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The Karush-Kuhn-Tucker (KKT) conditions are a set of optimality conditions for optimization problems in terms of the optimization variables and Lagrange multipliers.
This concept has the prerequisites:
- convex optimization (The KKT conditions characterize optimal solutions to convex optimization problems.)
- Langrange duality (The KKT conditions involve both the primal and dual variables.)
- gradient (The optimality conditions are given in terms of gradients.)
Core resources (read/watch one of the following)
→ Convex Optimization
A graduate-level textbook on convex optimization.
Location: Section 5.5, "Optimality conditions," of Chapter 5, "Duality," pages 241-249
- Applications in machine learning and statistics:
- Applying the conditions to support vector machines (SVMs) is critical to show sparsity, the property that lets them be kernelized.
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