Quadratically constrained quadratic program

In mathematical optimization, a quadratically constrained quadratic program (QCQP) is an optimization problem in which both the objective function and the constraints are quadratic functions. It has the form

${\begin{aligned}&{\text{minimize}}&&{\tfrac {1}{2}}x^{\mathrm {T} }P_{0}x+q_{0}^{\mathrm {T} }x\\&{\text{subject to}}&&{\tfrac {1}{2}}x^{\mathrm {T} }P_{i}x+q_{i}^{\mathrm {T} }x+r_{i}\leq 0\quad {\text{for }}i=1,\dots ,m,\\&&&Ax=b,\end{aligned}}$

where P₀, … P_m are n-by-n matrices and x ∈ Rⁿ is the optimization variable.

If P₀, … P_m are all positive semidefinite, then the problem is convex. If these matrices are neither positive nor negative semidefinite, the problem is non-convex. If P₁, … P_m are all zero, then the constraints are in fact linear and the problem is a quadratic program.

Hardness

Solving the general case is an NP-hard problem. To see this, note that the two constraints x₁(x₁ − 1) ≤ 0 and x₁(x₁ − 1) ≥ 0 are equivalent to the constraint x₁(x₁ − 1) = 0, which is in turn equivalent to the constraint x₁ ∈ {0, 1}. Hence, any 0–1 integer program (in which all variables have to be either 0 or 1) can be formulated as a quadratically constrained quadratic program. Since 0–1 integer programming is NP-hard in general, QCQP is also NP-hard.

Relaxation

There are two main relaxations of QCQP: using semidefinite programming (SDP), and using the reformulation-linearization technique (RLT).

Semidefinite programming

When P₀, … P_m are all positive-definite matrices, the problem is convex and can be readily solved using interior point methods, as done with semidefinite programming.

Example

Max Cut is a problem in graph theory, which is NP-hard. Given a graph, the problem is to divide the vertices in two sets, so that as many edges as possible go from one set to the other. Max Cut can be formulated as a QCQP, and SDP relaxation of the dual provides good lower bounds.

Solvers and scripting (programming) languages

Name	Brief info
FICO Xpress	A commercial optimization solver for linear programming, non-linear programming, mixed integer linear programming, convex quadratic programming, convex quadratically constrained quadratic programming, second-order cone programming and their mixed integer counterparts.
AMPL
CPLEX	Popular solver with an API for several programming languages. Free for academics.
Gurobi	Solver with parallel algorithms for large-scale linear programs, quadratic programs and mixed-integer programs. Free for academic use.
JOptimizer	Java library for convex optimization (open source)
MOSEK	A solver for large scale optimization with API for several languages (C++,java,.net, Matlab and python)
OpenOpt	universal cross-platform numerical optimization framework, see its QCQP page and other problems involved. Uses NumPy arrays and SciPy sparse matrices.
TOMLAB	Supports global optimization, integer programming, all types of least squares, linear, quadratic and unconstrained programming for MATLAB. TOMLAB supports solvers like Gurobi, CPLEX, SNOPT and KNITRO.

References

• Boyd, Stephen; Lieven Vandenberghe (2004). Convex Optimization. Cambridge: Cambridge University Press. ISBN 0-521-83378-7.

Further reading

In statistics

• Albers CJ, Critchley F, Gower, JC (2011). "Quadratic Minimisation Problems in Statistics". Journal of Multivariate Analysis. 102 (3): 698–713. doi:10.1016/j.jmva.2009.12.018.{{cite journal}}: CS1 maint: multiple names: authors list (link)

External links

• NEOS Optimization Guide: Quadratic Constrained Quadratic Programming