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This lecture covers the concept of convexifying nonconvex problems using techniques such as the kernel trick, sensitivity interpretation of dual solutions, and nonlinear dimensionality reduction. It explores the optimization problems involved in unfolding k-nearest neighbor graphs and provides insights into the exact solutions of convex-cardinality problems.
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