Skip to content
/ OOQP Public

Source repository for OOQP, a quadratic programming solver (and more)

Notifications You must be signed in to change notification settings

emgertz/OOQP

Repository files navigation

NAME
    OOQP - A package for solving convex quadratic programming problems.

SYNOPSIS
    This directory contains OOQP, a package for solving convex quadratic
    programming problems (QP). These are optimization problems in which the
    objective function is a convex quadratic function and the constraints
    are linear functions of a vector of real variables. They have the
    following general form:

        minimize (1/2) * x' * Q * x + c' * x
        subject to                     A * x  = b
                                       C * x >= d

    where Q is a symmetric positive semidefinite n by n matrix, x is a
    n-vector of variables, A and C are matrices of dimensions m_a by n and
    m_c by n, respectively, and c, b, and d are vectors of appropriate
    dimensions.

    Many QPs that arise in practice are highly structured. That is, the
    matrices that define them have properties that can be exploited in
    designing efficient solution techniques. In addition to the wide
    variations in problem structure, there is wide divergence in the ways in
    which the problem data and variables for a QP can be stored on a
    computer. This distribution of OOQP contains code for the implementation
    of solvers for a number of QP formulations, including a formulation for
    general QPs in which the Hessian and constraint matrices are sparse. The
    code in the distribution also provides a framework and examples for
    users who wish to implement solvers that are tailored to their own
    specific structured QPs and their specific computational environments.

OBTAINING OOQP
    Visit <http://www.cs.wisc.edu/~swright/ooqp> to obtain the latest
    version of OOQP.

GETTING STARTED
    The file INSTALL, included in this directory, is the installation guide
    for the OOQP package.

    Further documentation is available in the doc/ directory. For an index
    of the available documentation, point your favorite html viewer (e.g.
    Firefox) at the file doc/index.html. On many systems, this can be done
    by entering a string of the following form into the location window on
    the browser:

        file:/full/path/OOQP/doc/index.html

    The OOQP Users Guide, which contains a more complete description of OOQP
    and its capabilities, can be found at doc/ooqp-userguide.pdf. This file
    is in PDF format, which may be read by such programs as Adobe Acrobat
    Reader. If your browser contains a PDF plug-in, you may simply click on
    the link "Users Guide" from the doc/index.html file to display the
    Guide.

    Those wishing to use the OOQP framework to develop customized QP solvers
    should first read the Users Guide, and then browse through the reference
    manual, which is presented as a collection of web pages in the directory
    doc/reference_manual, also available as a link from doc/index.html

PROBLEM FORMULATIONS SUPPORTED IN THE OOQP DISTRIBUTION
    The OOQP distribution contains a solver for general sparse QPs, together
    a number of modules for solving certain structured QPs. Documentation
    for these modules can be found in the doc/formulations directory. The
    modules are as follows.

    QpGen
        This module solves general convex quadratic programs with sparse
        data. This problem is discussed at length in the Users Guide.

    QpBound
        This module solves bound-constrained quadratic programs, in which
        the only constraints are bounds on the variables. The formulation is
        as follows.

            minimize   (1/2) * x' * Q * x + c' * x
            subject to                      l <= x <= u

        See QpBound for more information.

    Svm This module creates a support vector machine classifier for solving
        the pattern classification problem in machine learning. See the Svm
        for more information.

    Huber
        Solves the Huber regression problem. See Huber.

COMMAND LINE EXECUTABLES
    The installation process will generate a number of command-line
    executables. A typical executable will be named

         formulation-linag-solver.exe

    Where the strings "formulation", "linalg" and "solver" will be replaced
    by appropriate values. The "formulation" string will be one of the
    problem formulations given in the preceding section, expressed in
    lowercase characters: qpgen, qpbound, svm, or huber. The "solver" string
    indicates the optimization algorithm used to solve the QP. It will have
    one of the following values:

    mehrotra
        Mehrotra's predictor-corrector algorithm

    gondzio
        Mehrotra's algorithm with Gondzio's higher-order corrections

    The "linalg" string is meant to represent how the linear algebra
    operations are performed. Typical values are "sparse", "dense" and
    "petsc". In some cases, the string will more specific, also referring to
    the linear solver used to solve linear equations, for instance
    "sparse-ma57." The linalg string does not appear in the names of the
    executables "svm-gondzio.exe" and "huber-gondzio.exe", for which we felt
    it would be redundant. (In these formulations, dense matrices are used
    to represent the larger, structured matrices that appear in the QP
    formulation.)

    Not all combinations of formulations, algorithms, and linear algebra
    packages are supported or provided. See the INSTALL file for information
    on how to build all supported executables. A users guide for executables
    based on the QpGen formulation may be found in doc/ooqp-userguide.pdf.
    For other executables, see the man page of their respective formulation
    in doc/formulations.

USE WITH MATLAB
    Some QP solvers supplied with OOQP may be invoked within the Matlab
    environment. Installation instructions, in addition to the general
    instructions found in INSTALL, have been supplied in the README_Matlab
    file. Once the Matlab interface has been properly installed,
    documentation is available from within Matlab, as described in the
    README_Matlab file.

USE WITH AMPL
    A solver for the QpGen formulation may be invoked from within Ampl. Use
    of this solver is documented in the OOQP Users Guide.

EXAMPLES
    The examples/ directory contains sample data and example programs
    demonstrating the use of OOQP. See the examples/README file for
    information on the contents of the examples directory.

    The src/QpExample directory contains an implementation of the
    formulation discussed extensively in the OOQP Users Guide. These files
    can be modified in the manner described in the Users Guide to create a
    specialized QP solver.

CONTENTS OF THE DISTRIBUTION
    The OOQP distribution contains the following top-level subdirectories:

    config/
        Helper files for configuring OOQP. This is part of the GNU Autoconf
        system and is of no interest to most users.

    doc/
        Documentation for the distribution. Point a browser at
        doc/index.html to see the available documentation.

    doc-src/
        Files use to create the documentation for the distribution. These
        are of no interest to most users.

    examples/
        Example problems and programs. See examples/README.

    include/
        Header files for OOQP. The header files are copied from src/ into
        this directory as part of the installation process.

    lib/
        Compiled libraries for OOQP (once you compile them).

    src/
        Source files for the OOQP distribution.

CREDITS
    OOQP is maintained by:

    E. Michael Gertz
        emgertz@mac.com

    Stephen Wright
        Computer Sciences Department, University of Wisconsin, Madison.
        <http://www.cs.wisc.edu/~swright/>

About

Source repository for OOQP, a quadratic programming solver (and more)

Resources

Stars

Watchers

Forks

Packages

No packages published