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Projected Gradient Descent
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Related lectures (27)
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Conjugate Duality: Understanding Convex Optimization
Explores conjugate duality in convex optimization, covering weak and supporting hyperplanes, subgradients, duality gap, and strong duality conditions.
Optimality of Convergence Rates: Accelerated Gradient Descent
Explores the optimality of convergence rates in convex optimization, focusing on accelerated gradient descent and adaptive methods.
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.
Optimization Techniques: Gradient Descent and Convex Functions
Provides an overview of optimization techniques, focusing on gradient descent and properties of convex functions in machine learning.
Linear Programming Techniques in Reinforcement Learning
Covers the linear programming approach to reinforcement learning, focusing on its applications and advantages in solving Markov decision processes.
Proximal and Subgradient Descent: Optimization Techniques
Discusses proximal and subgradient descent methods for optimization in machine learning.
Legendre Transform
Explores the Legendre transform, duality in convex analysis, and optimization problems.