REVIEW 6 cited by
Differentiating Through a Cone Program
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
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 map for its homogeneous self-dual embedding, and solving the linear systems of equations required using an iterative method. This allows us to efficiently compute the derivative operator, and its adjoint, evaluated at a vector. These correspond to computing an approximate new solution, given a perturbation to the cone program coefficients (i.e., perturbation analysis), and to computing the gradient of a function of the solution with respect to the coefficients. Our method scales to large problems, with numbers of coefficients in the millions. We present an open-source Python implementation of our method that solves a cone program and returns the derivative and its adjoint as abstract linear maps; our implementation can be easily integrated into software systems for automatic differentiation.
Forward citations
Cited by 6 Pith papers
-
Back from the Future: Key-Value Cache Management by Counter-Causal Surprise
Past tokens that the model can predict from their future context are evicted from the KV cache, judged by a counter-causal attention pass that reuses cached keys and values.
-
End-to-End Learning of Safe Optimal Feedback Control in High Dimensions with Control Barrier Function Layers
A scalable end-to-end training method for neural controllers with embedded control-barrier-function safety filters, demonstrated up to 1200 state dimensions and 400 control dimensions, with convergence guarantees unde...
-
Physics-Grounded Differentiable Simulation for Soft Growing Robots
A differentiable simulator for soft growing robots with a new wrinkling-based bending stiffness model, fitted and validated against real robot trajectories.
-
DeePC-Hunt: Data-enabled Predictive Control Hyperparameter Tuning via Differentiable Optimization
DeePC-Hunt uses backpropagation through an approximate model to automatically tune DeePC regularization hyperparameters for closed-loop performance.
-
BPQP: A Differentiable Convex Optimization Framework for Efficient End-to-End Learning
BPQP reformulates the backward pass of differentiable convex optimization layers as an equality-constrained quadratic program, allowing fast ADMM-based solvers to compute gradients.
-
Differentiable Convex Optimization Layers in Neural Architectures: Foundations and Perspectives
A survey of differentiable convex optimization layers, synthesizing OptNet and cvxpylayers with proofs of known results, but containing mathematical inaccuracies.
Discussion (0). Continue with ORCID to comment.