Convex analysis

Related lectures (29)

Covers Distributionally Robust Portfolio Optimization for maximizing expected utility with uncertain asset returns.

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

Introduces proximal operators and conditional gradient methods for composite convex minimization problems in data optimization.

Explores KKT conditions in convex optimization, covering dual cones, properties, generalized inequalities, and optimality conditions.

Explores dual cones, generalized inequalities, SDP duality, and KKT conditions in convex optimization.

Explores robust optimization in radiation therapy and support vector machines, emphasizing worst-case scenarios and the use of linear decision rules.

Covers convex optimization problems, transformation to standard form, and optimality criteria for differentiable objectives.

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

Explores convex optimization in nonlinear dimensionality reduction, showcasing practical applications in signal processing and regression tasks.

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