Quadratic Programming

quadprog(c, Q, A, sense, b, l, u, solver)

Solves the quadratic programming problem:

\[\begin{split}\min_{x}\, &\frac{1}{2}x^TQx + c^Tx\\ s.t. &a_i^Tx \text{ sense}_i \, b_i \forall\,\, i\\ &l \leq x \leq u\\\end{split}\]


  • c is the objective vector, always in the sense of minimization
  • Q is the Hessian matrix of the objective
  • A is the constraint matrix, with rows \(a_i\) (viewed as column-oriented vectors)
  • sense is a vector of constraint sense characters '<', '=', and '>'
  • b is the right-hand side vector
  • l is the vector of lower bounds on the variables
  • u is the vector of upper bounds on the variables, and
  • solver specifies the desired solver, see choosing solvers.

A scalar is accepted for the b, sense, l, and u arguments, in which case its value is replicated. The values -Inf and Inf are interpreted to mean that there is no corresponding lower or upper bound.


Quadratic programming solvers extensively exploit the sparsity of the Hessian matrix Q and the constraint matrix A. While both dense and sparse matrices are accepted, for large-scale problems sparse matrices should be provided if permitted by the problem structure.

The quadprog function returns an instance of the type:

type QuadprogSolution

where status is a termination status symbol, one of :Optimal, :Infeasible, :Unbounded, :UserLimit (iteration limit or timeout), :Error (and maybe others).

If status is :Optimal, the other members have the following values:

  • objval – optimal objective value
  • sol – primal solution vector
  • attrs – a dictionary that may contain other relevant attributes (not currently used).

Analogous shortened and range-constraint versions are available as well.

We can solve the three-dimensional QP (see test/quadprog.jl):

\[\begin{split}\min_{x,y,z}\, &x^2+y^2+z^2+xy+yz\\ s.t. &x + 2y + 3z \geq 4\\ &x + y \geq 1\end{split}\]


using MathProgBase, Ipopt

sol = quadprog([0., 0., 0.],[2. 1. 0.; 1. 2. 1.; 0. 1. 2.],[1. 2. 3.; 1. 1. 0.],'>',[4., 1.],-Inf,Inf, IpoptSolver())
if sol.status == :Optimal
    println("Optimal objective value is $(sol.objval)")
    println("Optimal solution vector is: [$(sol.sol[1]), $(sol.sol[2]), $(sol.sol[3])]")
    println("Error: solution status $(sol.status)")