4 papers · 1 filter
Generating adversarial inputs for a graph neural network model of AC power flow
Robert Parker
This work formulates and solves optimization problems to generate input points that yield high errors between a neural network's predicted AC power flow solution and solutions to t…
MathOptAI.jl: Embed trained machine learning predictors into JuMP models
Oscar Dowson, Robert B Parker, Russel Bent
We present \texttt{MathOptAI.jl}, an open-source Julia library for embedding trained machine learning predictors into a JuMP model. \texttt{MathOptAI.jl} can embed a wide variety o…
Nonlinear Optimization with GPU-Accelerated Neural Network Constraints
Robert Parker, Oscar Dowson, Nicole LoGiudice +2
We propose a reduced-space formulation for optimizing over trained neural networks where the network's outputs and derivatives are evaluated on a GPU. To do this, we treat the neur…
Formulations and scalability of neural network surrogates in nonlinear optimization problems
Robert B. Parker, Oscar Dowson, Nicole LoGiudice +2
We compare full-space, reduced-space, and gray-box formulations for representing trained neural networks in nonlinear constrained optimization problems. We test these formulations…