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  2. Linear inequality - Wikipedia

    en.wikipedia.org/wiki/Linear_inequality

    Two-dimensional linear inequalities are expressions in two variables of the form: + < +, where the inequalities may either be strict or not. The solution set of such an inequality can be graphically represented by a half-plane (all the points on one "side" of a fixed line) in the Euclidean plane. [2]

  3. Linear matrix inequality - Wikipedia

    en.wikipedia.org/wiki/Linear_matrix_inequality

    In convex optimization, a linear matrix inequality (LMI) is an expression of the form ⁡ ():= + + + + where = [, =, …,] is a real vector,,,, …, are symmetric matrices, is a generalized inequality meaning is a positive semidefinite matrix belonging to the positive semidefinite cone + in the subspace of symmetric matrices .

  4. Inequation - Wikipedia

    en.wikipedia.org/wiki/Inequation

    Computer support in solving inequations is described in constraint programming; in particular, the simplex algorithm finds optimal solutions of linear inequations. [6] The programming language Prolog III also supports solving algorithms for particular classes of inequalities (and other relations) as a basic language feature.

  5. List of inequalities - Wikipedia

    en.wikipedia.org/wiki/List_of_inequalities

    Azuma's inequality; Bennett's inequality, an upper bound on the probability that the sum of independent random variables deviates from its expected value by more than any specified amount

  6. Linear programming - Wikipedia

    en.wikipedia.org/wiki/Linear_programming

    Linear programming (LP), also called linear optimization, is a method to achieve the best outcome (such as maximum profit or lowest cost) in a mathematical model whose requirements and objective are represented by linear relationships. Linear programming is a special case of mathematical programming (also known as mathematical optimization).

  7. Relaxation (iterative method) - Wikipedia

    en.wikipedia.org/wiki/Relaxation_(iterative_method)

    Relaxation methods were developed for solving large sparse linear systems, which arose as finite-difference discretizations of differential equations. [2] [3] They are also used for the solution of linear equations for linear least-squares problems [4] and also for systems of linear inequalities, such as those arising in linear programming.

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