Search Results for author:
Found 5 papers, 3 papers with code
PySCIPOpt-ML: Embedding Trained Machine Learning Models into Mixed-Integer Programs
In this paper we propose a tool for the automatic MIP formulation of trained ML models, allowing easy integration of ML constraints into MIPs.
Cutting Plane Selection with Analytic Centers and Multiregression
Cutting planes are a crucial component of state-of-the-art mixed-integer programming solvers, with the choice of which subset of cuts to add being vital for solver performance.
Adaptive Cut Selection in Mixed-Integer Linear Programming
We show for a specific cut selection rule, that any finite grid search of the parameter space will always miss all parameter values, which select integer optimal inducing cuts in an infinite amount of our problems.
The Variational Bayesian Inference for Network Autoregression Models
We develop a variational Bayesian (VB) approach for estimating large-scale dynamic network models in the network autoregression framework.
Generative deep learning for decision making in gas networks
We utilise a deep neural network discriminator and a MILP solver as our oracle to train our generative neural network.
