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Found 8 papers, 3 papers with code
A cutting plane algorithm for globally solving low dimensional k-means clustering problems
Clustering is one of the most fundamental tools in data science and machine learning, and k-means clustering is one of the most common such methods.
Model-based feature selection for neural networks: A mixed-integer programming approach
In this work, we develop a novel input feature selection framework for ReLU-based deep neural networks (DNNs), which builds upon a mixed-integer optimization approach.
P-split formulations: A class of intermediate formulations between big-M and convex hull for disjunctive constraints
We develop a class of mixed-integer formulations for disjunctive constraints intermediate to the big-M and convex hull formulations in terms of relaxation strength.
Maximizing information from chemical engineering data sets: Applications to machine learning
For each of these four data characteristics, we discuss applications where these data characteristics arise and show how current chemical engineering research is extending the fields of data science and machine learning to incorporate these challenges.
Partition-based formulations for mixed-integer optimization of trained ReLU neural networks
This paper introduces a class of mixed-integer formulations for trained ReLU neural networks.
Between steps: Intermediate relaxations between big-M and convex hull formulations
This work develops a class of relaxations in between the big-M and convex hull formulations of disjunctions, drawing advantages from both.
A disjunctive cut strengthening technique for convex MINLP
We prove that both types of cuts are valid and that the second type of cut can dominate both the first type and the original cut.
ENTMOOT: A Framework for Optimization over Ensemble Tree Models
Gradient boosted trees and other regression tree models perform well in a wide range of real-world, industrial applications.
