Tutorial on amortized optimization
B Amos - Foundations and Trends® in Machine Learning, 2023 - nowpublishers.com
Optimization is a ubiquitous modeling tool and is often deployed in settings which
repeatedly solve similar instances of the same problem. Amortized optimization methods …
repeatedly solve similar instances of the same problem. Amortized optimization methods …
∇-prox: Differentiable proximal algorithm modeling for large-scale optimization
Tasks across diverse application domains can be posed as large-scale optimization
problems, these include graphics, vision, machine learning, imaging, health, scheduling …
problems, these include graphics, vision, machine learning, imaging, health, scheduling …
A closer look at learned optimization: Stability, robustness, and inductive biases
J Harrison, L Metz… - Advances in Neural …, 2022 - proceedings.neurips.cc
Learned optimizers---neural networks that are trained to act as optimizers---have the
potential to dramatically accelerate training of machine learning models. However, even …
potential to dramatically accelerate training of machine learning models. However, even …
Automated dynamic algorithm configuration
The performance of an algorithm often critically depends on its parameter configuration.
While a variety of automated algorithm configuration methods have been proposed to …
While a variety of automated algorithm configuration methods have been proposed to …
End-to-end learning to warm-start for real-time quadratic optimization
First-order methods are widely used to solve convex quadratic programs (QPs) in real-time
appli-cations because of their low per-iteration cost. However, they can suffer from slow …
appli-cations because of their low per-iteration cost. However, they can suffer from slow …
A robust energy management system for Korean green islands project
Penetration enhancement of renewable energy sources is a core component of Korean
green-island microgrid projects. This approach calls for a robust energy management …
green-island microgrid projects. This approach calls for a robust energy management …
Artificial Intelligence for Operations Research: Revolutionizing the Operations Research Process
The rapid advancement of artificial intelligence (AI) techniques has opened up new
opportunities to revolutionize various fields, including operations research (OR). This survey …
opportunities to revolutionize various fields, including operations research (OR). This survey …
A reinforcement learning approach to parameter selection for distributed optimal power flow
With the increasing penetration of distributed energy resources, distributed optimization
algorithms have attracted significant attention for power systems applications due to their …
algorithms have attracted significant attention for power systems applications due to their …
Deep reinforcement learning‐based active mass driver decoupled control framework considering control–structure interaction effects
Control–structure interaction (CSI) plays a significant role in active control systems. Popular
methods incorporate actuator dynamics into an integrated control system to account for CSI …
methods incorporate actuator dynamics into an integrated control system to account for CSI …
Learning Algorithm Hyperparameters for Fast Parametric Convex Optimization
R Sambharya, B Stellato - arXiv preprint arXiv:2411.15717, 2024 - arxiv.org
We introduce a machine-learning framework to learn the hyperparameter sequence of first-
order methods (eg, the step sizes in gradient descent) to quickly solve parametric convex …
order methods (eg, the step sizes in gradient descent) to quickly solve parametric convex …