•  #### Hands-On Simulation Modeling with Python #### Overview of this book

Simulation modeling helps you to create digital prototypes of physical models to analyze how they work and predict their performance in the real world. With this comprehensive guide, you'll understand various computational statistical simulations using Python. Starting with the fundamentals of simulation modeling, you'll understand concepts such as randomness and explore data generating processes, resampling methods, and bootstrapping techniques. You'll then cover key algorithms such as Monte Carlo simulations and Markov decision processes, which are used to develop numerical simulation models, and discover how they can be used to solve real-world problems. As you advance, you'll develop simulation models to help you get accurate results and enhance decision-making processes. Using optimization techniques, you'll learn to modify the performance of a model to improve results and make optimal use of resources. The book will guide you in creating a digital prototype using practical use cases for financial engineering, prototyping project management to improve planning, and simulating physical phenomena using neural networks. By the end of this book, you'll have learned how to construct and deploy simulation models of your own to overcome real-world challenges.
Preface Section 1: Getting Started with Numerical Simulation  Free Chapter
Chapter 1: Introducing Simulation Models Chapter 2: Understanding Randomness and Random Numbers Chapter 3: Probability and Data Generation Processes Section 2: Simulation Modeling Algorithms and Techniques Chapter 4: Exploring Monte Carlo Simulations Chapter 5: Simulation-Based Markov Decision Processes Chapter 6: Resampling Methods Chapter 7: Using Simulation to Improve and Optimize Systems Section 3: Real-World Applications Chapter 8: Using Simulation Models for Financial Engineering Chapter 9: Simulating Physical Phenomena Using Neural Networks Chapter 10: Modeling and Simulation for Project Management Chapter 11: What's Next? Other Books You May Enjoy # Discovering the 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...