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ReLU Networks as Surrogate Models in Mixed-Integer Linear Programs

ReLU Networks as Surrogate Models in Mixed-Integer Linear Programs

6 July 2019
B. Grimstad
H. Andersson
ArXivPDFHTML

Papers citing "ReLU Networks as Surrogate Models in Mixed-Integer Linear Programs"

14 / 14 papers shown
Title
Generative AI and Process Systems Engineering: The Next Frontier
Generative AI and Process Systems Engineering: The Next Frontier
Benjamin Decardi-Nelson
Abdulelah S. Alshehri
Akshay Ajagekar
Fengqi You
AI4CE
LLMAG
37
24
0
15 Feb 2024
When Deep Learning Meets Polyhedral Theory: A Survey
When Deep Learning Meets Polyhedral Theory: A Survey
Joey Huchette
Gonzalo Muñoz
Thiago Serra
Calvin Tsay
AI4CE
94
33
0
29 Apr 2023
Model-based feature selection for neural networks: A mixed-integer
  programming approach
Model-based feature selection for neural networks: A mixed-integer programming approach
Shudian Zhao
Calvin Tsay
Jan Kronqvist
46
5
0
20 Feb 2023
Physics Informed Piecewise Linear Neural Networks for Process
  Optimization
Physics Informed Piecewise Linear Neural Networks for Process Optimization
Ece S. Koksal
E. Aydın
PINN
24
11
0
02 Feb 2023
Finding Regions of Counterfactual Explanations via Robust Optimization
Finding Regions of Counterfactual Explanations via Robust Optimization
Donato Maragno
Jannis Kurtz
Tabea E. Rober
Rob Goedhart
cS. .Ilker Birbil
D. Hertog
52
22
0
26 Jan 2023
Counterfactual Explanations Using Optimization With Constraint Learning
Counterfactual Explanations Using Optimization With Constraint Learning
Donato Maragno
Tabea E. Rober
Ilker Birbil
CML
58
10
0
22 Sep 2022
On data-driven chance constraint learning for mixed-integer optimization
  problems
On data-driven chance constraint learning for mixed-integer optimization problems
Antonio Alcántara
Carlos Ruiz
24
7
0
08 Jul 2022
P-split formulations: A class of intermediate formulations between big-M and convex hull for disjunctive constraints
P-split formulations: A class of intermediate formulations between big-M and convex hull for disjunctive constraints
Jan Kronqvist
Ruth Misener
Calvin Tsay
54
7
0
10 Feb 2022
Mixed-Integer Optimization with Constraint Learning
Mixed-Integer Optimization with Constraint Learning
Donato Maragno
H. Wiberg
Dimitris Bertsimas
Ş. Birbil
D. Hertog
Adejuyigbe O. Fajemisin
61
50
0
04 Nov 2021
Modeling the AC Power Flow Equations with Optimally Compact Neural
  Networks: Application to Unit Commitment
Modeling the AC Power Flow Equations with Optimally Compact Neural Networks: Application to Unit Commitment
Alyssa Kody
Samuel C. Chevalier
Spyros Chatzivasileiadis
Daniel Molzahn
66
37
0
21 Oct 2021
Optimization with Constraint Learning: A Framework and Survey
Optimization with Constraint Learning: A Framework and Survey
Adejuyigbe O. Fajemisin
Donato Maragno
D. Hertog
58
47
0
05 Oct 2021
Fast Design Space Exploration of Nonlinear Systems: Part I
Fast Design Space Exploration of Nonlinear Systems: Part I
S. Narain
Emily Mak
Dana Chee
Brendan Englot
K. Pochiraju
N. Jha
Karthik Narayan
25
5
0
05 Apr 2021
Strong mixed-integer programming formulations for trained neural
  networks
Strong mixed-integer programming formulations for trained neural networks
Ross Anderson
Joey Huchette
Christian Tjandraatmadja
J. Vielma
21
251
0
20 Nov 2018
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks
Guy Katz
Clark W. Barrett
D. Dill
Kyle D. Julian
Mykel Kochenderfer
AAML
249
1,842
0
03 Feb 2017
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