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Convex Optimization Tutorial: KKT Conditions
This lecture covers the Karush-Kuhn-Tucker (KKT) conditions in the context of convex optimization. The instructor explains how to derive dual problems using conic duality, solve optimization problems with logarithmic constraints, and apply KKT conditions to least squares and matrix functions. The lecture also explores the suboptimality of covering ellipsoids and demonstrates the Langrangian dual of convex optimization problems.
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