├── .bumpversion.cfg ├── .github └── workflows │ └── build.yml ├── .gitmodules ├── LICENSE ├── MANIFEST.in ├── Makefile ├── Pipfile ├── Pipfile.lock ├── README ├── README.md ├── pyproject.toml ├── requirements.txt ├── setup.py └── src ├── ecos ├── __init__.py ├── ecos.py └── version.py ├── ecosmodule.c ├── test_interface.py └── test_interface_bb.py /.bumpversion.cfg: -------------------------------------------------------------------------------- 1 | [bumpversion] 2 | current_version = 2.0.14 3 | files = setup.py src/ecos/version.py 4 | commit = True 5 | tag = True 6 | -------------------------------------------------------------------------------- /.github/workflows/build.yml: -------------------------------------------------------------------------------- 1 | name: build 2 | 3 | on: 4 | pull_request: 5 | push: 6 | branches: 7 | - master 8 | tags: 9 | - '*' 10 | 11 | # https://docs.github.com/en/actions/using-jobs/using-concurrency#example-using-a-fallback-value 12 | # Only cancels-in-progress on PRs (head_ref only defined in PR, fallback run_id always unique) 13 | concurrency: 14 | group: ${{ github.head_ref || github.run_id }} 15 | cancel-in-progress: true 16 | 17 | jobs: 18 | build: 19 | runs-on: ${{ matrix.os }} 20 | defaults: 21 | run: 22 | shell: bash -l {0} 23 | strategy: 24 | fail-fast: false 25 | matrix: 26 | os: [ ubuntu-20.04, macos-12, windows-2019 ] 27 | python-version: [ 3.8, 3.9, "3.10", "3.11", "3.12" ] 28 | 29 | env: 30 | PYTHON_VERSION: ${{ matrix.python-version }} 31 | 32 | steps: 33 | - uses: actions/checkout@v4 34 | with: 35 | submodules: recursive 36 | - uses: conda-incubator/setup-miniconda@v3 37 | with: 38 | auto-update-conda: true 39 | python-version: ${{ matrix.python-version }} 40 | channels: conda-forge,anaconda 41 | - name: Install 42 | run: | 43 | if [[ "$PYTHON_VERSION" == "3.7" ]] || [[ "$PYTHON_VERSION" == "3.8" ]]; then 44 | conda install scipy=1.3 numpy=1.16 pytest 45 | elif [[ "$PYTHON_VERSION" == "3.9" ]]; then 46 | conda install scipy=1.5 numpy=1.19 pytest 47 | elif [[ "$PYTHON_VERSION" == "3.10" ]]; then 48 | conda install scipy=1.7 numpy=1.21 pytest 49 | elif [[ "$PYTHON_VERSION" == "3.11" ]]; then 50 | conda install scipy=1.9.3 numpy=1.23.4 pytest 51 | elif [[ "$PYTHON_VERSION" == "3.12" ]]; then 52 | conda install scipy=1.11.3 numpy=1.26.0 pytest 53 | fi 54 | if [[ "$RUNNER_OS" == "macOS" ]]; then 55 | sudo rm -rf /Library/Developer/CommandLineTools 56 | fi 57 | 58 | - name: Test 59 | run: | 60 | make install 61 | python -m pytest 62 | rm -rf build/ 63 | 64 | build_wheels: 65 | needs: build 66 | 67 | runs-on: ${{ matrix.os }} 68 | strategy: 69 | fail-fast: false 70 | matrix: 71 | os: [ ubuntu-20.04, macos-12, windows-2019 ] 72 | python-version: [ 3.9, "3.10", "3.11", "3.12" ] 73 | include: 74 | - os: ubuntu-20.04 75 | python-version: 3.8 76 | single_action_config: "True" 77 | - os: macos-12 78 | python-version: 3.8 79 | - os: windows-2019 80 | python-version: 3.8 81 | 82 | env: 83 | RUNNER_OS: ${{ matrix.os }} 84 | PYTHON_VERSION: ${{ matrix.python-version }} 85 | SINGLE_ACTION_CONFIG: "${{ matrix.single_action_config == 'True' }}" 86 | PYPI_SERVER: ${{ secrets.PYPI_SERVER }} 87 | PYPI_USER: ${{ secrets.PYPI_USER }} 88 | PYPI_PASSWORD: ${{ secrets.PYPI_PASSWORD }} 89 | 90 | steps: 91 | 92 | - uses: actions/checkout@v4 93 | with: 94 | submodules: recursive 95 | - uses: actions/setup-python@v5 96 | with: 97 | python-version: ${{ matrix.python-version }} 98 | - name: Set Additional Envs 99 | shell: bash 100 | run: | 101 | echo "PYTHON_SUBVERSION=$(echo $PYTHON_VERSION | cut -c 3-)" >> $GITHUB_ENV 102 | echo "DEPLOY_PYPI_SOURCE=$( [[ $PYTHON_VERSION == 3.8 && $RUNNER_OS == 'macOS' ]] && echo 'True' || echo 'False' )" >> $GITHUB_ENV 103 | echo "DEPLOY=$( [[ $GITHUB_EVENT_NAME == 'push' && $GITHUB_REF == 'refs/tags'* ]] && echo 'True' || echo 'False' )" >> $GITHUB_ENV 104 | 105 | - name: Build wheels 106 | if: ${{env.DEPLOY == 'True'}} 107 | env: 108 | CIBW_BUILD: "cp3${{env.PYTHON_SUBVERSION}}-*" 109 | CIBW_SKIP: "*-win32 *-manylinux_i686 *-musllinux*" 110 | uses: pypa/cibuildwheel@v2.19.1 111 | 112 | - name: Build source 113 | if: ${{env.DEPLOY == 'True' && env.SINGLE_ACTION_CONFIG == 'True'}} 114 | run: | 115 | python setup.py sdist --dist-dir=wheelhouse 116 | 117 | - name: Release to pypi 118 | if: ${{env.DEPLOY == 'True'}} 119 | shell: bash 120 | run: | 121 | python -m pip install --upgrade twine 122 | twine check wheelhouse/* 123 | twine upload --skip-existing --repository-url $PYPI_SERVER wheelhouse/* -u $PYPI_USER -p $PYPI_PASSWORD 124 | 125 | - name: Upload artifacts to github 126 | if: ${{env.DEPLOY == 'True'}} 127 | uses: actions/upload-artifact@v4 128 | with: 129 | name: wheels-${{ matrix.os }}-${{ matrix.python-version }} 130 | path: ./wheelhouse -------------------------------------------------------------------------------- /.gitmodules: -------------------------------------------------------------------------------- 1 | [submodule "ecos"] 2 | path = ecos 3 | url = https://github.com/embotech/ecos.git 4 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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But first, please read 674 | . 675 | 676 | -------------------------------------------------------------------------------- /MANIFEST.in: -------------------------------------------------------------------------------- 1 | graft ecos/include 2 | graft ecos/external/amd/include 3 | graft ecos/external/ldl/include 4 | graft ecos/external/SuiteSparse_config 5 | include LICENSE 6 | include README.md 7 | -------------------------------------------------------------------------------- /Makefile: -------------------------------------------------------------------------------- 1 | .PHONY: all install version release clean 2 | 3 | help: 4 | @echo "Make commands for packaging, releasing, and publishing ecos-python" 5 | @echo "" 6 | @echo " version: generates a version string using git tags for ecos-python" 7 | @echo " install: installs local version of ecos-python" 8 | @echo " release: uploads the wheels in the `dist` folder" 9 | 10 | TAG := $(shell git describe --tags --always --dirty=.dirty | \ 11 | sed 's/v\(.*\)/\1/' | \ 12 | sed 's/\([0-9][0-9]*\.[0-9][0-9]*\.[0-9][0-9]*\)\(rc[0-9]*\)\{0,1\}-\([0-9][0-9]*\)-\(g.*\)/\1\2.dev\3+\4/') 13 | 14 | all: version 15 | 16 | version: 17 | @echo "__version__=\"$(TAG)\"" > src/ecos/version.py 18 | 19 | src/ecos/version.py: version 20 | 21 | install: version 22 | pip install . 23 | 24 | release: version 25 | -rm -rf dist 26 | mkdir -p dist 27 | python setup.py sdist 28 | curl -s https://api.github.com/repos/embotech/ecos-python/releases/tags/$(TAG) \ 29 | | grep browser_download_url.*whl \ 30 | | cut -d : -f 2,3 \ 31 | | tr -d \" \ 32 | | wget -P dist -qi - 33 | twine upload dist/* 34 | 35 | clean: 36 | @echo "nothing" 37 | 38 | -------------------------------------------------------------------------------- /Pipfile: -------------------------------------------------------------------------------- 1 | [[source]] 2 | url = "https://pypi.python.org/simple" 3 | verify_ssl = true 4 | name = "pypi" 5 | 6 | [dev-packages] 7 | pytest = "*" 8 | 9 | [packages] 10 | numpy = "*" 11 | 12 | [requires] 13 | python_version = "3.6" 14 | -------------------------------------------------------------------------------- /Pipfile.lock: -------------------------------------------------------------------------------- 1 | { 2 | "_meta": { 3 | "hash": { 4 | "sha256": "01e35b99ecbe27e2ed9f2ab1d12dbee14db6e5ebafba221a324d420eefbe6f1a" 5 | }, 6 | "pipfile-spec": 6, 7 | "requires": { 8 | "python_version": "3.6" 9 | }, 10 | "sources": [ 11 | { 12 | "name": "pypi", 13 | "url": "https://pypi.python.org/simple", 14 | "verify_ssl": true 15 | } 16 | ] 17 | }, 18 | "default": { 19 | "numpy": { 20 | "hashes": [ 21 | "sha256:02f98011ba4ab17f46f80f7f8f1c291ee7d855fcef0a5a98db80767a468c85cd", 22 | "sha256:0b7e807d6888da0db6e7e75838444d62495e2b588b99e90dd80c3459594e857b", 23 | "sha256:12c70ac274b32bc00c7f61b515126c9205323703abb99cd41836e8125ea0043e", 24 | "sha256:1666f634cb3c80ccbd77ec97bc17337718f56d6658acf5d3b906ca03e90ce87f", 25 | 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-------------------------------------------------------------------------------- /README: -------------------------------------------------------------------------------- 1 | See the github repository for a detailed README. 2 | 3 | http://github.com/embotech/ecos 4 | 5 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Python Wrapper for Embedded Conic Solver (ECOS) 2 | 3 | [![Build Status](http://github.com/embotech/ecos-python/workflows/build/badge.svg?event=push)](https://github.com/embotech/ecos-python/actions/workflows/build.yml) 4 | 5 | 6 | **Visit www.embotech.com/ECOS for detailed information on ECOS.** 7 | 8 | ECOS is a numerical software for solving convex second-order cone 9 | programs (SOCPs) of type 10 | ``` 11 | min c'*x 12 | s.t. A*x = b 13 | G*x <=_K h 14 | ``` 15 | where the last inequality is generalized, i.e. `h - G*x` belongs to the 16 | cone `K`. ECOS supports the positive orthant `R_+` and second-order 17 | cones `Q_n` defined as 18 | ``` 19 | Q_n = { (t,x) | t >= || x ||_2 } 20 | ``` 21 | In the definition above, t is a scalar and `x` is in `R_{n-1}`. The cone 22 | `K` is therefore a direct product of the positive orthant and 23 | second-order cones: 24 | ``` 25 | K = R_+ x Q_n1 x ... x Q_nN 26 | ``` 27 | 28 | ## Installation 29 | The latest version of ECOS is available via `pip`: 30 | 31 | pip install ecos 32 | 33 | This will download the relevant wheel for your machine. 34 | 35 | ### Building from source 36 | If you are attempting to build the Python extension from source, then 37 | use 38 | 39 | make install 40 | 41 | This will use the latest tag on git to version your local installation 42 | of ECOS. 43 | 44 | You will need [Numpy](http://www.numpy.org/) 45 | and [Scipy](http://www.scipy.org/). For installation instructions, see 46 | their respective pages. 47 | 48 | You may need `sudo` privileges for a global installation. 49 | 50 | ### Windows users 51 | Windows users may experience some extreme pain when installing ECOS from 52 | source for Python 2.7. We suggest switching to Linux or Mac OSX. 53 | 54 | If you must use (or insist on using) Windows, we suggest using 55 | the [Miniconda](http://repo.continuum.io/miniconda/) 56 | distribution to minimize this pain. 57 | 58 | If during the installation process, you see the error message 59 | `Unable to find vcvarsall.bat`, you will need to install 60 | [Microsoft Visual Studio Express 2008](go.microsoft.com/?linkid=7729279), 61 | since *Python 2.7* is built against the 2008 compiler. 62 | 63 | If using a newer version of Python, you can use a newer version of 64 | Visual Studio. For instance, Python 3.3 is built against [Visual Studio 65 | 2010](http://go.microsoft.com/?linkid=9709949). 66 | 67 | ## Calling ECOS from Python 68 | 69 | After installing the ECOS interface, you must import the module with 70 | ``` 71 | import ecos 72 | ``` 73 | This module provides a single function `ecos` with one of the following calling sequences: 74 | ``` 75 | solution = ecos.solve(c,G,h,dims) 76 | solution = ecos.solve(c,G,h,dims,A,b,**kwargs) 77 | ``` 78 | The arguments `c`, `h`, and `b` are Numpy arrays (i.e., matrices with a single 79 | column). The arguments `G` and `A` are Scipy *sparse* matrices in CSR format; 80 | if they are not of the proper format, ECOS will attempt to convert them. The 81 | argument `dims` is a dictionary with two fields, `dims['l']` and `dims['q']`. 82 | These are the same fields as in the Matlab case. If the fields are omitted or 83 | empty, they default to 0. 84 | The argument `kwargs` can include the keywords 85 | + `feastol`, `abstol`, `reltol`, `feastol_inacc`, `abstol_innac`, and `reltol_inacc` for tolerance values, 86 | + `max_iters` for the maximum number of iterations, 87 | + the Booleans `verbose` and `mi_verbose`, 88 | + `bool_vars_idx`, a list of `int`s which index the boolean variables, 89 | + `int_vars_idx`, a list of `int`s which index the integer variables, 90 | + `mi_max_iters` for maximum number of branch and bound iterations (mixed integer problems only), 91 | + `mi_abs_eps` for the absolute tolerance between upper and lower bounds (mixed integer problems only), and 92 | + `mi_rel_eps` for the relative tolerance, (U-L)/L, between upper and lower bounds (mixed integer problems only). 93 | 94 | The arguments `A`, `b`, and `kwargs` are optional. 95 | 96 | The returned object is a dictionary containing the fields `solution['x']`, `solution['y']`, `solution['s']`, `solution['z']`, and `solution['info']`. 97 | The first four are Numpy arrays containing the relevant solution. The last field contains a dictionary with the same fields as the `info` struct in the MATLAB interface. 98 | 99 | ## Using ECOS with CVXPY 100 | 101 | [CVXPY](http://cvxpy.org) is a powerful Python modeling framework for 102 | convex optimization, similar to the MATLAB counterpart CVX. ECOS is one 103 | of the default solvers in CVXPY, so there is nothing special you have to 104 | do in order to use ECOS with CVXPY, besides specifying it as a solver. 105 | Here is a small 106 | [example](http://www.cvxpy.org/en/latest/tutorial/advanced/index.html#solve-method-options) 107 | from the CVXPY tutorial: 108 | 109 | ```py 110 | import cvxpy as cp 111 | 112 | # Solving a problem with different solvers. 113 | x = cp.Variable(2) 114 | obj = cp.Minimize(cp.norm(x, 2) + cp.norm(x, 1)) 115 | constraints = [x >= 2] 116 | prob = cp.Problem(obj, constraints) 117 | 118 | # Solve with ECOS. 119 | prob.solve(solver=cp.ECOS) 120 | print("optimal value with ECOS:", prob.value) 121 | ``` 122 | 123 | ## ECOS Versioning 124 | The Python module contains two version numbers: 125 | 126 | 1. `ecos.__version__`: This is the version of the Python wrapper for 127 | ECOS 128 | 2. `ecos.__solver_version__`: This is the version of the underlying ECOS 129 | solver 130 | 131 | These two version numbers should typically agree, but they might not 132 | when a bug in the Python module has been fixed and nothing in the 133 | underlying C solver has changed. The major version numbers should agree, 134 | however. 135 | 136 | ### What happened to 2.0.7? 137 | Because version-syncing ECOS and ECOS-Python can be tricky, the 2.0.7 138 | version did not incorporate some minor changes to ECOS. In an 139 | ill-advised move, the release was deleted in hopes it could be 140 | re-uploaded, despite plenty warnings stating otherwise. 141 | 142 | Instead, a post release has been made that contains identical content to 143 | the 2.0.7 release. Generally, `pip` should pick up the post release for 144 | 2.0.7 and any dependencies such as `pip install "ecos>=2.0.5"` should still 145 | work as expected. 146 | 147 | ## Deployment 148 | When creating new versions of the Python wrapper, please use 149 | `bumpversion` to bump the version number and also remember to tag the 150 | commit so that CI is able to properly pick it up. See 151 | [Release](RELEASE.md) for more information. 152 | 153 | ## Python2 Support 154 | Starting with version 2.0.8, ecos-python will no longer support 155 | Python2.7. You may be able to download an [older 156 | version](https://github.com/embotech/ecos-python/releases/tag/2.0.7.post1) 157 | but moving forward we will no longer publish Python2 wheels for use. 158 | 159 | ## License 160 | 161 | ECOS is distributed under the [GNU General Public License 162 | v3.0](http://www.gnu.org/copyleft/gpl.html). Other licenses may be 163 | available upon request from [embotech](http://www.embotech.com). 164 | 165 | 166 | 167 | 168 | ## Credits 169 | 170 | The solver is essentially based on Lieven Vandenberghe's [CVXOPT](http://cvxopt.org) [ConeLP](http://www.ee.ucla.edu/~vandenbe/publications/coneprog.pdf) solver, although it differs in the particular way the linear systems are treated. 171 | 172 | The following people have been, and are, involved in the development and maintenance of ECOS: 173 | 174 | + Alexander Domahidi (principal developer) 175 | + Eric Chu (Python interface, unit tests) 176 | + Stephen Boyd (methods and maths) 177 | + Michael Grant (CVX interface) 178 | + Johan Löfberg (YALMIP interface) 179 | + João Felipe Santos, Iain Dunning (Julia interface) 180 | + Han Wang (ECOS branch and bound) 181 | 182 | The main technical idea behind ECOS is described in a short [paper](http://www.stanford.edu/~boyd/papers/ecos.html). More details are given in Alexander Domahidi's [PhD Thesis](http://e-collection.library.ethz.ch/view/eth:7611?q=domahidi) in Chapter 9. 183 | 184 | If you find ECOS useful, you can cite it using the following BibTex entry: 185 | 186 | ``` 187 | @INPROCEEDINGS{bib:Domahidi2013ecos, 188 | author={Domahidi, A. and Chu, E. and Boyd, S.}, 189 | booktitle={European Control Conference (ECC)}, 190 | title={{ECOS}: {A}n {SOCP} solver for embedded systems}, 191 | year={2013}, 192 | pages={3071-3076} 193 | } 194 | ``` 195 | -------------------------------------------------------------------------------- /pyproject.toml: -------------------------------------------------------------------------------- 1 | [build-system] 2 | requires = [ 3 | "numpy >= 2.0.0; python_version > '3.8'", 4 | "oldest-supported-numpy; python_version <= '3.8'", 5 | "wheel", 6 | "setuptools" 7 | ] 8 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | numpy 2 | -------------------------------------------------------------------------------- /setup.py: -------------------------------------------------------------------------------- 1 | from __future__ import print_function 2 | try: 3 | from setuptools import setup, Extension 4 | from setuptools.command.build_ext import build_ext as _build_ext 5 | except ImportError: 6 | print("Please use pip (https://pypi.python.org/pypi/pip) to install.") 7 | raise 8 | 9 | import os 10 | from glob import glob 11 | from platform import system 12 | 13 | lib = [] 14 | if system() == 'Linux': 15 | lib += ['rt'] 16 | 17 | _ecos = Extension('_ecos', libraries = lib, 18 | # define LDL and AMD to use long ints 19 | # also define that we are building a python module 20 | define_macros = [ 21 | ('PYTHON',None), 22 | ('DLONG', None), 23 | ('LDL_LONG', None), 24 | ('CTRLC', 1)], 25 | include_dirs = ['ecos/include', 26 | 'ecos/external/amd/include', 27 | 'ecos/external/ldl/include', 28 | 'ecos/external/SuiteSparse_config'], 29 | sources = ['src/ecosmodule.c', 30 | 'ecos/external/ldl/src/ldl.c', 31 | 'ecos/src/cone.c', 32 | 'ecos/src/ctrlc.c', 33 | 'ecos/src/ecos.c', 34 | 'ecos/src/equil.c', 35 | 'ecos/src/expcone.c', 36 | 'ecos/src/kkt.c', 37 | 'ecos/src/preproc.c', 38 | 'ecos/src/spla.c', 39 | 'ecos/src/splamm.c', 40 | 'ecos/src/timer.c', 41 | 'ecos/src/wright_omega.c' 42 | ] + glob('ecos/external/amd/src/*.c') 43 | + glob('ecos/ecos_bb/*.c')) # glob bb source files 44 | 45 | def set_builtin(name, value): 46 | if isinstance(__builtins__, dict): 47 | __builtins__[name] = value 48 | else: 49 | setattr(__builtins__, name, value) 50 | 51 | class build_ext(_build_ext): 52 | """ This custom class for building extensions exists so we can force 53 | a numpy install before building the extension, thereby giving us 54 | access to the numpy headers. 55 | """ 56 | def finalize_options(self): 57 | _build_ext.finalize_options(self) 58 | # Prevent numpy from thinking it is still in its setup process: 59 | set_builtin("__NUMPY_SETUP__", False) 60 | import numpy 61 | self.include_dirs.append(numpy.get_include()) 62 | 63 | setup( 64 | name = 'ecos', 65 | version = '2.0.14', 66 | author = 'Alexander Domahidi, Eric Chu, Han Wang, Santiago Akle', 67 | author_email = 'domahidi@embotech.com, echu@cs.stanford.edu, hanwang2@stanford.edu, tiagoakle@gmail.com', 68 | url = 'http://github.com/embotech/ecos', 69 | description = 'This is the Python package for ECOS: Embedded Cone Solver. See Github page for more information.', 70 | long_description=open('README.md').read(), 71 | long_description_content_type="text/markdown", 72 | license = "GPLv3", 73 | packages = ['ecos'], 74 | package_dir = {'': 'src'}, 75 | cmdclass = {'build_ext': build_ext}, 76 | ext_modules = [_ecos], 77 | setup_requires = [ 78 | "numpy >= 1.6" 79 | ], 80 | install_requires = [ 81 | "numpy >= 1.6", 82 | "scipy >= 0.9" 83 | ], 84 | tests_require=['pytest'] 85 | ) 86 | -------------------------------------------------------------------------------- /src/ecos/__init__.py: -------------------------------------------------------------------------------- 1 | from .ecos import solve, __solver_version__ 2 | from .version import __version__ 3 | -------------------------------------------------------------------------------- /src/ecos/ecos.py: -------------------------------------------------------------------------------- 1 | import _ecos 2 | from warnings import warn 3 | import numpy as np 4 | from scipy import sparse 5 | 6 | __solver_version__ = _ecos.version() 7 | 8 | def solve(c,G,h,dims,A=None,b=None, **kwargs): 9 | """ This Python routine "unpacks" scipy sparse matrices G and A into the 10 | data structures that we need for calling ECOS' csolve routine. 11 | 12 | If G and h are both None, then we will automatically create an "empty" 13 | CSC matrix to use with ECOS. 14 | 15 | It is *not* compatible with CVXOPT spmatrix and matrix, although 16 | it would not be very difficult to make it compatible. We put the 17 | onus on the user to convert CVXOPT matrix types into numpy, scipy 18 | array types. 19 | """ 20 | if G is not None and not sparse.issparse(G): 21 | raise TypeError("G is required to be a sparse matrix") 22 | if A is not None and not sparse.issparse(A): 23 | raise TypeError("A is required to be a sparse matrix") 24 | 25 | if G is not None and not sparse.isspmatrix_csc(G): 26 | warn("Converting G to a CSC matrix; may take a while.") 27 | G = G.tocsc() 28 | if A is not None and not sparse.isspmatrix_csc(A): 29 | warn("Converting A to a CSC matrix; may take a while.") 30 | A = A.tocsc() 31 | 32 | # set the dimensions 33 | # note that we forcibly coerce the shape values to Python ints 34 | # (C longs) in case of shenanigans with the underlying storage 35 | m,n1 = (0,len(c)) if G is None else map(int, G.get_shape()) 36 | p,n2 = (0,n1) if A is None else map(int, A.shape) 37 | 38 | if n1 != n2: 39 | raise TypeError("Columns of A and G don't match") 40 | 41 | 42 | # G.sort_indices() # ECHU: performance hit? do we need this? 43 | # if A is not None: A.sort_indices() 44 | 45 | if (G is None and h is not None) or (G is not None and h is None): 46 | raise TypeError("G and h must be supplied together") 47 | 48 | if (A is None and b is not None) or (A is not None and b is None): 49 | raise TypeError("A and b must be supplied together") 50 | 51 | if G is None: 52 | data = np.zeros((0,),dtype=np.double) 53 | indices = np.zeros((0,),dtype=np.int64) 54 | colptr = np.zeros((n1+1,),dtype=np.int64) 55 | h = np.zeros((0,)) 56 | else: 57 | data, indices, colptr = G.data, G.indices, G.indptr 58 | 59 | if A is None: 60 | return _ecos.csolve((m,n1,p), c, data, indices, colptr, h, dims, **kwargs) 61 | else: 62 | return _ecos.csolve((m,n1,p), c, data, indices, colptr, h, dims, A.data, A.indices, A.indptr, b, **kwargs) 63 | -------------------------------------------------------------------------------- /src/ecos/version.py: -------------------------------------------------------------------------------- 1 | __version__="2.0.14" -------------------------------------------------------------------------------- /src/ecosmodule.c: -------------------------------------------------------------------------------- 1 | /* Check that we are clean against numpy 1.7 */ 2 | #define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION 3 | 4 | #include 5 | #include "ecos.h" 6 | #include "ecos_bb.h" 7 | #include "numpy/arrayobject.h" 8 | /* 9 | * Define INLINE for MSVC compatibility. 10 | */ 11 | #ifdef _MSC_VER 12 | #define INLINE __inline 13 | #else 14 | #define INLINE inline 15 | #endif 16 | 17 | /* IMPORTANT: This code now uses numpy array types. It is a private C module 18 | * in the sense that end users only see the front-facing Python code in 19 | * "ecos.py"; hence, we can get away with the inputs being numpy arrays of 20 | * the CSR data structures. 21 | * 22 | * WARNING: This code also does not check that the data for the sparse 23 | * matrices are *actually* in column compressed storage for a sparse matrix. 24 | * The C module is not designed to be used stand-alone. If the data provided 25 | * does not correspond to a CSR matrix, this code will just crash inelegantly. 26 | * Please use the "solve" interface in ecos.py. 27 | */ 28 | /* #include "cvxopt.h" */ 29 | 30 | /* ECHU: Note, Python3.x may require special handling for the int and double 31 | * types. */ 32 | static INLINE int getIntType(void) { 33 | switch(sizeof(idxint)) { 34 | case 1: return NPY_INT8; 35 | case 2: return NPY_INT16; 36 | case 4: return NPY_INT32; 37 | case 8: return NPY_INT64; 38 | default: return NPY_INT32; /* defaults to 4 byte int */ 39 | } 40 | } 41 | 42 | static INLINE int getDoubleType(void) { 43 | /* ECHU: known bug, if pfloat isn't "double", will cause aliasing in memory */ 44 | return NPY_DOUBLE; 45 | } 46 | 47 | static INLINE PyArrayObject *getContiguous(PyArrayObject *array, int typenum) { 48 | /* gets the pointer to the block of contiguous C memory 49 | * the overhead should be small unless the numpy array has been 50 | * reordered in some way or the data type doesn't quite match 51 | * 52 | * the "tmp_arr" pointer has to have Py_DECREF called on it; new_owner 53 | * owns the "new" array object created by PyArray_Cast 54 | */ 55 | PyArrayObject *tmp_arr; 56 | PyArrayObject *new_owner; 57 | tmp_arr = PyArray_GETCONTIGUOUS(array); 58 | new_owner = (PyArrayObject *) PyArray_Cast(tmp_arr, typenum); 59 | Py_DECREF(tmp_arr); 60 | return new_owner; 61 | } 62 | 63 | /* The PyInt variable is a PyLong in Python3.x. 64 | */ 65 | #if PY_MAJOR_VERSION >= 3 66 | #define PyInt_AsLong PyLong_AsLong 67 | #define PyInt_Check PyLong_Check 68 | #endif 69 | 70 | static PyObject *version(PyObject* self) 71 | { 72 | return Py_BuildValue("s",ECOS_VERSION); 73 | } 74 | 75 | static int checkNonnegativeInt(const char *key, idxint val) { 76 | if (val >= 0) { 77 | return 0; 78 | } 79 | PyErr_Format(PyExc_ValueError, "'%s' must be a nonnegative integer", key); 80 | return -1; 81 | } 82 | 83 | static int checkPositiveFloat(const char *key, pfloat val) { 84 | if (val > 0) { 85 | return 0; 86 | } 87 | PyErr_Format(PyExc_ValueError, "'%s' must be a positive float", key); 88 | return -1; 89 | } 90 | 91 | static PyObject *csolve(PyObject* self, PyObject *args, PyObject *kwargs) 92 | { 93 | /* Expects a function call 94 | * sol = csolve((m,n,p),c,Gx,Gi,Gp,h,dims,Ax,Ai,Ap,b,**kwargs) 95 | * where 96 | * 97 | * the triple (m,n,p) corresponds to: 98 | * `m`: the rows of G 99 | * `n`: the cols of G and A, must agree with the length of c 100 | * `p`: the rows of A 101 | * `c` is a Numpy array of doubles 102 | * "G" is a sparse matrix in column compressed storage. "Gx" are the values, 103 | * "Gi" are the rows, and "Gp" are the column pointers. 104 | * `Gx` is a Numpy array of doubles 105 | * `Gi` is a Numpy array of ints 106 | * `Gp` is a Numpy array of ints 107 | * `h` is a Numpy array 108 | * `dims` is a dictionary with 109 | * `dims['l']` an integer specifying the dimension of positive orthant cone 110 | * `dims['q']` an *list* specifying dimensions of second-order cones 111 | * `dims['e']` an integer specifying the number of exponential cones 112 | * 113 | * "A" is an optional sparse matrix in column compressed storage. "Ax" are 114 | * the values, "Ai" are the rows, and "Ap" are the column pointers. 115 | * `Ax` is a Numpy array of doubles 116 | * `Ai` is a Numpy array of ints 117 | * `Ap` is a Numpy array of ints 118 | * `b` is an optional argument, which is a Numpy array of doubles 119 | * other optional arguments are: 120 | * `feastol`: the tolerance on the primal and dual residual 121 | * `abstol`: the absolute tolerance on the duality gap 122 | * `reltol`: the relative tolerance on the duality gap 123 | * `feastol_inacc`: the tolerance on the primal and dual residual if reduced precisions 124 | * `abstol_inacc`: the absolute tolerance on the duality gap if reduced precision 125 | * `reltolL_inacc`: the relative tolerance on the duality gap if reduced precision 126 | * `max_iters`: the maximum numer of iterations. 127 | * `nitref`: the number of iterative refinement steps. 128 | * `verbose`: signals to print on non zero value. 129 | * `mi_max_iters`: maximum number of branch and bound iterations 130 | * (mixed integer problems only), 131 | * `mi_abs_eps`: the absolute tolerance between upper and lower 132 | * bounds (mixed integer problems only), 133 | * `mi_rel_eps`: the relative tolerance, (U-L)/L, between upper 134 | * and lower bounds (mixed integer problems only). 135 | * `mi_verbose`: whether to be verbose when solving mixed integer 136 | * problems 137 | * 138 | * This call will solve the problem 139 | * 140 | * minimize c'*x 141 | * subject to A*x = b 142 | * h - G*x \in K 143 | * 144 | * The code returns a Python dictionary with five keys, 'x', 'y', 'info', 's', 145 | * and 'z'. These correspond to the following: 146 | * 147 | * `x`: primal variables 148 | * `y`: dual variables for equality constraints 149 | * `s`: slacks for Gx + s <= h, s \in K 150 | * `z`: dual variables for inequality constraints s \in K 151 | * `info`: another dictionary with the following fields: 152 | * exitflag: 0=OPTIMAL, 1=PRIMAL INFEASIBLE, 2=DUAL INFEASIBLE, -1=MAXIT REACHED 153 | * infostring: gives information about the status of solution 154 | * pcost: value of primal objective 155 | * dcost: value of dual objective 156 | * pres: primal residual on inequalities and equalities 157 | * dres: dual residual 158 | * pinf: primal infeasibility measure 159 | * dinf: dual infeasibility measure 160 | * pinfres: NaN 161 | * dinfres: 3.9666e+15 162 | * gap: duality gap 163 | * relgap: relative duality gap 164 | * r0: ??? 165 | * numerr: numerical error? 166 | * iter: number of iterations 167 | * timing: dictionary with timing information 168 | */ 169 | 170 | /* data structures for arguments */ 171 | /* ECHU: below is for CVXOPT 172 | * matrix *c, *h, *b = NULL; 173 | * spmatrix *G, *A = NULL; 174 | */ 175 | 176 | /* BEGIN VARIABLE DECLARATIONS */ 177 | PyArrayObject *Gx, *Gi, *Gp, *c, *h; 178 | PyListObject *bool_idx = NULL; 179 | PyListObject *int_idx = NULL; 180 | PyArrayObject *Ax = NULL; 181 | PyArrayObject *Ai = NULL; 182 | PyArrayObject *Ap = NULL; 183 | PyArrayObject *b = NULL; 184 | PyObject *dims = NULL; 185 | PyObject *verbose = NULL; 186 | PyObject *mi_verbose = NULL; 187 | idxint n; /* number or variables */ 188 | idxint m; /* number of conic variables */ 189 | idxint p = 0; /* number of equality constraints */ 190 | idxint ncones = 0; /* number of cones */ 191 | idxint numConicVariables = 0; 192 | 193 | /* ECOS data structures */ 194 | idxint l = 0; 195 | idxint *q = NULL; 196 | idxint e = 0; 197 | 198 | pfloat *Gpr = NULL; 199 | idxint *Gjc = NULL; 200 | idxint *Gir = NULL; 201 | 202 | pfloat *Apr = NULL; 203 | idxint *Ajc = NULL; 204 | idxint *Air = NULL; 205 | 206 | pfloat *cpr = NULL; 207 | pfloat *hpr = NULL; 208 | pfloat *bpr = NULL; 209 | 210 | idxint *bool_vars_idx = NULL; 211 | idxint *int_vars_idx = NULL; 212 | idxint num_bool = 0; 213 | idxint num_int = 0; 214 | 215 | long mi_iterations = -1; 216 | 217 | /* Default ECOS settings */ 218 | settings opts_ecos = {0}; 219 | settings_bb opts_ecos_bb = {0}; 220 | 221 | pwork* mywork = NULL; 222 | ecos_bb_pwork* myecos_bb_work = NULL; 223 | 224 | idxint i; 225 | static char *kwlist[] = {"shape", "c", "Gx", "Gi", "Gp", "h", "dims", 226 | "Ax", "Ai", "Ap", "b", 227 | "verbose", "feastol", "abstol", "reltol", 228 | "feastol_inacc", "abstol_inacc", "reltol_inacc", 229 | "max_iters", "nitref", "bool_vars_idx", "int_vars_idx", 230 | "mi_verbose", "mi_max_iters", "mi_abs_eps", 231 | "mi_rel_eps", "mi_int_tol", NULL}; 232 | int intType, doubleType; 233 | 234 | /* parse the arguments and ensure they are the correct type */ 235 | #ifdef DLONG 236 | #ifdef _WIN64 237 | // use long long on win64 238 | static char *argparse_string = "(LLL)O!O!O!O!O!O!|O!O!O!O!O!ddddddLLO!O!O!Lddd"; 239 | #else 240 | static char *argparse_string = "(lll)O!O!O!O!O!O!|O!O!O!O!O!ddddddllO!O!O!lddd"; 241 | #endif 242 | #else 243 | static char *argparse_string = "(iii)O!O!O!O!O!O!|O!O!O!O!O!ddddddiiO!O!O!iddd"; 244 | #endif 245 | PyArrayObject *Gx_arr, *Gi_arr, *Gp_arr; 246 | PyArrayObject *c_arr; 247 | PyArrayObject *h_arr; 248 | PyObject *linearObj; 249 | PyObject *socObj; 250 | PyObject *expObj; 251 | PyArrayObject *Ax_arr = NULL; 252 | PyArrayObject *Ai_arr = NULL; 253 | PyArrayObject *Ap_arr = NULL; 254 | PyArrayObject *b_arr = NULL; 255 | 256 | idxint exitcode, numerr = 0; 257 | npy_intp veclen[1]; 258 | PyObject *x, *y, *z, *s; 259 | const char* infostring; 260 | PyObject *infoDict = NULL; 261 | PyObject *tinfos = NULL; 262 | PyObject *returnDict = NULL; 263 | /* END VARIABLE DECLARATIONS */ 264 | 265 | /* Default ECOS settings */ 266 | opts_ecos.feastol = FEASTOL; 267 | opts_ecos.reltol = RELTOL; 268 | opts_ecos.abstol = ABSTOL; 269 | opts_ecos.feastol_inacc = FTOL_INACC; 270 | opts_ecos.abstol_inacc = ATOL_INACC; 271 | opts_ecos.reltol_inacc = RTOL_INACC; 272 | opts_ecos.maxit = MAXIT; 273 | opts_ecos.nitref = NITREF; 274 | opts_ecos.verbose = VERBOSE; 275 | 276 | opts_ecos_bb.verbose = 1; 277 | opts_ecos_bb.maxit = MI_MAXITER; 278 | opts_ecos_bb.abs_tol_gap = MI_ABS_EPS; 279 | opts_ecos_bb.rel_tol_gap = MI_REL_EPS; 280 | opts_ecos_bb.integer_tol = MI_INT_TOL; 281 | 282 | if( !PyArg_ParseTupleAndKeywords(args, kwargs, argparse_string, kwlist, 283 | &m, &n, &p, 284 | &PyArray_Type, &c, 285 | &PyArray_Type, &Gx, 286 | &PyArray_Type, &Gi, 287 | &PyArray_Type, &Gp, 288 | &PyArray_Type, &h, 289 | &PyDict_Type, &dims, 290 | &PyArray_Type, &Ax, 291 | &PyArray_Type, &Ai, 292 | &PyArray_Type, &Ap, 293 | &PyArray_Type, &b, 294 | &PyBool_Type, &verbose, 295 | &opts_ecos.feastol, 296 | &opts_ecos.abstol, 297 | &opts_ecos.reltol, 298 | &opts_ecos.feastol_inacc, 299 | &opts_ecos.abstol_inacc, 300 | &opts_ecos.reltol_inacc, 301 | &opts_ecos.maxit, 302 | &opts_ecos.nitref, 303 | &PyList_Type, &bool_idx, 304 | &PyList_Type, &int_idx, 305 | &PyBool_Type, &mi_verbose, 306 | &opts_ecos_bb.maxit, 307 | &opts_ecos_bb.abs_tol_gap, 308 | &opts_ecos_bb.rel_tol_gap, 309 | &opts_ecos_bb.integer_tol 310 | ) 311 | ) { return NULL; } 312 | 313 | if (checkNonnegativeInt("m", m) < 0) return NULL; 314 | if (checkNonnegativeInt("n", n) < 0) return NULL; 315 | if (checkNonnegativeInt("p", p) < 0) return NULL; 316 | 317 | if (bool_idx){ 318 | if (!PyList_Check(bool_idx)){ 319 | PyErr_SetString(PyExc_TypeError, "bool_vars_idx must be a list"); 320 | return NULL; 321 | } 322 | 323 | /* Ensure the list of indices are monotonic */ 324 | PyList_Sort((PyObject *) bool_idx); 325 | 326 | num_bool = (idxint)PyList_Size((PyObject *) bool_idx); 327 | for (i = 0; i= n || 334 | PyLong_AsLong(PyList_GetItem((PyObject *) bool_idx, (Py_ssize_t)i)) < 0){ 335 | PyErr_SetString(PyExc_ValueError, "bool_vars_idx must be in range [0,n-1] "); 336 | return NULL; 337 | } 338 | } 339 | } 340 | 341 | if (int_idx){ 342 | if (!PyList_Check(int_idx)){ 343 | PyErr_SetString(PyExc_TypeError, "int_vars_idx must be a list"); 344 | return NULL; 345 | } 346 | 347 | /* Ensure the list of indices are monotonic */ 348 | PyList_Sort((PyObject *) int_idx); 349 | 350 | num_int = (idxint)PyList_Size((PyObject *) int_idx); 351 | for (i = 0; i= n || 359 | PyLong_AsLong(PyList_GetItem((PyObject *) int_idx, (Py_ssize_t)i)) < 0){ 360 | PyErr_SetString(PyExc_ValueError, "int_vars_idx entries must be in range [0,n-1] "); 361 | return NULL; 362 | } 363 | } 364 | } 365 | 366 | 367 | /* check the opts*/ 368 | if (verbose) 369 | opts_ecos.verbose = (idxint) PyObject_IsTrue(verbose); 370 | if (checkNonnegativeInt("maxit", opts_ecos.maxit) < 0) return NULL; 371 | if (checkNonnegativeInt("nitref", opts_ecos.nitref) < 0) return NULL; 372 | if (checkPositiveFloat("abstol", opts_ecos.abstol) < 0) return NULL; 373 | if (checkPositiveFloat("feastol", opts_ecos.feastol) < 0) return NULL; 374 | if (checkPositiveFloat("reltol", opts_ecos.reltol) < 0) return NULL; 375 | if (checkPositiveFloat("abstol_inacc", opts_ecos.abstol_inacc) < 0) return NULL; 376 | if (checkPositiveFloat("feastol_inacc", opts_ecos.feastol_inacc) < 0) return NULL; 377 | if (checkPositiveFloat("reltol_inacc", opts_ecos.reltol_inacc) < 0) return NULL; 378 | 379 | if (mi_verbose) 380 | opts_ecos_bb.verbose = (idxint) PyObject_IsTrue(mi_verbose); 381 | if (checkNonnegativeInt("mi_max_iters", opts_ecos_bb.maxit) < 0) return NULL; 382 | if (checkPositiveFloat("mi_abs_eps", opts_ecos_bb.abs_tol_gap) < 0) return NULL; 383 | if (checkPositiveFloat("mi_rel_eps", opts_ecos_bb.rel_tol_gap) < 0) return NULL; 384 | if (checkPositiveFloat("mi_int_tol", opts_ecos_bb.integer_tol) < 0) return NULL; 385 | 386 | /* get the typenum for the primitive int and double types */ 387 | intType = getIntType(); 388 | doubleType = getDoubleType(); 389 | 390 | /* set G */ 391 | if( !PyArray_ISFLOAT(Gx) || PyArray_NDIM(Gx) != 1) { 392 | PyErr_SetString(PyExc_TypeError, "Gx must be a numpy array of floats"); 393 | return NULL; 394 | } 395 | if( !PyArray_ISINTEGER(Gi) || PyArray_NDIM(Gi) != 1) { 396 | PyErr_SetString(PyExc_TypeError, "Gi must be a numpy array of ints"); 397 | return NULL; 398 | } 399 | if( !PyArray_ISINTEGER(Gp) || PyArray_NDIM(Gp) != 1) { 400 | PyErr_SetString(PyExc_TypeError, "Gp must be a numpy array of ints"); 401 | return NULL; 402 | } 403 | Gx_arr = getContiguous(Gx, doubleType); 404 | Gi_arr = getContiguous(Gi, intType); 405 | Gp_arr = getContiguous(Gp, intType); 406 | Gpr = (pfloat *) PyArray_DATA(Gx_arr); 407 | Gir = (idxint *) PyArray_DATA(Gi_arr); 408 | Gjc = (idxint *) PyArray_DATA(Gp_arr); 409 | 410 | /* set c */ 411 | if (!PyArray_ISFLOAT(c) || PyArray_NDIM(c) != 1) { 412 | PyErr_SetString(PyExc_TypeError, "c must be a dense numpy float array with one dimension"); 413 | Py_DECREF(Gx_arr); Py_DECREF(Gi_arr); Py_DECREF(Gp_arr); 414 | return NULL; 415 | } 416 | 417 | if (PyArray_DIM(c,0) != n){ 418 | PyErr_SetString(PyExc_ValueError, "c has incompatible dimension with G"); 419 | Py_DECREF(Gx_arr); Py_DECREF(Gi_arr); Py_DECREF(Gp_arr); 420 | return NULL; 421 | } 422 | c_arr = getContiguous(c, doubleType); 423 | cpr = (pfloat *) PyArray_DATA(c_arr); 424 | 425 | /* set h */ 426 | if (!PyArray_ISFLOAT(h) || PyArray_NDIM(h) != 1) { 427 | PyErr_SetString(PyExc_TypeError, "h must be a dense numpy float array with one dimension"); 428 | Py_DECREF(Gx_arr); Py_DECREF(Gi_arr); Py_DECREF(Gp_arr); 429 | Py_DECREF(c_arr); 430 | return NULL; 431 | } 432 | 433 | 434 | if (PyArray_DIM(h,0) != m){ 435 | PyErr_SetString(PyExc_ValueError, "h has incompatible dimension with G"); 436 | Py_DECREF(Gx_arr); Py_DECREF(Gi_arr); Py_DECREF(Gp_arr); 437 | Py_DECREF(c_arr); 438 | return NULL; 439 | } 440 | h_arr = getContiguous(h, doubleType); 441 | hpr = (pfloat *) PyArray_DATA(h_arr); 442 | 443 | /* get dims['l'] */ 444 | linearObj = PyDict_GetItemString(dims, "l"); 445 | if(linearObj) { 446 | if ( (PyInt_Check(linearObj) && ((l = (idxint) PyInt_AsLong(linearObj)) >= 0)) || 447 | (PyLong_Check(linearObj) && ((l = PyLong_AsLong(linearObj)) >= 0)) ){ 448 | numConicVariables += l; 449 | } else { 450 | PyErr_SetString(PyExc_TypeError, "dims['l'] ought to be a nonnegative integer"); 451 | Py_DECREF(Gx_arr); Py_DECREF(Gi_arr); Py_DECREF(Gp_arr); 452 | Py_DECREF(c_arr); Py_DECREF(h_arr); 453 | return NULL; 454 | } 455 | } 456 | 457 | /* get dims['q'] */ 458 | socObj = PyDict_GetItemString(dims, "q"); 459 | if(socObj) { 460 | if (PyList_Check(socObj)) { 461 | ncones = (idxint)PyList_Size(socObj); 462 | q = calloc(ncones, sizeof(idxint)); 463 | for (i = 0; i < ncones; ++i) { 464 | PyObject *qi = PyList_GetItem(socObj, i); 465 | if( (PyInt_Check(qi) && ((q[i] = (idxint) PyInt_AsLong(qi)) > 0)) || 466 | (PyLong_Check(qi) && ((q[i] = PyLong_AsLong(qi)) > 0)) ) { 467 | numConicVariables += q[i]; 468 | } else { 469 | PyErr_SetString(PyExc_TypeError, "dims['q'] ought to be a list of positive integers"); 470 | if(q) free(q); 471 | Py_DECREF(Gx_arr); Py_DECREF(Gi_arr); Py_DECREF(Gp_arr); 472 | Py_DECREF(c_arr); Py_DECREF(h_arr); 473 | return NULL; 474 | } 475 | 476 | } 477 | } else { 478 | PyErr_SetString(PyExc_TypeError, "dims['q'] ought to be a list"); 479 | Py_DECREF(Gx_arr); Py_DECREF(Gi_arr); Py_DECREF(Gp_arr); 480 | Py_DECREF(c_arr); Py_DECREF(h_arr); 481 | return NULL; 482 | } 483 | } 484 | 485 | 486 | /* get dims['e'] */ 487 | expObj = PyDict_GetItemString(dims, "e"); 488 | if(expObj) { 489 | if ( (PyInt_Check(expObj) && ((e = (idxint) PyInt_AsLong(expObj)) >= 0)) || 490 | (PyLong_Check(expObj) && ((e = PyLong_AsLong(expObj)) >= 0)) ){ 491 | numConicVariables += 3*e; 492 | } else { 493 | PyErr_SetString(PyExc_TypeError, "dims['e'] ought to be a nonnegative integer"); 494 | Py_DECREF(Gx_arr); Py_DECREF(Gi_arr); Py_DECREF(Gp_arr); 495 | Py_DECREF(c_arr); Py_DECREF(h_arr); 496 | return NULL; 497 | } 498 | } 499 | 500 | 501 | if(Ax && Ai && Ap && b) { 502 | /* set A */ 503 | if( !PyArray_ISFLOAT(Ax) || PyArray_NDIM(Ax) != 1 ) { 504 | PyErr_SetString(PyExc_TypeError, "Ax must be a numpy array of floats"); 505 | if(q) free(q); 506 | Py_DECREF(Gx_arr); Py_DECREF(Gi_arr); Py_DECREF(Gp_arr); 507 | Py_DECREF(c_arr); Py_DECREF(h_arr); 508 | return NULL; 509 | } 510 | if( !PyArray_ISINTEGER(Ai) || PyArray_NDIM(Ai) != 1) { 511 | PyErr_SetString(PyExc_TypeError, "Ai must be a numpy array of ints"); 512 | if(q) free(q); 513 | Py_DECREF(Gx_arr); Py_DECREF(Gi_arr); Py_DECREF(Gp_arr); 514 | Py_DECREF(c_arr); Py_DECREF(h_arr); 515 | return NULL; 516 | } 517 | if( !PyArray_ISINTEGER(Ap) || PyArray_NDIM(Ap) != 1) { 518 | PyErr_SetString(PyExc_TypeError, "Ap must be a numpy array of ints"); 519 | if(q) free(q); 520 | Py_DECREF(Gx_arr); Py_DECREF(Gi_arr); Py_DECREF(Gp_arr); 521 | Py_DECREF(c_arr); Py_DECREF(h_arr); 522 | return NULL; 523 | } 524 | /* if ((SpMatrix_Check(A) && SP_ID(A) != DOUBLE)){ 525 | * PyErr_SetString(PyExc_TypeError, "A must be a sparse 'd' matrix"); 526 | * if(q) free(q); 527 | * Py_DECREF(Gx_arr); Py_DECREF(Gi_arr); Py_DECREF(Gp_arr); 528 | * Py_DECREF(c_arr); Py_DECREF(h_arr); 529 | * return NULL; 530 | * } 531 | * if ((p = SP_NROWS(A)) < 0) { 532 | * PyErr_SetString(PyExc_ValueError, "p must be a nonnegative integer"); 533 | * if(q) free(q); 534 | * Py_DECREF(Gx_arr); Py_DECREF(Gi_arr); Py_DECREF(Gp_arr); 535 | * Py_DECREF(c_arr); Py_DECREF(h_arr); 536 | * return NULL; 537 | * } 538 | * if (SP_NCOLS(A) != n) { 539 | * PyErr_SetString(PyExc_ValueError, "A has incompatible dimension with c"); 540 | * if(q) free(q); 541 | * Py_DECREF(Gx_arr); Py_DECREF(Gi_arr); Py_DECREF(Gp_arr); 542 | * Py_DECREF(c_arr); Py_DECREF(h_arr); 543 | * return NULL; 544 | * } 545 | * if (p != 0) { 546 | * Apr = SP_VALD(A); 547 | * Air = SP_ROW(A); 548 | * Ajc = SP_COL(A); 549 | * } 550 | */ 551 | Ax_arr = getContiguous(Ax, doubleType); 552 | Ai_arr = getContiguous(Ai, intType); 553 | Ap_arr = getContiguous(Ap, intType); 554 | Apr = (pfloat *) PyArray_DATA(Ax_arr); 555 | Air = (idxint *) PyArray_DATA(Ai_arr); 556 | Ajc = (idxint *) PyArray_DATA(Ap_arr); 557 | 558 | /* set b */ 559 | /* if (!Matrix_Check(b) || MAT_NCOLS(b) != 1 || MAT_ID(b) != DOUBLE) { 560 | * PyErr_SetString(PyExc_TypeError, "b must be a dense 'd' matrix with one column"); 561 | * if(q) free(q); 562 | * return NULL; 563 | * } 564 | * if (MAT_NROWS(b) != p){ 565 | * PyErr_SetString(PyExc_ValueError, "b has incompatible dimension with A"); 566 | * if(q) free(q); 567 | * return NULL; 568 | * } 569 | * if (p != 0) { 570 | * bpr = MAT_BUFD(b); 571 | * } 572 | */ 573 | if (!PyArray_ISFLOAT(b) || PyArray_NDIM(b) != 1) { 574 | PyErr_SetString(PyExc_TypeError, "b must be a dense numpy float array with one dimension"); 575 | if(q) free(q); 576 | Py_DECREF(Gx_arr); Py_DECREF(Gi_arr); Py_DECREF(Gp_arr); 577 | Py_DECREF(c_arr); Py_DECREF(h_arr); 578 | Py_DECREF(Ax_arr); Py_DECREF(Ai_arr); Py_DECREF(Ap_arr); 579 | return NULL; 580 | } 581 | if (PyArray_DIM(b,0) != p){ 582 | PyErr_SetString(PyExc_ValueError, "b has incompatible dimension with A"); 583 | if(q) free(q); 584 | Py_DECREF(Gx_arr); Py_DECREF(Gi_arr); Py_DECREF(Gp_arr); 585 | Py_DECREF(c_arr); Py_DECREF(h_arr); 586 | Py_DECREF(Ax_arr); Py_DECREF(Ai_arr); Py_DECREF(Ap_arr); 587 | return NULL; 588 | } 589 | b_arr = getContiguous(b, doubleType); 590 | bpr = (pfloat *) PyArray_DATA(b_arr); 591 | } else if (Ax || Ai || Ap || b) { 592 | /* check that A and b are both supplied */ 593 | PyErr_SetString(PyExc_ValueError, "A and b arguments must be supplied together"); 594 | if(q) free(q); 595 | Py_DECREF(Gx_arr); Py_DECREF(Gi_arr); Py_DECREF(Gp_arr); 596 | Py_DECREF(c_arr); Py_DECREF(h_arr); 597 | return NULL; 598 | } 599 | 600 | /* check that sum(q) + l = m */ 601 | if( numConicVariables != m ){ 602 | PyErr_SetString(PyExc_ValueError, "Number of rows of G does not match dims.l+sum(dims.q)+dims.e"); 603 | if (q) free(q); 604 | Py_DECREF(Gx_arr); Py_DECREF(Gi_arr); Py_DECREF(Gp_arr); 605 | Py_DECREF(c_arr); Py_DECREF(h_arr); 606 | if (b_arr) Py_DECREF(b_arr); 607 | if (Ax_arr) Py_DECREF(Ax_arr); 608 | if (Ai_arr) Py_DECREF(Ai_arr); 609 | if (Ap_arr) Py_DECREF(Ap_arr); 610 | return NULL; 611 | } 612 | 613 | if (num_bool > 0 || num_int > 0){ 614 | if (bool_idx){ 615 | bool_vars_idx = malloc( num_bool*sizeof(idxint) ); 616 | for (i=0; iecos_prob; 643 | 644 | /* Set settings for ECOS. */ 645 | mywork->stgs->verbose = opts_ecos.verbose; 646 | mywork->stgs->abstol = opts_ecos.abstol; 647 | mywork->stgs->feastol = opts_ecos.feastol; 648 | mywork->stgs->reltol = opts_ecos.reltol; 649 | mywork->stgs->abstol_inacc = opts_ecos.abstol_inacc; 650 | mywork->stgs->feastol_inacc = opts_ecos.feastol_inacc; 651 | mywork->stgs->reltol_inacc = opts_ecos.reltol_inacc; 652 | mywork->stgs->maxit = opts_ecos.maxit; 653 | mywork->stgs->nitref = opts_ecos.nitref; 654 | 655 | /* Solve! */ 656 | Py_BEGIN_ALLOW_THREADS; 657 | exitcode = ECOS_BB_solve(myecos_bb_work); 658 | Py_END_ALLOW_THREADS; 659 | mi_iterations =(long) myecos_bb_work->iter; 660 | 661 | } else{ 662 | 663 | /* This calls ECOS setup function. */ 664 | Py_BEGIN_ALLOW_THREADS; 665 | mywork = ECOS_setup(n, m, p, l, ncones, q, e, Gpr, Gjc, Gir, Apr, Ajc, Air, cpr, hpr, bpr); 666 | Py_END_ALLOW_THREADS; 667 | if( mywork == NULL ){ 668 | PyErr_SetString(PyExc_RuntimeError, "Internal problem occurred in ECOS while setting up the problem.\nPlease send a bug report with data to Alexander Domahidi.\nEmail: domahidi@control.ee.ethz.ch"); 669 | if(q) free(q); 670 | Py_DECREF(Gx_arr); Py_DECREF(Gi_arr); Py_DECREF(Gp_arr); 671 | Py_DECREF(c_arr); Py_DECREF(h_arr); 672 | if (b_arr) Py_DECREF(b_arr); 673 | if (Ax_arr) Py_DECREF(Ax_arr); 674 | if (Ai_arr) Py_DECREF(Ai_arr); 675 | if (Ap_arr) Py_DECREF(Ap_arr); 676 | return NULL; 677 | } 678 | 679 | /* Set settings for ECOS. */ 680 | mywork->stgs->verbose = opts_ecos.verbose; 681 | mywork->stgs->abstol = opts_ecos.abstol; 682 | mywork->stgs->feastol = opts_ecos.feastol; 683 | mywork->stgs->reltol = opts_ecos.reltol; 684 | mywork->stgs->abstol_inacc = opts_ecos.abstol_inacc; 685 | mywork->stgs->feastol_inacc = opts_ecos.feastol_inacc; 686 | mywork->stgs->reltol_inacc = opts_ecos.reltol_inacc; 687 | mywork->stgs->maxit = opts_ecos.maxit; 688 | mywork->stgs->nitref = opts_ecos.nitref; 689 | 690 | /* Solve! */ 691 | Py_BEGIN_ALLOW_THREADS; 692 | exitcode = ECOS_solve(mywork); 693 | Py_END_ALLOW_THREADS; 694 | } 695 | 696 | /* create output (all data is *deep copied*) */ 697 | /* TODO: request CVXOPT API for constructing from existing pointer */ 698 | /* x */ 699 | /* matrix *x; 700 | * if(!(x = Matrix_New(n,1,DOUBLE))) 701 | * return PyErr_NoMemory(); 702 | * memcpy(MAT_BUFD(x), mywork->x, n*sizeof(double)); 703 | */ 704 | veclen[0] = n; 705 | x = PyArray_SimpleNewFromData(1, veclen, NPY_DOUBLE, mywork->x); 706 | /* give memory ownership to numpy array */ 707 | PyArray_ENABLEFLAGS((PyArrayObject *) x, NPY_ARRAY_OWNDATA); 708 | 709 | /* y */ 710 | /* matrix *y; 711 | * if(!(y = Matrix_New(p,1,DOUBLE))) 712 | * return PyErr_NoMemory(); 713 | * memcpy(MAT_BUFD(y), mywork->y, p*sizeof(double)); 714 | */ 715 | veclen[0] = p; 716 | y = PyArray_SimpleNewFromData(1, veclen, NPY_DOUBLE, mywork->y); 717 | /* give memory ownership to numpy array */ 718 | PyArray_ENABLEFLAGS((PyArrayObject *) y, NPY_ARRAY_OWNDATA); 719 | 720 | /* s */ 721 | /* matrix *s; 722 | * if(!(s = Matrix_New(m,1,DOUBLE))) 723 | * return PyErr_NoMemory(); 724 | * memcpy(MAT_BUFD(s), mywork->s, m*sizeof(double)); 725 | */ 726 | veclen[0] = m; 727 | s = PyArray_SimpleNewFromData(1, veclen, NPY_DOUBLE, mywork->s); 728 | /* give memory ownership to numpy array */ 729 | PyArray_ENABLEFLAGS((PyArrayObject *) s, NPY_ARRAY_OWNDATA); 730 | 731 | /* z */ 732 | /* matrix *z; 733 | * if(!(z = Matrix_New(m,1,DOUBLE))) 734 | * return PyErr_NoMemory(); 735 | * memcpy(MAT_BUFD(z), mywork->z, m*sizeof(double)); 736 | */ 737 | veclen[0] = m; 738 | z = PyArray_SimpleNewFromData(1, veclen, NPY_DOUBLE, mywork->z); 739 | /* give memory ownership to numpy array */ 740 | PyArray_ENABLEFLAGS((PyArrayObject *) z, NPY_ARRAY_OWNDATA); 741 | 742 | if (num_bool > 0 || num_int > 0){ 743 | /* info dict */ 744 | /* infostring */ 745 | switch( exitcode ){ 746 | case MI_OPTIMAL_SOLN: 747 | infostring = "Optimal branch and bound solution found"; 748 | break; 749 | case MI_MAXITER_FEASIBLE_SOLN: 750 | infostring = "Maximum iterations reached with feasible solution found"; 751 | break; 752 | case MI_MAXITER_NO_SOLN: 753 | infostring = "Maximum iterations reached with no feasible solution found"; 754 | break; 755 | case MI_INFEASIBLE: 756 | infostring = "Problem is infeasible"; 757 | break; 758 | default: 759 | infostring = "UNKNOWN PROBLEM IN BRANCH AND BOUND SOLVER"; 760 | } 761 | } else { 762 | /* info dict */ 763 | /* infostring */ 764 | switch( exitcode ){ 765 | case ECOS_OPTIMAL: 766 | infostring = "Optimal solution found"; 767 | break; 768 | case ECOS_OPTIMAL + ECOS_INACC_OFFSET: 769 | infostring = "Close to optimal solution found"; 770 | break; 771 | case ECOS_MAXIT: 772 | infostring = "Maximum number of iterations reached"; 773 | break; 774 | case ECOS_PINF: 775 | infostring = "Primal infeasible"; 776 | break; 777 | case ECOS_PINF + ECOS_INACC_OFFSET: 778 | infostring = "Close to primal infeasible"; 779 | break; 780 | case ECOS_DINF: 781 | infostring = "Dual infeasible"; 782 | break; 783 | case ECOS_DINF + ECOS_INACC_OFFSET: 784 | infostring = "Close to dual infeasible"; 785 | break; 786 | case ECOS_NUMERICS: 787 | infostring = "Run into numerical problems"; 788 | break; 789 | case ECOS_OUTCONE: 790 | infostring = "PROBLEM: Multipliers leaving the cone"; 791 | break; 792 | case ECOS_FATAL: 793 | infostring = "PROBLEM: Fatal error during initialization"; 794 | break; 795 | default: 796 | infostring = "UNKNOWN PROBLEM IN SOLVER"; 797 | } 798 | 799 | /* numerical errors */ 800 | if( (exitcode == ECOS_NUMERICS) || (exitcode == ECOS_OUTCONE) || (exitcode == ECOS_FATAL) ){ 801 | numerr = 1; 802 | } 803 | } 804 | 805 | /* timings */ 806 | #if PROFILING > 0 807 | tinfos = Py_BuildValue( 808 | #if PROFILING > 1 809 | "{s:d,s:d,s:d,s:d,s:d,s:d,s:d,s:d}", 810 | #else 811 | "{s:d,s:d,s:d}", 812 | #endif 813 | #if PROFILING > 1 814 | "tkktcreate",(double)mywork->info->tkktcreate, 815 | "tkktsolve",(double)mywork->info->tkktsolve, 816 | "tkktfactor",(double)mywork->info->tfactor, 817 | "torder",(double)mywork->info->torder, 818 | "ttranspose",(double)mywork->info->ttranspose, 819 | #endif 820 | "runtime",(double)mywork->info->tsolve + (double)mywork->info->tsetup, 821 | "tsetup",(double)mywork->info->tsetup, 822 | "tsolve",(double)mywork->info->tsolve); 823 | #endif 824 | 825 | infoDict = Py_BuildValue( 826 | #if PROFILING > 0 827 | "{s:l,s:d,s:d,s:d,s:d,s:d,s:d,s:d,s:d,s:d,s:d,s:d,s:l,s:l,s:s,s:O,s:l}", 828 | #else 829 | "{s:l,s:d,s:d,s:d,s:d,s:d,s:d,s:d,s:d,s:d,s:d,s:d,s:l,s:l,s:s,s:l}", 830 | #endif 831 | "exitFlag", exitcode, 832 | "pcost", (double)mywork->info->pcost, 833 | "dcost", (double)mywork->info->dcost, 834 | "pres", (double)mywork->info->pres, 835 | "dres", (double)mywork->info->dres, 836 | "pinf", (double)mywork->info->pinf, 837 | "dinf", (double)mywork->info->dinf, 838 | "pinfres",(double)mywork->info->pinfres, 839 | "dinfres",(double)mywork->info->dinfres, 840 | "gap",(double)mywork->info->gap, 841 | "relgap",(double)mywork->info->relgap, 842 | "r0",(double)mywork->stgs->feastol, 843 | "iter",mywork->info->iter, 844 | "mi_iter",mi_iterations, 845 | "infostring",infostring, 846 | #if PROFILING > 0 847 | "timing", tinfos, 848 | #endif 849 | "numerr",numerr); 850 | 851 | #if PROFILING > 0 852 | /* give reference to infoDict */ 853 | Py_DECREF(tinfos); 854 | #endif 855 | 856 | returnDict = Py_BuildValue( 857 | "{s:O,s:O,s:O,s:O,s:O}", 858 | "x",x, 859 | "y",y, 860 | "z",z, 861 | "s",s, 862 | "info",infoDict); 863 | /* give up ownership to the return dictionary */ 864 | Py_DECREF(x); Py_DECREF(y); Py_DECREF(z); Py_DECREF(s); Py_DECREF(infoDict); 865 | 866 | /* cleanup */ 867 | if (num_bool > 0 || num_int > 0){ 868 | ECOS_BB_cleanup(myecos_bb_work, 4); 869 | } else { 870 | ECOS_cleanup(mywork, 4); 871 | } 872 | 873 | /* no longer need pointers to arrays that held primitives */ 874 | if(q) free(q); 875 | if(bool_vars_idx) free(bool_vars_idx); 876 | if(int_vars_idx) free(int_vars_idx); 877 | Py_DECREF(Gx_arr); Py_DECREF(Gi_arr); Py_DECREF(Gp_arr); 878 | Py_DECREF(c_arr); Py_DECREF(h_arr); 879 | if (b_arr) Py_DECREF(b_arr); 880 | if (Ax_arr) Py_DECREF(Ax_arr); 881 | if (Ai_arr) Py_DECREF(Ai_arr); 882 | if (Ap_arr) Py_DECREF(Ap_arr); 883 | 884 | return returnDict; 885 | } 886 | 887 | static PyMethodDef ECOSMethods[] = 888 | { 889 | {"csolve", (PyCFunction)csolve, METH_VARARGS | METH_KEYWORDS, 890 | "Solve an SOCP using ECOS."}, 891 | {"version", (PyCFunction)version, METH_NOARGS, "Version number for ECOS."}, 892 | {NULL, NULL, 0, NULL} /* sentinel */ 893 | }; 894 | 895 | /* Module initialization */ 896 | #if PY_MAJOR_VERSION >= 3 897 | static struct PyModuleDef moduledef = { 898 | PyModuleDef_HEAD_INIT, 899 | "_ecos", /* m_name */ 900 | "Solve an SOCP using ECOS.", /* m_doc */ 901 | -1, /* m_size */ 902 | ECOSMethods, /* m_methods */ 903 | NULL, /* m_reload */ 904 | NULL, /* m_traverse */ 905 | NULL, /* m_clear */ 906 | NULL, /* m_free */ 907 | }; 908 | #endif 909 | 910 | static PyObject* moduleinit(void) 911 | { 912 | PyObject* m; 913 | 914 | #if PY_MAJOR_VERSION >= 3 915 | m = PyModule_Create(&moduledef); 916 | #else 917 | m = Py_InitModule("_ecos", ECOSMethods); 918 | #endif 919 | 920 | /*if (import_array() < 0) return NULL; */ /* for numpy arrays */ 921 | /*if (import_cvxopt() < 0) return NULL; */ /* for cvxopt support */ 922 | 923 | if (m == NULL) 924 | return NULL; 925 | 926 | return m; 927 | }; 928 | 929 | #if PY_MAJOR_VERSION >= 3 930 | PyMODINIT_FUNC PyInit__ecos(void) 931 | { 932 | import_array(); /* for numpy arrays */ 933 | return moduleinit(); 934 | } 935 | #else 936 | PyMODINIT_FUNC init_ecos(void) 937 | { 938 | import_array(); /* for numpy arrays */ 939 | moduleinit(); 940 | } 941 | #endif 942 | -------------------------------------------------------------------------------- /src/test_interface.py: -------------------------------------------------------------------------------- 1 | import platform 2 | import pytest 3 | import ecos 4 | import numpy as np 5 | import scipy.sparse as sp 6 | 7 | # global data structures for problem 8 | c = np.array([-1.]) 9 | h = np.array([4., -0.]) 10 | G = (sp.csc_matrix([1., -1.]).T).tocsc() 11 | A = sp.csc_matrix([1.]) 12 | b = np.array([3.]) 13 | dims = {'q': [], 'l': 2} 14 | 15 | 16 | def check_solution(solution, expected): 17 | np.testing.assert_almost_equal(solution, expected, decimal=5) 18 | 19 | @pytest.mark.parametrize("inputs,expected", [ 20 | ((c, G, h, dims, {'feastol': 2e-8, 'reltol': 2e-8, 'abstol': 2e-8, 'verbose': False}), 4), 21 | ((c, G, h, dims, A, b, {'feastol': 2e-8, 'reltol': 2e-8, 'abstol': 2e-8, 'verbose': False}), 3), 22 | ((c, G, h, {'q': [2], 'l': 0}, {'feastol': 2e-8, 'reltol': 2e-8, 'abstol': 2e-8, 'verbose': False}), 2) 23 | ]) 24 | def test_problems(inputs, expected): 25 | sol = ecos.solve(*inputs[:-1], **inputs[-1]) 26 | check_solution(sol['x'][0], expected) 27 | 28 | 29 | def test_call_failures(): 30 | with pytest.raises(TypeError): 31 | ecos.solve() 32 | 33 | with pytest.raises(TypeError): 34 | ecos.solve(c, G, h, dims, A) 35 | 36 | with pytest.raises(ValueError): 37 | ecos.solve(c, G, h, {'q':[], 'l':0}) 38 | 39 | with pytest.raises(TypeError): 40 | ecos.solve(c, G, h, {'q':[4], 'l':-2}) 41 | 42 | 43 | @pytest.mark.parametrize("error_type,keyword,value", [ 44 | (TypeError, 'verbose', 0), 45 | (ValueError, 'feastol', 0), 46 | (ValueError, 'abstol', 0), 47 | (ValueError, 'reltol', 0), 48 | (ValueError, 'feastol_inacc', 0), 49 | (ValueError, 'abstol_inacc', 0), 50 | (ValueError, 'reltol_inacc', 0), 51 | (ValueError, 'max_iters', -1), 52 | (TypeError, 'max_iters', 1.1), 53 | ]) 54 | def test_keyword_errors(error_type, keyword, value): 55 | with pytest.raises(error_type): 56 | ecos.solve(c, G, h, dims, **{keyword: value}) 57 | -------------------------------------------------------------------------------- /src/test_interface_bb.py: -------------------------------------------------------------------------------- 1 | import ecos 2 | import numpy as np 3 | import scipy.sparse as sp 4 | 5 | c = np.array([-1., -1.]) 6 | h = np.array([ 4., 12., 0., 0.]) 7 | bool_idx = [1] 8 | G = sp.csc_matrix(( 9 | np.array([2.0, 3.0, -1.0, 1.0, 4.0, -1.0]), 10 | np.array([0, 1, 2, 0, 1, 3]), 11 | np.array([0, 3, 6]), 12 | )) 13 | 14 | dims = dict() 15 | dims['l'] = 4 16 | 17 | sol = ecos.solve(c, G, h, dims, verbose=False, mi_verbose=False, int_vars_idx=bool_idx) 18 | 19 | c = np.array([-1., -1.]) 20 | h = np.array([ 4., 12., 0., 0.]) 21 | bool_idx = [] 22 | G = sp.csc_matrix(( 23 | np.array([2.0, 3.0, -1.0, 1.0, 4.0, -1.0]), 24 | np.array([0, 1, 2, 0, 1, 3]), 25 | np.array([0, 3, 6]), 26 | )) 27 | 28 | dims = dict() 29 | dims['l'] = 4 30 | 31 | sol = ecos.solve(c, G, h, dims, verbose=False, mi_verbose=False, int_vars_idx=bool_idx) 32 | 33 | c = np.array([-1., -1.1]) 34 | h = np.array([ 4., 12., 0., 0.]) 35 | bool_idx = [1,0] 36 | G = sp.csc_matrix(( 37 | np.array([2.0, 3.0, -1.0, 1.0, 4.0, -1.0]), 38 | np.array([0, 1, 2, 0, 1, 3]), 39 | np.array([0, 3, 6]), 40 | )) 41 | 42 | dims = dict() 43 | dims['l'] = 4 44 | 45 | sol = ecos.solve(c, G, h, dims, verbose=False, mi_verbose=False, int_vars_idx=bool_idx) 46 | 47 | 48 | c = np.array([-1., -1.5]) 49 | h = np.array([ 4., 12., 0. , 0.]) 50 | bool_idx = [1] 51 | G = sp.csc_matrix(( 52 | np.array([2.0, 3.0, -1.0, 1.0, 4.0, -1.0]), 53 | np.array([0, 1, 2, 0, 1, 3]), 54 | np.array([0, 3, 6]), 55 | )) 56 | 57 | dims = dict() 58 | dims['l'] = 4 59 | 60 | sol = ecos.solve(c, G, h, dims, verbose=False, mi_verbose=True, bool_vars_idx=bool_idx) --------------------------------------------------------------------------------