10th International Scientific Conference Technics, Informatics and Education – TIE 2024, str. 174-178
АУТОР(И) / AUTHOR(S): Ladislav Stazić , Đorđe Dobrota , Antonija Mišura , Maja Račić
DOI: 10.46793/TIE24.174S
САЖЕТАК /ABSTRACT:
Every day we witness the introduction of technological innovations into our daily lives, which are changing significantly under their influence. In the past, roots were drawn by hand with paper and pencil, then calculators appeared. Today, nobody tries to draw the root by hand anymore. People used to look for important information in encyclopedias and various archives and libraries, but today they turn to Google for every little thing… Using an example from the field of applied mathematics, we want to show how technology is changing the approach to solving relatively complex technical problems. The example presented in this paper is solving complex mathematical problems such as optimization using a method that has only recently become available through the development of technology. Advanced methods for solving optimization problems such as the Brents method or the Limited memory Broyden–Fletcher–Goldfarb–Shanno method with boundaries are now partially replaced by the Brute Force method, which is much easier to handle. This example shows that we are constantly adapting to new circumstances and that each generation uses the latest knowledge available, changing the approach to daily practice. Adapting to new circumstances and technologies and abandoning and neglecting old methods (i.e. optimizing the necessary acquired knowledge) leads to the false claim that the new generation does not know and does not want to learn the basic methods.
КЉУЧНЕ РЕЧИ / KEYWORDS:
Technology, teaching, computer, optimization
ЛИТЕРАТУРА / REFERENCES:
- Brent, R. P. (1973). Algorithms for minimization without derivatives. Prentice-Hall Inc, New Jersey, USA. ISBN: 0-13-022335-2
- Press, W.H., Teukolsky, S.A., Vetterling, W.T., Flannery, B.P. (2007). Numerical recipes: The art of scientific computing, 3rd edition. Cambridge university press. New York, USA. ISBN-13: 978-0-511-33555-6
- Li, H. (2022). Finding Roots of Equations. Numerical Methods Using Java. Apress, Berkeley, CA. pp.207-228. doi: 10.1007/978-1-4842-6797-4_3
- Stage, S. A. (2013). Comments on an improvement to the Brent’s method. International Journal of Experimental Algorithms, Vol. 4(1), pp. 1-16
- Gegenfurtner, K. R. (1992). PRAXIS: Brent’s algorithm for function minimization. Behavior Research Methods, Instruments, & Computers, Vol. 24, pp. 560-564.
- Brent, R. P. (1991). Fast training algorithms for multilayer neural nets. IEEE Transactions on Neural Networks, Vol. 2(3), pp. 346-354
- Lorencin, I., Anđelić, N., Mrzljak, V., Car, Z. (2019). Multilayer Perceptron approach to Condition-Based Maintenance of Marine CODLAG Propulsion System Components. Pomorstvo, Vol. 33(2), pp. 181–190, doi: 10.31217/p.33.2.8
- Fei, Y., Rong, G., Wang, B., Wang, W. (2014). Parallel L-BFGS-B algorithm on GPU. Computers & Graphics, Vol. 40, pp. 1–9, doi: 10.1016/j.cag.2014.01.002
- Zhu, C., Byrd, R. H., Lu, P., Nocedal, J. (1997). Algorithm 778: L-BFGS-B: Fortran subroutines for large-scale bound-constrained optimization. ACM Transactions on Mathematical Software (TOMS), Vol. 23(4), pp. 550-560, doi: 10.1145/279232.279236
- Byrd, R. H., Lu, P., Nocedal, J., Zhu, C. (1995). A limited memory algorithm for bound constrained optimization. SIAM Journal on scientific computing, Vol. 16(5), pp. 1190-1208
- Torregrossa, D., Capitanescu, F. (2018). Optimization models to save energy and enlarge the operational life of water pumping systems. Journal of Cleaner Production, Vol. 213, pp. 89-98, doi: 10.1016/j.jclepro.2018.12.124
- Frischer, R., Pollak, M., Jančíková, Z. K. (2019). Optimizing logistics routes in the field of maintenance. Acta Logistica, Vol. 6(3), pp. 93-101, doi: 10.22306/al.v6i3.123
- Xing, Z., Zhang, Z., Guo, J., Qin, Y., Jia, L. (2023). Rail train operation energy-saving optimization based on improved brute-force search. Applied Energy, Vol. 330, pp. 120345, doi: 10.1016/j.apenergy.2022.120345
- Shakeri Aski, F., Mirparizi, M., Sheykh Samani, F., Ali Hajabasi, M. (2014). Vibration behavior optimization of planetary gear sets. Propulsion and Power Research, Vol. 3(4), pp. 196–206, doi: 10.1016/j.jppr.2014.11.002
- Soman, R., Boyer, A., Kim, J.M., Peters, K. (2022) Particle Swarm Optimization Algorithm for Guided Waves Based Damage Localization Using Fiber Bragg Grating Sensors in Remote Configuration. Sensors, Vol. 22(16), pp. 6000, doi: 10.3390/s22166000
- Vincent, P., Cunha Sergio, G., Jang, J., Kang, I.M., Park, J., Kim, H., Lee, M., Bae, J. H. (2020). Application of Genetic Algorithm for More Efficient Multi-Layer Thickness Optimization in Solar Cells. Energies. Vol. 13. pp. 1726. doi: 10.3390/en13071726
- Python (2024). available at: https://www.python.org/ [accessed on May 15th, 2024]
- Liang, Y. D. (2013). Introduction to programming using Python, Pearson Education, Inc. PNew Jersey, USA. ISBN 13: 978-0-13-274718-9
- Nagpal, A., Gabrani, G. (2019). Python for Data Analytics, Scientific and Technical Applications. Amity International Conference on Artificial Intelligence (AICAI), Dubai. pp. 140-145, doi: 10.1109/AICAI.2019.8701341.
- SciPy (2024). available at: https://scipy.org/. [accessed on May 15th, 2024]
- Bhattacharjee, M. C. (1987). New results for the Brown-Proschan model of imperfect repair. Journal of Statistical Planning and Inference, Vol. 16, pp. 305–316, doi: 10.1016/0378-3758(87)90083-8
- Schaller, R. R. (1997). Moore’s law: past, present and future. IEEE Spectrum, Vol. 34(6), pp. 52–59, doi: 10.1109/6.591665