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

☕️ Buy me a coffee: https://paypal.me/donationlink240 🙏🏻 Support me on Patreon:   / ahmadbazzi   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 📚Related Courses: 📚 Convex Optimization Extended Course    • Convex Optimization   📚 Python Programming Extended Course    • Python Programming   📚 Convex Optimization Applications Extended Course    • The Transshipment Problem in Decision Maki...   📚 Linear Algebra Extended Course    • Linear Algebra   📚 Python projects course    • Python   🔴 Subscribe for more videos on CUDA programming 👍 Smash that like button, in case you find this tutorial useful. 👁‍🗨 Speak up and comment, I am all ears. 💰 If you are able to, donate to help the channel Patreon -   / ahmadbazzi   #python #optimization #algorithm