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Hands-On Simulation Modeling with Python

You're reading from   Hands-On Simulation Modeling with Python Develop simulation models for improved efficiency and precision in the decision-making process

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Product type Paperback
Published in Nov 2022
Publisher Packt
ISBN-13 9781804616888
Length 460 pages
Edition 2nd Edition
Languages
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Author (1):
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Giuseppe Ciaburro Giuseppe Ciaburro
Author Profile Icon Giuseppe Ciaburro
Giuseppe Ciaburro
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Table of Contents (19) Chapters Close

Preface 1. Part 1:Getting Started with Numerical Simulation
2. Chapter 1: Introducing Simulation Models FREE CHAPTER 3. Chapter 2: Understanding Randomness and Random Numbers 4. Chapter 3: Probability and Data Generation Processes 5. Part 2:Simulation Modeling Algorithms and Techniques
6. Chapter 4: Exploring Monte Carlo Simulations 7. Chapter 5: Simulation-Based Markov Decision Processes 8. Chapter 6: Resampling Methods 9. Chapter 7: Using Simulation to Improve and Optimize Systems 10. Chapter 8: Introducing Evolutionary Systems 11. Part 3:Simulation Applications to Solve Real-World Problems
12. Chapter 9: Using Simulation Models for Financial Engineering 13. Chapter 10: Simulating Physical Phenomena Using Neural Networks 14. Chapter 11: Modeling and Simulation for Project Management 15. Chapter 12: Simulating Models for Fault Diagnosis in Dynamic Systems 16. Chapter 13: What’s Next? 17. Index 18. Other Books You May Enjoy

Discovering multivariate optimization methods in Python

In this section, we will analyze some numerical optimization methods contained in the Python SciPy library. SciPy is a collection of mathematical algorithms and functions based on numpy. It contains a series of commands and high-level classes that can be used to manipulate and display data. With SciPy, functionality is added to Python, making it a data processing and system prototyping environment, similar to commercial systems such as MATLAB.

Scientific applications that use SciPy benefit from the development of add-on modules in numerous fields of numerical computing made by developers around the world. Numerical optimization problems are also covered among the available modules.

The SciPy optimize module contains numerous functions for the minimization/maximization of objective functions, both constrained and unconstrained. It treats nonlinear problems with support for both local and global optimization algorithms. In...

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