cvxpylayers | Differentiable convex optimization layers | Math library

by cvxgrp Python Version: 0.1.6 License: Apache-2.0

X-Ray Key Features Code Snippets(1)Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | cvxpylayers Summary

cvxpylayers is a Python library typically used in Utilities, Math applications. cvxpylayers has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. You can install using 'pip install cvxpylayers' or download it from GitHub, PyPI.

cvxpylayers is a Python library for constructing differentiable convex optimization layers in PyTorch, JAX, 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 on differentiable convex optimization layers. For an informal introduction to convex optimization layers, see our blog post. Our package uses CVXPY for specifying parametrized convex optimization problems.

Support

Quality

Security

License

Reuse

Support

cvxpylayers has a medium active ecosystem.

It has 1540 star(s) with 137 fork(s). There are 54 watchers for this library.

It had no major release in the last 12 months.

There are 39 open issues and 64 have been closed. On average issues are closed in 106 days. There are 4 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of cvxpylayers is 0.1.6

Quality

cvxpylayers has 0 bugs and 0 code smells.

Security

cvxpylayers has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

cvxpylayers code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

cvxpylayers is licensed under the Apache-2.0 License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

cvxpylayers releases are not available. You will need to build from source code and install.

Deployable package is available in PyPI.

Build file is available. You can build the component from source.

Installation instructions, examples and code snippets are available.

cvxpylayers saves you 717 person hours of effort in developing the same functionality from scratch.

It has 2225 lines of code, 109 functions and 15 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed cvxpylayers and discovered the below as its top functions. This is intended to give you an instant insight into cvxpylayers implemented functionality, and help decide if they suit your requirements.

Construct a CVX layer
Given a list of parameters return information about the batch size
Simple qp test
Compute a CVX layer
Wrapper function for ConvxPyLayer
Converts torch tensor to numpy array
Convert numpy array to torch object
Full qp
Run ball problem
Compute the likelihood of the given parameters
Compute the QP test
Compute the QP
Runs a running example
Softmax problem
Resolve the problem
Sigmoid

Get all kandi verified functions for this library.

cvxpylayers Key Features

No Key Features are available at this moment for cvxpylayers.

cvxpylayers Examples and Code Snippets

diffqcqp,Experiments

C++

Lines of Code : 1

License : Permissive (BSD-2-Clause)

Copy

python test_script.py

Community Discussions

Trending Discussions on cvxpylayers

How to construct a SOCP problem and solve using cvxpy and cvxpylayers

QUESTION

How to construct a SOCP problem and solve using cvxpy and cvxpylayers

Asked 2020-Dec-14 at 02:47

I'm trying to solve a SOCP problem using cvxpy and integrating it to cvxpylayers. I'm looking at this SOCP problem (problem 11) (here is the scihub link in case you can't access), and here is a snippet of the problem (note min (p-t) comes from an adaptation of problem 4 using expression 8 in the link above):

Go to ---> EDIT 2

OLD

I've looked at this example, but is still stuck and can't get the problem to be solved. Here is a sample code at my attempt:

...

ANSWER

Answered 2020-Dec-11 at 12:20

You should change

x.T @ Q @ x <= N * p**2

(x.T @ Q @ x)/p <= N * p

assuming p>=0.

Btw if you want to know more about how to formulate this as a SOCP, then consult the Mosek modelling cookbok.

Source https://stackoverflow.com/questions/65227344

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install cvxpylayers

Use the package manager pip to install cvxpylayers. Our package includes convex optimization layers for PyTorch, JAX, and TensorFlow 2.0; the layers are functionally equivalent. You will need to install PyTorch, JAX, or TensorFlow separately, which can be done by following the instructions on their websites.
Python 3
NumPy
CVXPY >= 1.1.a4
PyTorch >= 1.0, JAX >= 0.2.12, or TensorFlow >= 2.0
diffcp >= 1.0.13

Support

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.

Find more information at: