This lecture covers optimization problems and how they can be solved using greedy algorithms, illustrated by the Cashier's Algorithm for making change. The instructor proves the optimality of the algorithm for U.S. coins.
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Explores KKT conditions in convex optimization, covering dual problems, logarithmic constraints, least squares, matrix functions, and suboptimality of covering ellipsoids.
