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Found 10 papers, 9 papers with code
Joint Graph Learning and Model Fitting in Laplacian Regularized Stratified Models
Laplacian regularized stratified models (LRSM) are models that utilize the explicit or implicit network structure of the sub-problems as defined by the categorical features called strata (e. g., age, region, time, forecast horizon, etc.
Minimum-Distortion Embedding
Our software scales to data sets with millions of items and tens of millions of distortion functions.
Learning Convex Optimization Models
A convex optimization model predicts an output from an input by solving a convex optimization problem.
Differentiating through Log-Log Convex Programs
We use the adjoint of the derivative to implement differentiable log-log convex optimization layers in PyTorch and TensorFlow.
Optimization and Control
Learning Convex Optimization Control Policies
Common examples of such convex optimization control policies (COCPs) include the linear quadratic regulator (LQR), convex model predictive control (MPC), and convex control-Lyapunov or approximate dynamic programming (ADP) policies.
Differentiable Convex Optimization Layers
In this paper, we propose an approach to differentiating through disciplined convex programs, a subclass of convex optimization problems used by domain-specific languages (DSLs) for convex optimization.
Disciplined Quasiconvex Programming
We present a composition rule involving quasiconvex functions that generalizes the classical composition rule for convex functions.
Optimization and Control Mathematical Software
Differentiating Through a Cone Program
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.
Optimization and Control
TensorFlow Eager: A Multi-Stage, Python-Embedded DSL for Machine Learning
TensorFlow Eager is a multi-stage, Python-embedded domain-specific language for hardware-accelerated machine learning, suitable for both interactive research and production.
A Rewriting System for Convex Optimization Problems
We describe a modular rewriting system for translating optimization problems written in a domain-specific language to forms compatible with low-level solver interfaces.
Optimization and Control Mathematical Software
