Metadata-Version: 2.1
Name: cvxpylayers
Version: 0.1.4
Summary: Differentiable convex optimization layers
Home-page: https://github.com/cvxgrp/cvxpylayers
License: Apache License, Version 2.0
Description: ![cvxpylayers logo](cvxpylayers_logo.png)
        [![Build Status](https://travis-ci.org/cvxgrp/cvxpylayers.svg?branch=master)](https://travis-ci.org/cvxgrp/cvxpylayers)
        [![Build Status](https://ci.appveyor.com/api/projects/status/dhtbi9fb96mce56i/branch/master?svg=true)](https://ci.appveyor.com/project/sbarratt/cvxpylayers/branch/master)
        
        # cvxpylayers
        
        cvxpylayers is a Python library for constructing differentiable convex
        optimization layers in PyTorch and TensorFlow using CVXPY.
        A convex optimization layer solves a parametrized convex optimization problem
        in the forward pass to produce a solution.
        It computes the derivative of the solution with respect to
        the parameters in the backward pass.
        
        This library accompanies our [NeurIPS 2019 paper](http://web.stanford.edu/~boyd/papers/pdf/diff_cvxpy.pdf)
        on differentiable convex optimization layers.
        For an informal introduction to convex optimization layers, see our
        [blog post](https://locuslab.github.io/2019-10-28-cvxpylayers/).
        
        Our package uses [CVXPY](https://github.com/cvxgrp/cvxpy) for specifying
        parametrized convex optimization problems.
        
        - [Installation](#installation)
        - [Usage](#usage)
        - [Examples](#examples)
        - [Contributing](#contributing)
        - [Projects using cvxpylayers](#projects-using-cvxpylayers)
        - [License](#contributing)
        - [Citing](#citing)
        
        ## Installation
        
        Use the package manager [pip](https://pip.pypa.io/en/stable/) to install
        cvxpylayers.
        
        ```bash
        pip install cvxpylayers
        ```
        
        Our package includes convex optimization layers for PyTorch and TensorFlow 2.0;
        the layers are functionally equivalent. You will need to install
        [PyTorch](http://pytorch.org) or [TensorFlow](https://www.tensorflow.org)
        separately, which can be done by following the instructions on their websites.
        
        cvxpylayers has the following dependencies:
        * Python 3
        * [NumPy](https://pypi.org/project/numpy/)
        * [CVXPY](https://github.com/cvxgrp/cvxpy) >= 1.1.a4
        * [TensorFlow](https://tensorflow.org) >= 2.0 or [PyTorch](https://pytorch.org) >= 1.0
        * [diffcp](https://github.com/cvxgrp/diffcp) >= 1.0.13
        
        ## Usage
        Below are usage examples of our PyTorch and TensorFlow layers. Note that
        the parametrized convex optimization problems must be constructed in CVXPY,
        using [DPP](https://www.cvxpy.org/tutorial/advanced/index.html#disciplined-parametrized-programming).
        
        ### PyTorch
        
        ```python
        import cvxpy as cp
        import torch 
        from cvxpylayers.torch import CvxpyLayer
        
        n, m = 2, 3
        x = cp.Variable(n)
        A = cp.Parameter((m, n))
        b = cp.Parameter(m)
        constraints = [x >= 0]
        objective = cp.Minimize(0.5 * cp.pnorm(A @ x - b, p=1))
        problem = cp.Problem(objective, constraints)
        assert problem.is_dpp()
        
        cvxpylayer = CvxpyLayer(problem, parameters=[A, b], variables=[x])
        A_tch = torch.randn(m, n, requires_grad=True)
        b_tch = torch.randn(m, requires_grad=True)
        
        # solve the problem
        solution, = cvxpylayer(A_tch, b_tch)
        
        # compute the gradient of the sum of the solution with respect to A, b
        solution.sum().backward()
        ```
        
        Note: `CvxpyLayer` cannot be traced with `torch.jit`.
        
        ### TensorFlow 2
        ```python
        import cvxpy as cp
        import tensorflow as tf
        from cvxpylayers.tensorflow import CvxpyLayer
        
        n, m = 2, 3
        x = cp.Variable(n)
        A = cp.Parameter((m, n))
        b = cp.Parameter(m)
        constraints = [x >= 0]
        objective = cp.Minimize(0.5 * cp.pnorm(A @ x - b, p=1))
        problem = cp.Problem(objective, constraints)
        assert problem.is_dpp()
        
        cvxpylayer = CvxpyLayer(problem, parameters=[A, b], variables=[x])
        A_tf = tf.Variable(tf.random.normal((m, n)))
        b_tf = tf.Variable(tf.random.normal((m,)))
        
        with tf.GradientTape() as tape:
          # solve the problem, setting the values of A, b to A_tf, b_tf
          solution, = cvxpylayer(A_tf, b_tf)
          summed_solution = tf.math.reduce_sum(solution)
        # compute the gradient of the summed solution with respect to A, b
        gradA, gradb = tape.gradient(summed_solution, [A_tf, b_tf])
        ```
        
        Note: `CvxpyLayer` cannot be traced with `tf.function`.
        
        ### Log-log convex programs
        Starting with version 0.1.3, cvxpylayers can also differentiate through log-log convex programs (LLCPs), which generalize geometric programs. Use the keyword argument `gp=True` when constructing a `CvxpyLayer` for an LLCP. Below is a simple usage example
        
        ```python
        import cvxpy as cp
        import torch
        from cvxpylayers.torch import CvxpyLayer
                                                
        x = cp.Variable(pos=True)
        y = cp.Variable(pos=True)
        z = cp.Variable(pos=True)
                                                               
        a = cp.Parameter(pos=True, value=2.)
        b = cp.Parameter(pos=True, value=1.)
        c = cp.Parameter(value=0.5)
        
        objective_fn = 1/(x*y*z)
        objective = cp.Minimize(objective_fn)
        constraints = [a*(x*y + x*z + y*z) <= b, x >= y**c]
        problem = cp.Problem(objective, constraints)
        assert problem.is_dgp(dpp=True)
        
        layer = CvxpyLayer(problem, parameters=[a, b, c],
                           variables=[x, y, z], gp=True)
        a_tch = torch.tensor(a.value, requires_grad=True)
        b_tch = torch.tensor(b.value, requires_grad=True)
        c_tch = torch.tensor(c.value, requires_grad=True)
        
        x_star, y_star, z_star = layer(a_tch, b_tch, c_tch)
        sum_of_solution = x_star + y_star + z_star
        sum_of_solution.backward()
        ```
        
        ## Examples
        Our [examples](examples) subdirectory contains simple applications of convex optimization
        layers in IPython notebooks.
        
        ## Contributing
        Pull requests are welcome. For major changes, please open an issue first to
        discuss what you would like to change.
        
        Please make sure to update tests as appropriate.
        
        Please lint the code with `flake8`.
        ```bash
        pip install flake8  # if not already installed
        flake8
        ```
        
        ### Running tests
        
        cvxpylayers uses the `pytest` framework for running tests.
        To install `pytest`, run:
        ```bash
        pip install pytest
        ```
        
        To run the tests for `torch`, in the main directory of this repository, run:
        ```bash
        pytest cvxpylayers/torch
        ``` 
        
        To run the tests for `tensorflow`, in the main directory of this repository, run:
        ```bash
        pytest cvxpylayers/tensorflow
        ```
        
        ## Projects using cvxpylayers
        Below is  a list of projects using cvxpylayers. If you have used cvxpylayers in a project, you're welcome to make a PR to add it to this list.
        * [Learning Convex Optimization Control Policies](http://web.stanford.edu/~boyd/papers/learning_cocps.html)
        
        ## License
        cvxpylayers carries an Apache 2.0 license.
        
        ## Citing
        If you use cvxpylayers for research, please cite our accompanying [NeurIPS paper](http://web.stanford.edu/~boyd/papers/pdf/diff_cvxpy.pdf):
        
        ```
        @inproceedings{cvxpylayers2019,
          author={Agrawal, A. and Amos, B. and Barratt, S. and Boyd, S. and Diamond, S. and Kolter, Z.},
          title={Differentiable Convex Optimization Layers},
          booktitle={Advances in Neural Information Processing Systems},
          year={2019},
        }
        ```
        
        If you use cvxpylayers to differentiate through a log-log convex program, please cite the accompanying [paper](http://web.stanford.edu/~boyd/papers/diff_dgp.html):
        
        ```
        @article{agrawal2020differentiating,
          title={Differentiating through log-log convex programs},
          author={Agrawal, Akshay and Boyd, Stephen},
          journal={arXiv},
          archivePrefix={arXiv},
          eprint={2004.12553},
          primaryClass={math.OC},
          year={2020},
        }
        ```
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Description-Content-Type: text/markdown
