The quadratic programming (QP) problem involves minimizing a quadratic function subject to linear constraints. A general formulation is
$\begin{array}{lcll} \mbox{minimize} & \frac{1}{2} x^T Q x + c^T x & & \\ \mbox{subject to} & a_i^T x = b_i & \forall i \in \mathcal{E} \\ & a_i^T x \geq b_i & \forall i \in \mathcal{I} \end{array}$ where $$Q \in R^{n\times n}$$ is symmetric, and the index sets $$\mathcal{E}$$ and $$\mathcal{I} \,$$ specify the equality and inequality constraints, respectively. Quadratic programs are an important class of problems on their own and as subproblems in methods for general constrained optimization problems, such as sequential quadratic programming (SQP) and augmented Lagrangian methods.

The difficulty of solving the QP problem depends largely on the nature of the matrix $$Q$$. For convex QPs, the matrix $$Q$$ is positive semidefinite on the feasible set; there are polynomial-time algorithms to solve these relatively easy problems. For non-convex QPs in which the matrix $$Q$$ has negative eigenvalues, the objective function may have more than one local minimizer; in this case, the problem is NP-complete. An extreme example is the problem
$\begin{array}{lllll} \mbox{minimize} & – x^T x & & & \\ \mbox{subject to} & -1 \leq & x_i & \leq 1, & \forall i=1,\ldots,n \end{array}$ which has a minimizer at any $$x$$ with $$|x_i| \,=1$$ for $$i = 1,…, n \,$$. In this extreme case, there are $$\, 2^n_{} \,$$ local minimizers.

##### Optimality conditions

The necessary optimality conditions for vector $$x^*_{}$$ to be a local minimizer are
(1) that it should be primal feasible:

$$a_i^T x^* = b_i$$ for $$i \in \mathcal{E}$$ and $$a_i^T x^* \geq b_i$$ for $$i \in \mathcal{I}$$,

(2) that it should be dual feasible:
$$Q x^* + c = \sum_{i \in \mathcal{E} \cup \mathcal{I}} a_i y_i^* \,$$ and $$y_i^* \geq 0$$ for $$i \in I$$,
for some vector of Lagrange multipliers $$y^*_{} \,$$, and

(3) that the complementary slackness condition holds:
$$( a_i^T x^* – b_i ) y_i^* = 0$$ for all $$i \in \mathcal{I}$$.

These requirements are commonly known as the Karush-Kuhn-Tucker (KKT) conditions.

A KKT point is a local minimizer if and only if $$s^T H s \geq 0$$ for all vectors $$s \in \mathcal{S}$$, where
$\mathcal{S} = \left\{\begin{array}{ll} s : & a_i^T s = 0 \, \mbox{for } \, i \in \mathcal{E} \, \mbox{and } \, i \in \mathcal{I} \, \mbox{such that } \, a_i^T x^* = b_i \, \mbox{and } \, y^*_i > 0 \, \mbox{and } \\ & a_i^T s \geq 0 \, \mbox{for } \, i \in \mathcal{I} \, \mbox{such that } \, a_i^T x^* = b_i \, \mbox{and } \, y^*_i = 0 \end{array} \right\}$

This second-order condition is trivially satisfied for convex problems but may be hard (NP-complete) to check for non-convex ones if there are many $$i \in \mathcal{I}$$ for which $$a_i^T x^* = b_i$$ and $$y^*_i = 0$$.

##### Algorithms

Linear programming is a special case of quadratic programming when the matrix $$Q = 0$$. Linear least squares problems are QPs; Levenberg-Marquardt and Gauss-Newton are specialized methods for solving them. If there are no constraints in the QP, then the problem is similar to solving a system of equations, for which there are many techniques (e.g. Conjugate Gradient Method or direct factorizations such as LU or Cholesky Decompositions).

Quadratic programming (QP) problems can be viewed as special types of more general problems, so they can be solved by software packages for these more general problems. Quadratically constrained quadratic programming (QCQP) problems generalize QPs in that the constraints are quadratic instead of linear. Second order cone programming (SOCP) problems generalize QCQPs, and nonlinear programming (NLP) problems generalize SOCPs.

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