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Numerical Python: Scientific Computing and Data Science Applications with Numpy, SciPy and Matplotlib
book

Numerical Python: Scientific Computing and Data Science Applications with Numpy, SciPy and Matplotlib

by Robert Johansson
September 2024
Intermediate to advanced content levelIntermediate to advanced
501 pages
17h 6m
English
Apress
Content preview from Numerical Python: Scientific Computing and Data Science Applications with Numpy, SciPy and Matplotlib
© The Author(s), under exclusive license to APress Media, LLC, part of Springer Nature 2024
R. JohanssonNumerical Pythonhttps://doi.org/10.1007/979-8-8688-0413-7_6

6. Optimization

Robert Johansson1  
(1)
Urayasu-shi, Chiba, Japan
 

This chapter builds on Chapter 5 about equation solving and explores the related topic of solving optimization problems. Optimization is the process of finding and selecting the optimal element from a set of feasible candidates. In mathematical optimization, this problem is usually formulated as determining the extreme value of a function on a given domain. An extreme value, or an optimal value, can refer to either the minimum or maximum of the function, depending on the application and the specific problem. The chapter covers ...

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