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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.

The quadratic programming problem can be formulated as:[1]

Assume x belongs to $\mathbb{R}^{n}$ space. The n×n matrix Q is symmetric, and c is any n×1 vector.

Minimize (with respect to x)

Subject to one or more constraints of the form:

where $\mathbf{x}^T$ indicates the vector transpose of $\mathbf{x}$. The notation $Ax \leq b$ means that every entry of the vector Ax is less than or equal to the corresponding entry of the vector $\mathbf b$.

If the matrix $Q\;$ is positive semidefinite matrix, then f() is a convex function: In this case the quadratic program has a global minimizer if there exists some feasible vector x (satisfying the constraints) and if f() is bounded below on the feasible region. If the matrix Q is positive definite and the problem has a feasible solution, then the global minimizer is unique.

If $Q\;$ is zero, then the problem becomes a linear program.

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### Solution methods

If there are only equality constraints, then the QP can be solved by a linear system. Otherwise, a variety of methods for solving the QP are commonly used, including

Convex quadratic programming is a special case of the more general field of convex optimization.