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KKT conditions generalize Lagrange multipliers to handle inequality constraints in constrained optimization problems.
The Hessian matrix collects all second-order partial derivatives of a scalar function and measures local curvature.
The Minimax Theorem states that in zero-sum two-player games with suitable convexity and compactness, the best guaranteed payoff for the maximizer equals the worst-case loss for the minimizer.
Lagrangian duality turns a constrained minimization problem into a related maximization problem that provides lower bounds on the original objective.