Newton’s Method vs Other Optimization Algorithms
Now, we compare Newton’s Method with some other popular optimization algorithms.
Criteria | Newton’s Method | Gradient Descent (GD) | Quasi-Newton Methods | Genetic Algorithms |
---|---|---|---|---|
Convergence Rate | Quadratic | Linear | Faster than GD and slower than Newton’s | Typically slower than gradient-based methods |
Initialization Sensitivity | Sensitive | Less Sensitive | Less Sensitive | Less Sensitive |
Memory Requirement | Low | Low | Moderate | Moderate |
Derivative Requirement | First and second order derivatives | First order derivatives | First order derivatives | Doesn’t require derivatives |
Optimizer Type | Local | Local | Local | Global |
Complexity | Moderate | Low | Moderate | High |
Newton’s method in Machine Learning
Optimization algorithms are essential tools across various fields, ranging from engineering and computer science to economics and physics. Among these algorithms, Newton’s method holds a significant place due to its efficiency and effectiveness in finding the roots of equations and optimizing functions, here in this article we will study more about Newton’s method and it’s use in machine learning.
Table of Content
- Newton’s Method for Optimization
- Second-Order Approximation
- Newton’s Method for Finding Local Minima or Maxima in Python
- Convergence Properties of Newton’s Method
- Complexity of Newton’s Method
- Time Complexity of Newton’s Method
- Parameter Estimation in Logistic Regression using Newton’s Method
- Data Fitting with Newton’s Method
- Newton’s Method vs Other Optimization Algorithms
- Applications of Newton’s Method
Contact Us