Abstract
Optimization is part of many university courses because of its importance in many disciplines and applications such as engineering design, business planning, computer science, data mining, machine learning, artificial intelligence and industries. The techniques and algorithms for optimization are diverse, ranging from the traditional gradient-based algorithms to contemporary swarm intelligence based algorithms. This chapter introduces the fundamentals of optimization and some of the traditional optimization techniques.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bertsekas DP, Nedic A, Ozdaglar A (2003) Convex analysis and optimization, 2nd edn. Athena Scientific, Belmont
Bottou L (2010) Large-scale machine learning with stochastic gradient descent. In: Lechevallier Y, Saporta G (eds) Proceedings of COMPSTAT’2010, pp 177–186
Boyd S, Vandenberghe L (2004) Convex optimization. Cambridge University Press, Cambridge
Dantzig GB, Thapa MN (1997) Linear programming. 1: Introduction. Springer, Heidelberg
LeCun Y, Bengio Y, Hinton GE (2015). Deep learning. Nature 521:636–444
Press WH, Teukolsky SA, Vetterling WT, Flannery BP (2007) Numerical recipes: the art of scientific computing, 3rd edn. Cambridge University Press, Cambridge
Süli E, Mayer D (2003) An introduction to numerical analysis. Cambridge University Press, Cambridge
Yang XS (2010) Engineering optimization: an introduction with metaheuristic applications. Wiley, Hoboken
Yang XS (2018) Optimization techniques and applications with examples. Wiley, Hoboken
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2019 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Yang, XS., He, XS. (2019). Introduction to Optimization. In: Mathematical Foundations of Nature-Inspired Algorithms. SpringerBriefs in Optimization. Springer, Cham. https://doi.org/10.1007/978-3-030-16936-7_1
Download citation
DOI: https://doi.org/10.1007/978-3-030-16936-7_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-16935-0
Online ISBN: 978-3-030-16936-7
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)