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README.md

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CVXcanon

Introduction

Convex optimization modeling tools like CVX, CVXPY, and Convex.Jl translate high-level problem descriptions into low-level, canonical forms that are then passed to an backend solver. CVXcanon is a software package that factors out the common operations that all such modeling systems perform into a single library with a simple C++ interface. CVXcanon removes the need to reimplement this canonicalization process in new languages and provides significant performance gains over high level language implemententations.

Usage with CVXPY

If you're using CVXPY update to it 0.3.0 or higher.

One can expect a 3 - 10x speed-up over the original CVXPY implementation on most other problems.

Installation

CVXcanon supports both Python 2 and Python 3.

  1. Install numpy with pip from the command-line.
pip install numpy
  1. Install CVXcanon with pip from the command-line.
pip install CVXcanon

Note: If you're installing CVXcanon on Windows, a nonstandard system, or wish to build CVXcanon directly from source, you need to install swig. We are currently working to remove this dependency.

On Linux,

sudo apt-get install swig

On Mac OSX, using homebrew,

brew install swig

Integration with other CVX.* solvers

To use CVXcanon with the CVX solver of your choice one must take the following steps:

  1. Represent the problem's objective and constraints each as linear atom trees at some point during the solve process. To convert the linOp trees to a matrix representation, first call the wrapper to convert the high level language linOp tree into a C++ LinOp tree. This involves tree traversal, and some special cases depending on the representation of dense and sparse matrices. You may refer to the build_lin_op_tree function in canonInterface.py to see an example of how this is done.

  2. Pass your vector of C++ LinOps into CVXcanon's build matrix function. This will return a ProblemData structure, containing a sparse matrix representation of the problem data. Currently, final problem data is stored in COO representation using std::vector. You should convert this to a data format accessable to the target language. For Python, this unpacking can be done efficiently using CVXcanon's get{V/I/J} functions, which converts the representation to NUMPY arrays. For future languages, some work may be needed to do this efficiently.

  3. With these two steps implemented, you have essentially recreated canonInterface.py in the language of your choice. You now should be able to execute code of the form

V, I, J, b = canonInterface.get_problem_matrix(lin_expr_tree, var_offset_map)

where V, I, J is a COO representation of the problem matrix A. Matrix V, I, J and vector b can then be passed to your solver as needed.

Code Organization

  • /src/ contains the source code for CVXcanon

    • CVXcanon.(c/h)pp implements the matrix building algorithm. This file also provides the main access point into CVXcanon's functionality, the build_matrix function.
    • LinOp.hpp defines the LinOp class, linear atoms which we traverse during construction of the matrix.
    • LinOpOperations.(c/h)pp defines functions to get coefficients corresponding to each of the LinOps. This includes 18 special cases, one for each LinOp.
    • ProblemData.hpp defines the structure returned by build_matrix, which includes a sparse representation of the problem matrix and the dense constant vector.
  • /src/python contains code specific to our integration of CVXcanon with CVXPY.

    • canonInterface.py implements code which calls our SWIG binding of CVXcanon, including the function get_problem_matrix. It also defines a function to create a C++ LinOp tree from a Python LinOp tree, handling a variety of special cases related to data representation.
    • CVXcanon.py the Python binding autmatically generated by SWIG.

    • CVXcanon.i exposes functions and data types to SWIG, which automatically generate bindings for CVXcanon in a variety of common programming languages.

  • /tests/ contains code to test the accuracy and performance of CVXcanon. test_linops.py tests a variety of problems to ensure that our basic LinOp construction and representation is correct. huge_testman.py benchmarks CVXcanon on a variety of EE364A problems.

Contact

If you have comments or concerns, please do not hesitate to contact one of us at {piq93,jackzhu,millerjp}@stanford.edu.