133 citations · 204 across the 12 of their papers we have counts for
5 papers · 1 filter
Learning Convex Optimization Control Policies
Akshay Agrawal, Shane Barratt, Stephen Boyd +1
Many control policies used in various applications determine the input or action by solving a convex optimization problem that depends on the current state and some parameters. Com…
Differentiable Convex Optimization Layers
Akshay Agrawal, Brandon Amos, Shane Barratt +3
Recent work has shown how to embed differentiable optimization problems (that is, problems whose solutions can be backpropagated through) as layers within deep learning architectur…
Disciplined Quasiconvex Programming
Akshay Agrawal, Stephen Boyd
We present a composition rule involving quasiconvex functions that generalizes the classical composition rule for convex functions. This rule complements well-known rules for the c…
Differentiating Through a Cone Program
Akshay Agrawal, Shane Barratt, Stephen Boyd +2
We consider the problem of efficiently computing the derivative of the solution map of a convex cone program, when it exists. We do this by implicitly differentiating the residual…
TensorFlow Eager: A Multi-Stage, Python-Embedded DSL for Machine Learning
Akshay Agrawal, Akshay Naresh Modi, Alexandre Passos +8
TensorFlow Eager is a multi-stage, Python-embedded domain-specific language for hardware-accelerated machine learning, suitable for both interactive research and production. Tensor…