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  2. 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 ...

  3. 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 ...

  4. Sequential quadratic programming - Wikipedia

    en.wikipedia.org/wiki/Sequential_quadratic...

    Sequential quadratic programming (SQP) is an iterative method for constrained nonlinear optimization, also known as Lagrange-Newton method.SQP methods are used on mathematical problems for which the objective function and the constraints are twice continuously differentiable, but not necessarily convex.

  5. Lagrangian - Wikipedia

    en.wikipedia.org/wiki/Lagrangian

    Lagrangian dual problem, the problem of maximizing the value of the Lagrangian function, in terms of the Lagrange-multiplier variable; See Dual problem; Lagrangian, a functional whose extrema are to be determined in the calculus of variations; Lagrangian submanifold, a class of submanifolds in symplectic geometry; Lagrangian system, a pair ...

  6. Karush–Kuhn–Tucker conditions - Wikipedia

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

    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 domain of the choice variables and a global minimum (maximum) over the multipliers.

  7. Lagrangian relaxation - Wikipedia

    en.wikipedia.org/wiki/Lagrangian_relaxation

    A Lagrangian relaxation algorithm thus proceeds to explore the range of feasible values while seeking to minimize the result returned by the inner problem. Each value returned by P {\displaystyle P} is a candidate upper bound to the problem, the smallest of which is kept as the best upper bound.

  8. Dual linear program - Wikipedia

    en.wikipedia.org/wiki/Dual_linear_program

    The strong duality theorem says that if one of the two problems has an optimal solution, so does the other one and that the bounds given by the weak duality theorem are tight, i.e.: max x c T x = min y b T y. The strong duality theorem is harder to prove; the proofs usually use the weak duality theorem as a sub-routine.

  9. N = 4 supersymmetric Yang–Mills theory - Wikipedia

    en.wikipedia.org/wiki/N_=_4_supersymmetric_Yang...

    There is a duality between Type IIB string theory on AdS 5 × S 5 space (a product of 5-dimensional AdS space with a 5-dimensional sphere) and N = 4 super Yang–Mills on the 4-dimensional boundary of AdS 5. However, this particular realization of the AdS/CFT correspondence is not a realistic model of gravity, since gravity in our universe is 4 ...