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jdumouchelle committed Apr 19, 2024
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387 changes: 387 additions & 0 deletions index.html
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<script src="http://www.google.com/jsapi" type="text/javascript"></script>
<script type="text/javascript">google.load("jquery", "1.3.2");</script>

<style type="text/css">
body {
font-family: "HelveticaNeue-Light", "Helvetica Neue Light", "Helvetica Neue", Helvetica, Arial, "Lucida Grande", sans-serif;
font-weight:300;
font-size:18px;
margin-left: auto;
margin-right: auto;
width: 1100px;
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h1 {
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box-shadow:
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</style>

<html>
<head>
<title>Neur2RO: Neural Two-Stage Robust Optimization</title>
<meta property="og:image" content="Path to my teaser.pdf"/> <!-- Facebook automatically scrapes this. Go to https://developers.facebook.com/tools/debug/ if you update and want to force Facebook to rescrape. -->
<meta property="og:title" content="Neur2RO: Neural Two-Stage Robust Optimization" />
<meta property="og:description" content="A neural network-based technique" />

<!-- Get from Google Analytics -->
<!-- Global site tag (gtag.js) - Google Analytics -->
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<script>
window.dataLayer = window.dataLayer || [];
function gtag(){dataLayer.push(arguments);}
gtag('js', new Date());
gtag('config', 'UA-75863369-6');
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</head>

<body>
<br>
<center>
<span style="font-size:36px">Neur2RO: Neural Two-Stage Robust Optimization</span>
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<span style="font-size:24px"><a href="https://jdumouchelle.github.io/">Justin Dumouchelle<sup>1</sup></a></span>
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<span style="font-size:24px"><a href="https://www.tudelft.nl/ewi/over-de-faculteit/afdelingen/applied-mathematics/people/eat-esther-julien-msc">Esther Julien<sup>2</sup></a></span>
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<span style="font-size:24px"><a href="https://www.uva.nl/en/profile/k/u/j.kurtz/j.kurtz.html?cb">Jannis Kurtz<sup>3</sup></a></span>
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<span style="font-size:24px"><a href="https://ekhalil.com/">Elias B. Khalil<sup>1</sup></a></span>
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<br>
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<span><sup>1</sup>University of Toronto</span>
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<span><sup>2</sup>TU Delft</span>
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<span><sup>3</sup>University of Amsterdam</span>
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<span><a href="mailto:[email protected]">[email protected]</a></span>
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<br>
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<span style="font-size:24px"><a href='https://arxiv.org/abs/2310.04345'>[Paper]&nbsp&nbsp</a></span>
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<span style="font-size:24px"><a href='https://github.com/khalil-research/Neur2RO'>[GitHub]&nbsp&nbsp</a></span><br>
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<span style="font-size:24px"><a href='https://www.youtube.com/watch?v=3cl0smeaTxk'>[Video]&nbsp&nbsp</a></span><br>
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<span style="font-size:24px"><a href='https://github.com/khalil-research/Neur2SP/raw/gh-pages/resources/Neur2SP_Slides.pdf'>[Slides]&nbsp&nbsp</a></span><br>
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<!-- <td align=center width=120px>
<center>
<span style="font-size:24px"><a href='https://github.com/khalil-research/Neur2SP/raw/gh-pages/resources/Neur2SP_Poster.pdf'>[Poster]&nbsp&nbsp</a></span><br>
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</table>
</center>
<br><br>
<center>
<table align=center width=1200px>
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<td width=260px>
<center>
<img class="round" style="width:1000px" src="./resources/teaser.pdf"/>
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<table align=center width=850px>
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<center>
Overview of Neur2RO, a fast learning-based approximation to column-and-constraint generation (CCG) for solving two-stage robust optimization problems. In each iteration, a <i>main problem</i> (box (a)) is solved to find a good first-stage solution x for the set of scenarios that have been identified thus far (initially, none). Then, an <i>adversarial problem</i> (box (c)) is solved to obtain a new scenario for which the solution x is not feasible anymore in main problem. If no such scenario exists, then x is optimal and CCG terminates. Otherwise, the adversarial scenario is added to the set of worst-case scenarios and we iterate to main problem. For each of the main and adversarial problems, we show two versions: classical (CCG, boxes (a) and (c)) and learning-augmented (Neur2RO, dashed boxes (b) and (d))
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<hr>

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<center><h1>Abstract</h1></center>
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<td style="text-align: justify; text-align-last: left;">
Robust optimization provides a mathematical framework for modeling and solving decision-making problems under worst-case uncertainty. This work addresses two-stage robust optimization (2RO) problems (also called adjustable robust optimization), wherein first-stage and second-stage decisions are made before and after uncertainty is realized, respectively. This results in a nested min-max-min optimization problem which is extremely challenging computationally, especially when the decisions are discrete. We propose Neur2RO, an efficient machine learning-driven instantiation of column-and-constraint generation (CCG), a classical iterative algorithm for 2RO. Specifically, we learn to estimate the value function of the second-stage problem via a novel neural network architecture that is easy to optimize over by design. Embedding our neural network into CCG yields high-quality solutions quickly as evidenced by experiments on two 2RO benchmarks, knapsack and capital budgeting. For knapsack, Neur2RO finds solutions that are within roughly 2% of the best-known values in a few seconds compared to the three hours of the state-of-the-art exact branch-and-price algorithm; for larger and more complex instances, Neur2RO finds even better solutions. For capital budgeting, Neur2RO outperforms three variants of the k-adaptability algorithm, particularly on the largest instances, with a 10 to 100-fold reduction in solution time.
</td>
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</table>
<br>

<!-- <hr>
<center><h1><a href='https://github.com/khalil-research/Neur2SP/tree/gh-pages/resources/Neur2SP_Slides.pdf'>[Slides]</a></h1></center>
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<td><img class="round" style="width:450px" src="./resources/method_diagram.png"/></td>
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<hr>

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<center><h1><a href="https://www.youtube.com/watch?v=3cl0smeaTxk">[Video]</a></h1></center>
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<p align="center">
<iframe width="500" height="350" src="https://www.youtube.com/watch?v=3cl0smeaTxk"></iframe>
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</table>
<br>
<hr>

<!-- <center><h1><a href='https://github.com/khalil-research/Neur2SP/raw/gh-pages/resources/Neur2SP_Poster.pdf'>[Poster]</a></h1></center>
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<td><img class="round" style="width:500px" src="./resources/Neur2SP_poster_preview.png"/></td>
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<span style="font-size:28px"><a href=''>[Slides]</a>
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Short description if wanted
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<!-- <table align=center width=800px>
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<span style="font-size:28px">&nbsp;<a href='https://github.com/khalil-research/Neur2SP'>[GitHub]</a>
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<hr>
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<center><h1>Paper and Supplementary Material</h1></center>
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<td><a href=""><img class="layered-paper-big" style="height:175px" src="./resources/paper.pdf"/></a></td>
<td><span style="font-size:14pt">Dumouchelle, Julien, Kurtz, Khalil.<br>
<b>Neur2RO: Neural Two-Stage Robust Optimization.</b><br>
In ICLR, 2024.<br>
(hosted on <a href="https://arxiv.org/abs/2310.04345">ArXiv</a>)<br>
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<a href="./resources/bibtex.txt">[Bibtex]</a>
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<!-- <table align=center width=900px>
<center><h1>Acknowledgements</h1></center>
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<ul>
<li>Bodur would like to acknowledge support from an NSERC Discovery Grant.</li>
<li>Dumouchelle, Patel, and Khalil acknowledge support from the Scale AI Research Chair Program and an NSERC Discovery Grant.</li>
<li>Website template adapted from <a href="https://github.com/richzhang/webpage-template">here</a>.</li>
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</body>
</html>

6 changes: 6 additions & 0 deletions resources/bibtex.txt
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@inproceedings{dumouchelle2023neur2ro,
title={Neur2{RO}: Neural Two-Stage Robust Optimization},
author={Dumouchelle, Justin and Julien, Esther and Kurtz, Jannis and Khalil, Elias Boutros},
booktitle={The Twelfth International Conference on Learning Representations},
year={2024}
}
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