Campus Units

Supply Chain and Information Systems

Document Type

Article

Publication Version

Published Version

Publication Date

2017

Journal or Book Title

Journal of Machine Learning Research

Volume

18

Issue

99

First Page

1

Last Page

32

Abstract

We propose a mixed integer programming (MIP) model and iterative algorithms based on topological orders to solve optimization problems with acyclic constraints on a directed graph. The proposed MIP model has a significantly lower number of constraints compared to popular MIP models based on cycle elimination constraints and triangular inequalities. The proposed iterative algorithms use gradient descent and iterative reordering approaches, respectively, for searching topological orders. A computational experiment is presented for the Gaussian Bayesian network learning problem, an optimization problem minimizing the sum of squared errors of regression models with L1 penalty over a feature network with application of gene network inference in bioinformatics.

Comments

This article is published as Y.W. Park and D. Klabjan (2017) Bayesian Newtwork Learning via Topological Order. Journal of Machine Learning Research 18(99);1-32.

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

Copyright Owner

The Author(s)

Language

en

File Format

application/pdf

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