Übersetzung für 'Kuhn-Tucker' von Englisch nach Deutsch

Kuhn-Tucker conditions

Kuhn-Tucker-Bedingungen {pl}math.

• Complementarity problems were originally studied because the Karush–Kuhn–Tucker conditions in linear programming and quadratic programming constitute a linear complementarity problem (LCP) or a mixed complementarity problem (MCP).
• If the problem has only equality constraints, then the method is equivalent to applying Newton's method to the first-order optimality conditions, or Karush–Kuhn–Tucker conditions, of the problem.
• With inequality constraints, the problem can be characterized in terms of the geometric optimality conditions, Fritz John conditions and Karush–Kuhn–Tucker conditions, under which simple problems may be solvable.
• This is the significance of the Karush–Kuhn–Tucker conditions.
• Common applications of Farkas' lemma include proving the strong duality theorem associated with linear programming and the Karush–Kuhn–Tucker conditions.

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• In mathematical optimization, the Karush–Kuhn–Tucker (KKT) conditions, also known as the Kuhn–Tucker conditions, are first derivative tests (sometimes called first-order necessary conditions) for a solution in nonlinear programming to be optimal, provided that some regularity conditions are satisfied.
• Another approach is to use an interior-point method that uses Newton-like iterations to find a solution of the Karush–Kuhn–Tucker conditions of the primal and dual problems.
• The problem is solved as a constrained optimization problem and is converged when the solver satisfies Karush–Kuhn–Tucker conditions.
• Karush–Kuhn–Tucker (KKT) conditions are available.
• They are used as lemma in the proof of the Karush–Kuhn–Tucker conditions, but they are relevant on their own.

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• , then the Karush–Kuhn–Tucker conditions are sufficient for a global minimum.
• In allowing inequality constraints, the Kuhn–Tucker approach generalized the classic method of Lagrange multipliers, which (until then) had allowed only equality constraints.
• Using the Karush–Kuhn–Tucker conditions, it can be shown that the optimization problem has a unique solution because the objective function in the optimization is concave in [...].
• In 1950, Albert Tucker gave the name and interpretation "prisoner's dilemma" to Merrill M.