Lecture
Optimization Basics: Norms, Convexity, Differentiability
Related lectures (41)
Convex Functions
Covers the properties and operations of convex functions.
Mathematics of Data: Optimization Basics
Covers basics on optimization, including norms, Lipschitz continuity, and convexity concepts.
Convexity and Jacobians
Explores convexity, Jacobians, subdifferentials, and convergence rates in optimization and function analysis.
Mathematics of Data: Optimization Basics
Covers optimization basics, including metrics, norms, convexity, gradients, and logistic regression, with a focus on strong convexity and convergence rates.
Vector Differentiation and Convex Functions
Covers the concepts of vector differentiation and convex functions.
Primal-dual Optimization: Fundamentals
Explores primal-dual optimization, minimax problems, and gradient descent-ascent methods for optimization algorithms.
Meromorphic Functions & Differentials
Explores meromorphic functions, poles, residues, orders, divisors, and the Riemann-Roch theorem.
Convex Optimization
Introduces convex optimization, focusing on the importance of convexity in algorithms and optimization problems.
Gradient Descent Methods: Theory and Computation
Explores gradient descent methods for smooth convex and non-convex problems, covering iterative strategies, convergence rates, and challenges in optimization.
Convex Optimization: Gradient Descent
Explores VC dimension, gradient descent, convex sets, and Lipschitz functions in convex optimization.