Convex Optimization: Elementary Results

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Explores convex sets, their properties, and applications in optimization.

Explores convex functions, affine transformations, pointwise maximum, minimization, Schur's Lemma, and relative entropy in mathematical optimization.

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

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

Covers the KKT conditions and Slater's condition in convex optimization problems.

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

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

Explores convex constrained optimization through Projected Gradient Descent, focusing on tangent space and iterative minimization.

Covers geodesically convex optimization on Riemannian manifolds, exploring convexity properties and minimization relationships.