State-of-the-art iterative methods: Enables users to apply the latest techniques for solving nonlinear equations
Julia code examples: Provides practical implementations for each method, facilitating efficient coding
Pseudocode and algorithm descriptions: Clarifies complex methods for easier understanding
Jupyter notebooks and Julia package: Supports interactive learning and real-time experimentation
Disciplinary examples: Illustrates applications across various fields, enhancing relevance
Summarized by Shop
This user-oriented guide describes state-of-the-art methods for nonlinear equations and shows, via algorithms in pseudocode and Julia with several examples, how to choose an appropriate iterative method for a given problem and write an efficient solver or apply one written by others. A sequel to the author's Solving Nonlinear Equations w