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Related lectures (31)

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KKT Conditions: Convex Optimization

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

Convex Functions: Elementary Concepts

Introduces elementary concepts of convex functions, including hulls, transformations, conditions, and examples.

Convex Functions: Theory and Applications

Introduces convex functions, covering affine, convex, and conic hulls, transformations, inequalities, and conditions for convexity.

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.

Convex Optimization

Introduces convex optimization, focusing on the importance of convexity in algorithms and optimization problems.

Convex Hulls: Complexity and Vertices

Explores the complexity of convex hulls and the concept of vertices within them.

Optimal Transport: Theory and Applications

Explores Lagrange multipliers, minimax theorems, and convex subsets in optimal transport theory.

Density Matrix: Recap and Bloch 'Ball'

Covers the concept of density matrix and its properties, focusing on statistical mixtures and measurements.

Diophantine Approximation: Minbowski's Theorem

Covers Minbowski's Theorem on Diophantine Approximation and Gram-Schmidt orthogonalization.

Conjugate Duality: Understanding Convex Optimization

Explores conjugate duality in convex optimization, covering weak and supporting hyperplanes, subgradients, duality gap, and strong duality conditions.