Newton's method | Wolfe Condition | Theory and Python Code | Optimization Algorithms #3

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In this one, I will show you what the (damped) newton algorithm is and how to use it with the Wolfe condition for backtracking. We will approach both methods from intuitive and animated perspectives. Next, let’s talk about the line search we are going to use in this tutorial, which is based on Wolfe criterion. This is achieved by the Wolfe condition, which sufficiently decreases our function ! The Wolfe condition combines both the Armijo condition as well as an additional curvature condition to formulate the strong Wolfe condition. The curvature condition ensures a sufficient increase of the gradient. This is also a strong wolfe condition, which restricts slopes from getting too positive, hence excluding points far away from stationary points.






⏲Outline⏲


00:00 Introduction
00:57 (Damped) Newton Method
03:27 Wolfe Criterion
04:44 Python Implementation
17:55 Animation Module
32:42 Animating Iterations
35:57 Outro


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