**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":

... Convex

**programming**studies the case when the objective function is convex (minimization) or concave (maximization) and the constraint set is convex ... 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 inequalities ...

**Quadratic Programming**- Solvers and Scripting (programming) Languages

... Name Brief info AIMMS AMPL CPLEX Popular solver with an API for several

**programming**languages ... algorithms for large-scale linear programs,

**quadratic**programs and mixed-integer programs ... A general-purpose and matrix-oriented

**programming**-language for numerical computing ...

... of packages for the solution of optimizationâ€”or mathematical

**programming**â€”problems ... The areas covered by the library are unconstrained and bound-constrained optimization,

**quadratic programming**, nonlinear

**programming**, systems of nonlinear equations ... The library is mostly written in the Fortran 90

**programming**language ...

### 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)