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Lecture
Primal-dual Optimization: Lagrangian Methods
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Nonlinear Programming: Part I
Covers the fundamentals of Nonlinear Programming and its applications in Optimal Control, exploring techniques, examples, optimality definitions, and necessary conditions.
Primal-dual Optimization: Fundamentals
Explores primal-dual optimization, minimax problems, and gradient descent-ascent methods for optimization algorithms.
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.
Optimization with Constraints: KKT Conditions
Covers the KKT conditions for optimization with constraints, essential for solving constrained optimization problems efficiently.
Primal-dual Optimization III: Lagrangian Gradient Methods
Explores primal-dual optimization methods, emphasizing Lagrangian gradient techniques and their applications in data optimization.
Primal-dual Optimization: Extra-Gradient Method
Explores the Extra-Gradient method for Primal-dual optimization, covering nonconvex-concave problems, convergence rates, and practical performance.
Convex Optimization
Introduces convex optimization, focusing on the importance of convexity in algorithms and optimization problems.
Applications of GAMP
Delves into applying the GAMP algorithm to simplify the lasso problem and analyze optimization challenges in neural networks.
Proximal and Subgradient Descent: Optimization Techniques
Discusses proximal and subgradient descent methods for optimization in machine learning.
KKT for convex problems and Slater's CQ
Covers the KKT conditions and Slater's condition in convex optimization problems.