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Convex Optimization

Convexity identifies optimization problems where local information can support global guarantees. Build this branch around:

  1. convex sets and functions;
  2. standard problem forms and transformations;
  3. optimality conditions;
  4. Lagrange duality;
  5. first- and second-order methods;
  6. applications in statistics and machine learning.

This is currently a seed map. The canonical external path is Stanford EE364A, paired with Boyd and Vandenberghe's Convex Optimization.