Optimization Masterclass - Hands-on: How to Solve Convex Optimization Problems in CVXPY Ep6 скачать в хорошем качестве

Optimization Masterclass - Hands-on: How to Solve Convex Optimization Problems in CVXPY Ep6

Optimization Masterclass - Ep 6: How to Solve Convex Optimization Problems in CVXPY Smart Handout: https://colab.research.google.com/dri... In this video we take a hands-on approach to solving optimization problems using CVXPY, a powerful open-source Python library for convex optimization. CVXPY allows you to focus on modeling rather than algorithm selection, ensures code readability through math-like syntax, and supports multiple solvers. We walk through two complete practical example step-by-step: solving a norm approximation problem and regularization problem using CVXPY. This tutorial is ideal for those who want to learn how to implement optimization models in code and understand how CVXPY verifies convexity and solves problems reliably. All code and materials are linked above. CVXPY website: https://www.cvxpy.org/ ➡️Subscribe to follow the entire Optimization Masterclass series    • Optimization Masterclass   and hit the Notification Bell 🔔 so you don't miss future episodes covering linear programming, duality, gradient methods, and more! 👍 Like this video if you find it helpful! 💬 Comment below with your questions or what you'd like to see next! Resources: My Research Lab page: https://giordanoscarciotti.com/ Smart Handout: https://colab.research.google.com/dri... Recommended Textbooks: https://web.stanford.edu/~boyd/cvxboo... Connect: LinkedIn:   / giordano-scarciotti-70167115   #Optimization #ConvexOptimization #MathematicalOptimization #ImperialCollegeLondon #OptimizationMasterclass #MachineLearning #DataScience #Engineering #OperationsResearch #lecture CHAPTERS: 00:00 Introduction 00:46 Why CVXPY? 03:31 First example: basic norm approximation 19:14 Common error 25:25 Recap first example 33:24 Second example: Ridge vs Lasso regression 45:54 Recap second example 46:56 Intro to Disciplined Convex Programming 52:51 Conclusion