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Modern Fortran Edition of the quadprog solver

Status

Description

This is an updated version of the quadprog solver initially written by Berwin A. Turlach in Fortran 77. It can be used to solve strictly convex quadratic programs of the form

$$ \begin{aligned} \mathrm{minimize} & \quad \dfrac12 \mathbf{x}^T \mathbf{Px} - \mathbf{x}^T \mathbf{q} \\ \mathrm{subject~to} & \quad \mathbf{Ax} = \mathbf{b} \\ & \quad \mathbf{Cx} \geq \mathbf{d}. \end{aligned} $$

using an active set method. It is most efficient for small to moderate sized QP described using dense matrices.

Updates to the original code include:

• Sources have been translated from FORTRAN 77 fixed-form to Fortran 90 free-form.
• All obsolescent features (goto, etc) have been removed. It is now 100% standard compliant (Fortran 2018).
• It makes use of derived-type and easy to use interfaces. The qp_problem class is used to defined the quadratic program and solve to compute its solution.
• Calls to blas functions and subroutines now replace some hand-crafted implementations for improved performances.
• Calls to lapack subroutines now replace the original functionalities provided by linpack.

Building QuadProg

Fortran Package Manager

The library can be built with the Fortran Package Manager fpm using the provided fpm.toml file like so:

fpm build --release

Only double precision (real64) is currently supported.

To use QuadProg within your fpm project, add the following to your fpm.toml file:

[dependencies]
QuadProg = { git="https://github.com/loiseaujc/QuadProg.git"}

Dependencies

The library requires some blas and lapack routines which are not included. You thus need to have it available on your system.

Example

The following program solves the QP

$$ \begin{aligned} \mathrm{minimize} & \quad \dfrac12 \left( x_1^2 + x_2^2 + x_3^2 \right) - 5 x_2 \\ \mathrm{subject~to} & \quad -4x_1 + 2x_2 \geq -8 \\ & \quad -3x_1 + x_2 - 2x_3 \geq -2 \\ & \quad x_3 \geq 0. \end{aligned} $$

program example
    use quadprog
    implicit none
    integer, parameter :: dp = selected_real_kind(15, 307)
    ! Size of the problem.
    integer, parameter :: n = 3
    ! Quadratic cost.
    real(dp) :: P(n, n), q(n)
    ! Inequality constraints.
    real(dp) :: C(n, n), d(n)
    ! Convenience types.
    type(qp_problem) :: prob
    type(OptimizeResult) :: solution
    ! Miscellaneous
    integer :: i

    !> Setup the quadratic function..
    P = 0.0_dp ; q = [0.0_dp, 5.0_dp, 0.0_dp]
    do i = 1, n
        P(i, i) = 1.0_dp
    enddo
    
    !> Setup the inequality constraints.
    C(:, 1) = [-4.0_dp, 2.0_dp, 0.0_dp]
    C(:, 2) = [-3.0_dp, 1.0_dp, -2.0_dp]
    C(:, 3) = [0.0_dp, 0.0_dp, 1.0_dp]
    d = [-8.0_dp, 2.0_dp, 0.0_dp]

    !> Solve the inequality constrained QP.
    prob = qp_problem(P, q, C=C, d=d)
    solution = solve(prob)

    if (solution%success) then
        print *, "x   =", solution%x ! Solution of the QP.
        print *, "y   =", solution%y ! Lagrange multipliers.
        print *, "obj =", solution%obj ! Objective function.
    endif
end program

Licence

• The original source code was released under the GNU General Public Licence version 2 (GPL-2).

Development

• Development continues on Github.

Similar projects

• quadprog, an equivalent project written in JavaScript.
• quadprog, an equivalent project written in Rust.
• quadprog, some R bindings to the original quadprog solver.
• GoldfarbIdnaniSolver.jl, a port of the original quadprog code in Julia.

References

• D. Goldfarb and A. Idnani (1983). A numerically stable dual method for solving strictly convex quadratic programs. Mathematical Programming, 27, 1-33.