numerical Optimization},
+  year      = {2006},
+  address   = {New York, NY, USA},
+  edition   = {2e},
+}
+
+@article{convergence_lbfgs,
+    title   = {On the limited memory BFGS method for large scale optimization},
+    volume  = {45},
+    DOI     = {10.1007/bf01589116},
+    number  = {1–3},
+    journal = {Mathematical Programming},
+    author  = {Liu, Dong C. and Nocedal, Jorge},
+    year    = {1989},
+    month   = {8},
+    pages   = {503–528}
+}
+
+@ARTICLE{BenchmarkTools,
+  author    = {{Chen}, Jiahao and {Revels}, Jarrett},
+  title     = "{Robust benchmarking in noisy environments}",
+  journal   = {arXiv e-prints},
+  keywords  = {Computer Science - Performance, 68N30, B.8.1, D.2.5},
+  year      = 2016,
+  month     = 8,
+  eid       = {arXiv:1608.04295},
+  archivePrefix = {arXiv},
+  eprint    = {1608.04295},
+  primaryClass = {cs.PF},
+  adsurl    = {https://ui.adsabs.harvard.edu/abs/2016arXiv160804295C},
+  adsnote   = {Provided by the SAO/NASA Astrophysics Data System}
+}
+
+@INPROCEEDINGS {Dogleg,
+    author = {N. Ampazis and S. Spirou and S. Perantonis},
+    booktitle = {Neural Networks, IEEE - INNS - ENNS International Joint Conference on},
+    title = {Training Feedforward Neural Networks with the Dogleg Method and BFGS Hessian Updates},
+    year = {2000},
+    volume = {2},
+    issn = {1098-7576},
+    pages = {1138},
+    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.},
+    doi = {10.1109/IJCNN.2000.857827},
+    url = {https://doi.ieeecomputersociety.org/10.1109/IJCNN.2000.857827},
+    publisher = {IEEE Computer Society},
+    address = {Los Alamitos, CA, USA},
+    month = 7
+}