The KKT conditions, or Karush-Kuhn-Tucker conditions, are a set of mathematical criteria used to find the optimal solutions of constrained optimization problems. They are especially important in variational analysis as they provide necessary conditions for a solution to be optimal when there are both equality and inequality constraints involved in the optimization process. Understanding these conditions is crucial for analyzing algorithms in machine learning and data science that rely on optimization techniques.
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