Quadratic Programming

Quadratic programming (QP) is a special type of mathematical optimization problem. It is the problem of optimizing (minimizing or maximizing) a quadratic function of several variables subject to linear constraints on these variables.

Read more about Quadratic Programming:  Problem Formulation, Solution Methods, Lagrangian Duality, Complexity, Solvers and Scripting (programming) Languages

Other articles related to "programming, quadratic programming, quadratic":

Galahad Library
... library of packages for the solution of optimization—or mathematical programming—problems ... library are unconstrained and bound-constrained optimization, quadratic programming, nonlinear programming, systems of nonlinear equations and inequalities, and nonlinear least squares ... The library is mostly written in the Fortran 90 programming language ...
Optimization - Major Subfields
... Convex programming studies the case when the objective function is convex (minimization) or concave (maximization) and the constraint set is convex ... This can be viewed as a particular case of nonlinear programming or as generalization of linear or convex quadratic programming ... Linear programming (LP), a type of convex programming, studies the case in which the objective function f is linear and the set of constraints is specified using only linear equalities and ...
Quadratic Programming - Solvers and Scripting (programming) Languages
... CPLEX Popular solver with an API for several programming languages ... Solver with parallel algorithms for large-scale linear programs, quadratic programs and mixed-integer programs ... MATLAB A general-purpose and matrix-oriented programming-language for numerical computing ...

Famous quotes containing the word programming:

    If there is a price to pay for the privilege of spending the early years of child rearing in the driver’s seat, it is our reluctance, our inability, to tolerate being demoted to the backseat. Spurred by our success in programming our children during the preschool years, we may find it difficult to forgo in later states the level of control that once afforded us so much satisfaction.
    Melinda M. Marshall (20th century)