Oct 28, 2019 · In this paper, we propose an approach to differentiating through disciplined convex programs, a subclass of convex optimization problems used by ...
The point of this paper is to do what DSLs have done for convex optimization, but for differentiable convex optimization layers. In this work, we show how to ...
cvxpylayers is a Python library for constructing differentiable convex optimization layers in PyTorch, JAX, and TensorFlow using CVXPY.
We're talking about layers that solve small (or else the network would take too long to actually perform a forward pass) optimization problems as the layer's ...
Oct 28, 2019 · In this tutorial we introduce our new library cvxpylayers for easily creating differentiable new convex optimization layers. This lets you ...
▷ Differentiable convex optimization layers can be used in an ADP method. Page 26. Learning convex-optimization control policies. ▷ Take φ to be the solution ...
In this paper, we propose an approach to differentiating through disciplined convex programs, a subclass of convex optimization problems used by domain-specific ...
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This paper introduces disciplined parametrized programming, a subset of disciplined convex programming, and demonstrates how to efficiently differentiate ...
Dec 30, 2024 · Abstract page for arXiv paper 2412.20679: Differentiable Convex Optimization Layers in Neural Architectures: Foundations and Perspectives.
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Originality: 5/5 The paper relies on already-studied building blocks: the DCP formalism, and the residual approach to differentiation through a cone solver.