Quadratically constrained quadratic program

From Wikipedia, the free encyclopedia
Jump to: navigation, search

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

& \text{minimize} && \tfrac12 x^\mathrm{T} P_0 x + q_0^\mathrm{T} x \\
& \text{subject to} && \tfrac12 x^\mathrm{T} P_i x + q_i^\mathrm{T} x + r_i \leq 0 \quad \text{for } i = 1,\dots,m , \\
&&& Ax = b, 

where P0, … Pm are n-by-n matrices and xRn is the optimization variable.

If P0, … Pm are all positive semidefinite, then the problem is convex. If these matrices are neither positive or negative semidefinite, the problem is non-convex. If P1, … Pm are all zero, then the constraints are in fact linear and the problem is a quadratic program.


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.


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

Semidefinite programming[edit]

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


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[edit]

Name Brief info
CPLEX Popular solver with an API for several programming languages. Free for academics.
JOptimizer Java library for convex optimization (open source)
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.


Further reading[edit]

In statistics[edit]

  • 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. 

External links[edit]