Overview

This chapter covers optimization methods. We will focus on the optimization algorithms motivated by data analysis and machine learning.

Lectures

• Convexity
• Rate of Convergence
• PyTorch Basics
• Gradient Descent
• Accelerated Gradient Descent
• Stochastic Gradient Descent
• Proximal Gradient Descent
• Mirror Descent
• Nesterov's Smooth Method
• Duality and ADMM