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  2. Variational inequality - Wikipedia

    en.wikipedia.org/wiki/Variational_inequality

    The first problem involving a variational inequality was the Signorini problem, posed by Antonio Signorini in 1959 and solved by Gaetano Fichera in 1963, according to the references (Antman 1983, pp. 282–284) and (Fichera 1995): the first papers of the theory were (Fichera 1963) and (Fichera 1964a), (Fichera 1964b).

  3. Calculus of variations - Wikipedia

    en.wikipedia.org/wiki/Calculus_of_Variations

    The calculus of variations began with the work of Isaac Newton, such as with Newton's minimal resistance problem, which he formulated and solved in 1685, and later published in his Principia in 1687, [2] which was the first problem in the field to be formulated and correctly solved, [2] and was also one of the most difficult problems tackled by variational methods prior to the twentieth century.

  4. Differential variational inequality - Wikipedia

    en.wikipedia.org/wiki/Differential_variational...

    DVIs are related to a number of other concepts including differential inclusions, projected dynamical systems, evolutionary inequalities, and parabolic variational inequalities. Differential variational inequalities were first formally introduced by Pang and Stewart, whose definition should not be confused with the differential variational ...

  5. Total variation distance of probability measures - Wikipedia

    en.wikipedia.org/wiki/Total_variation_distance...

    Total variation distance is half the absolute area between the two curves: Half the shaded area above. In probability theory, the total variation distance is a statistical distance between probability distributions, and is sometimes called the statistical distance, statistical difference or variational distance.

  6. Gradient descent - Wikipedia

    en.wikipedia.org/wiki/Gradient_descent

    This method is a specific case of the forward-backward algorithm for monotone inclusions (which includes convex programming and variational inequalities). [ 31 ] Gradient descent is a special case of mirror descent using the squared Euclidean distance as the given Bregman divergence .

  7. 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 ...

  8. Janson inequality - Wikipedia

    en.wikipedia.org/wiki/Janson_inequality

    Janson's Inequality has been used in pseudorandomness for bounds on constant-depth circuits. [1] Research leading to these inequalities were originally motivated by estimating chromatic numbers of random graphs. [2]

  9. Hardy's inequality - Wikipedia

    en.wikipedia.org/wiki/Hardy's_inequality

    Hardy's inequality is an inequality in mathematics, named after G. H. Hardy. Its discrete version states that if a 1 , a 2 , a 3 , … {\displaystyle a_{1},a_{2},a_{3},\dots } is a sequence of non-negative real numbers , then for every real number p > 1 one has