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  2. Saddle point - Wikipedia

    en.wikipedia.org/wiki/Saddle_point

    A saddle point (in red) on the graph of z = x 2 − y 2 (hyperbolic paraboloid). In mathematics, a saddle point or minimax point [1] is a point on the surface of the graph of a function where the slopes (derivatives) in orthogonal directions are all zero (a critical point), but which is not a local extremum of the function. [2]

  3. Karush–Kuhn–Tucker conditions - Wikipedia

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

    Theorem — (sufficiency) If there exists a solution to the primal problem, a solution (,) to the dual problem, such that together they satisfy the KKT conditions, then the problem pair has strong duality, and , (,) is a solution pair to the primal and dual problems.

  4. Method of steepest descent - Wikipedia

    en.wikipedia.org/wiki/Method_of_steepest_descent

    In mathematics, the method of steepest descent or saddle-point method is an extension of Laplace's method for approximating an integral, where one deforms a contour integral in the complex plane to pass near a stationary point (saddle point), in roughly the direction of steepest descent or stationary phase. The saddle-point approximation is ...

  5. Max–min inequality - Wikipedia

    en.wikipedia.org/wiki/Max–min_inequality

    A theorem giving conditions on f, W, and Z which guarantee the saddle point property is called a minimax theorem. Proof. Define () (,) . ...

  6. Second partial derivative test - Wikipedia

    en.wikipedia.org/wiki/Second_partial_derivative_test

    At the remaining critical point (0, 0) the second derivative test is insufficient, and one must use higher order tests or other tools to determine the behavior of the function at this point. (In fact, one can show that f takes both positive and negative values in small neighborhoods around (0, 0) and so this point is a saddle point of f.)

  7. Uzawa iteration - Wikipedia

    en.wikipedia.org/wiki/Uzawa_iteration

    In numerical mathematics, the Uzawa iteration is an algorithm for solving saddle point problems. It is named after Hirofumi Uzawa and was originally introduced in the context of concave programming. [ 1 ]

  8. Ladyzhenskaya–Babuška–Brezzi condition - Wikipedia

    en.wikipedia.org/wiki/Ladyzhenskaya–Babuška...

    For saddle point problems, however, many discretizations are unstable, giving rise to artifacts such as spurious oscillations. The LBB condition gives criteria for when a discretization of a saddle point problem is stable. The condition is variously referred to as the LBB condition, the Babuška–Brezzi condition, or the "inf-sup" condition.

  9. Hyperbolic equilibrium point - Wikipedia

    en.wikipedia.org/wiki/Hyperbolic_equilibrium_point

    Near a hyperbolic point the orbits of a two-dimensional, non-dissipative system resemble hyperbolas. This fails to hold in general. Strogatz notes that "hyperbolic is an unfortunate name—it sounds like it should mean 'saddle point'—but it has become standard." [1] Several properties hold about a neighborhood of a hyperbolic point, notably [2]