Lectures related to Convex Optimization

Optimization Techniques: Convexity in Machine Learning

Covers optimization techniques in machine learning, focusing on convexity and its implications for efficient problem-solving.

Proximal and Subgradient Descent: Optimization Techniques

Discusses proximal and subgradient descent methods for optimization in machine learning.

Faster and Projected Gradient Descent: Optimization Techniques

Discusses advanced optimization techniques, focusing on faster and projected gradient descent methods in machine learning.

Optimality of Convergence Rates: Accelerated Gradient Descent

Explores the optimality of convergence rates in convex optimization, focusing on accelerated gradient descent and adaptive methods.

Subgradients and Convex Functions

Explores subgradients in convex functions, emphasizing non-differentiable yet convex scenarios and properties of subdifferentials.

Optimization Basics: Norms, Convexity, Differentiability

Explores optimization basics such as norms, convexity, and differentiability, along with practical applications and convergence rates.

The Hidden Convex Optimization Landscape of Deep Neural Networks

Explores the hidden convex optimization landscape of deep neural networks, showcasing the transition from non-convex to convex models.

Optimization with Constraints: KKT Conditions

Covers the KKT conditions for optimization with constraints, essential for solving constrained optimization problems efficiently.

Stochastic Gradient Descent: Non-convex Optimization Techniques

Discusses Stochastic Gradient Descent and its application in non-convex optimization, focusing on convergence rates and challenges in machine learning.

Gradient Descent: Principles and Applications

Covers gradient descent, its principles, applications, and convergence rates in optimization for machine learning.

Optimization Techniques: Convexity and Algorithms in Machine Learning Log in to Mediaspace to watch this video

Covers optimization techniques in machine learning, focusing on convexity, algorithms, and their applications in ensuring efficient convergence to global minima.

Proximal Gradient Descent: Optimization Techniques in Machine Learning Log in to Mediaspace to watch this video

Discusses proximal gradient descent and its applications in optimizing machine learning algorithms.

Optimization Techniques: Gradient Descent and Convex Functions Log in to Mediaspace to watch this video

Provides an overview of optimization techniques, focusing on gradient descent and properties of convex functions in machine learning.

Faster Gradient Descent: Projected Optimization Techniques Log in to Mediaspace to watch this video

Covers faster gradient descent methods and projected gradient descent for constrained optimization in machine learning.

Convex Optimization: Gradient Flow Log in to Mediaspace to watch this video

Explores convex optimization, emphasizing the importance of minimizing functions within a convex set and the significance of continuous processes in studying convergence rates.

KKT and Convex Optimization Log in to Mediaspace to watch this video

Covers the KKT conditions and convex optimization, discussing constraint qualifications and tangent cones of convex sets.

Convex Optimization: Gradient Descent Log in to Mediaspace to watch this video

Explores VC dimension, gradient descent, convex sets, and Lipschitz functions in convex optimization.

Optimal Transport: Rockafellar Theorem Log in to Mediaspace to watch this video

Explores the Rockafellar Theorem in optimal transport, focusing on c-cyclical monotonicity and convex functions.

Convex Optimization Log in to Mediaspace to watch this video

Introduces the fundamentals of convex optimization, emphasizing the significance of convex functions in simplifying the minimization process.

Convex Optimization: Elementary Results Log in to Mediaspace to watch this video

Explores elementary results in convex optimization, including affine, convex, and conic hulls, proper cones, and convex functions.