Convex optimization layers

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.

Does TensorFlow support differentiable convex optimization layers?

Most prior work on differentiable optimization layers has used PyTorch and in our project we significantly bring differentiable convex optimization layers to TensorFlow so that it is just as easy now

Overview

How can a convex optimization problem be differentiable in cvxpy?

This project turns every convex optimization problem expressed in CVXPY into a differentiable layer

Before this, implementing these layers has required manually implementing efficient problem-specific batched solvers and manually implicitly differentiating the optimization problem

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 tutorial is downloadable as a Jupyter notebookand in the following portion we interleave PyTorch code inlineto create all of th…

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