Quadratic programming

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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.


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.

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