Work

Learning to Branch with Interpretable Machine Learning Models

Year: 2024

Type: conference-paper

Abstract: Machine learning is being increasingly used in improving decisions made within branch-and-bound algorithms for solving mixed-integer programs (MIPs). Branching is a key component in branch-and-bound algorithms, this work presents IDAES-core project update on building simple and interpretable machine learning models for branching and improving decision-making tools applied for the optimization of advanced energy systems.

DOI: 10.2172/2447617

Authors: Selin Bayramoglu, George Nemhauser, Nick Sahinidis

Institution: Georgia Institute of Technology

Language: English

Topic: Advanced Optimization Algorithms Research

Subfield: Numerical Analysis

Field: Mathematics

Domain: Physical Sciences

Open Access status: gold

OpenAlex ID: W4389500013

FWCI:

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