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  2. Lagrange multiplier - Wikipedia

    en.wikipedia.org/wiki/Lagrange_multiplier

    In mathematical optimization, the method of Lagrange multipliers is a strategy for finding the local maxima and minima of a function subject to equation constraints (i.e., subject to the condition that one or more equations have to be satisfied exactly by the chosen values of the variables). [1] It is named after the mathematician Joseph-Louis ...

  3. Augmented Lagrangian method - Wikipedia

    en.wikipedia.org/wiki/Augmented_Lagrangian_method

    Augmented Lagrangian methods are a certain class of algorithms for solving constrained optimization problems. They have similarities to penalty methods in that they replace a constrained optimization problem by a series of unconstrained problems and add a penalty term to the objective, but the augmented Lagrangian method adds yet another term designed to mimic a Lagrange multiplier.

  4. Karush–Kuhn–Tucker conditions - Wikipedia

    en.wikipedia.org/wiki/Karush–Kuhn–Tucker...

    Allowing inequality constraints, the KKT approach to nonlinear programming generalizes the method of Lagrange multipliers, which allows only equality constraints. Similar to the Lagrange approach, the constrained maximization (minimization) problem is rewritten as a Lagrange function whose optimal point is a global maximum or minimum over the ...

  5. Lagrange multipliers on Banach spaces - Wikipedia

    en.wikipedia.org/wiki/Lagrange_multipliers_on...

    In the field of calculus of variations in mathematics, the method of Lagrange multipliers on Banach spaces can be used to solve certain infinite-dimensional constrained optimization problems. The method is a generalization of the classical method of Lagrange multipliers as used to find extrema of a function of finitely many variables.

  6. Lagrangian relaxation - Wikipedia

    en.wikipedia.org/wiki/Lagrangian_relaxation

    The method penalizes violations of inequality constraints using a Lagrange multiplier, which imposes a cost on violations. These added costs are used instead of the strict inequality constraints in the optimization. In practice, this relaxed problem can often be solved more easily than the original problem.

  7. Duality (optimization) - Wikipedia

    en.wikipedia.org/wiki/Duality_(optimization)

    Another condition in which the min-max and max-min are equal is when the Lagrangian has a saddle point: (x∗, λ∗) is a saddle point of the Lagrange function L if and only if x∗ is an optimal solution to the primal, λ∗ is an optimal solution to the dual, and the optimal values in the indicated problems are equal to each other. [18 ...

  8. Least-squares adjustment - Wikipedia

    en.wikipedia.org/wiki/Least-squares_adjustment

    Krakiwsky, "A synthesis of recent advances in the method of least squares", Lecture Notes #42, Department of Geodesy and Geomatics Engineering, University of New Brunswick, 1975 Cross, P.A. "Advanced least squares applied to position-fixing" , University of East London , School of Surveying, Working Paper No. 6, ISSN 0260-9142 , January 1994.

  9. Lagrange's theorem - Wikipedia

    en.wikipedia.org/wiki/Lagrange's_theorem

    Lagrange's theorem (group theory) Lagrange's theorem (number theory) Lagrange's four-square theorem, which states that every positive integer can be expressed as the sum of four squares of integers; Mean value theorem in calculus; The Lagrange inversion theorem; The Lagrange reversion theorem; The method of Lagrangian multipliers for ...