Theorie

Introduction

The Karush-Kuhn-Tucker (KKT) Theorem is a model of nonlinear optimization (NLP). The model is based on the Langrangian optimization, but considers inequality as part of the KKT constraints. The approach proofs the optimality of a (given) point concerning a nonlinear objective function. The satisfaction of KKT constraint is a necessary condition for a solution being optimal in NLP.

KKT Conditions

The six KKT condition are based on the Langrangian function of a random maximization (or minimization) problem.

Fehler beim Parsen (http://mathoid.testme.wmflabs.org Serverantwort ist ungültiges JSON.): L(x,\lambda) = f(x_1 , ... , x_n ) - \sum_{i=0} ^k \lambda_i * g (x_1,...,x_n)