TY - GEN
T1 - End-to-End Constrained Optimization Learning
T2 - 30th International Joint Conference on Artificial Intelligence, IJCAI 2021
AU - Kotary, James
AU - Fioretto, Ferdinando
AU - van Hentenryck, Pascal
AU - Wilder, Bryan
N1 - Publisher Copyright:
© 2021 International Joint Conferences on Artificial Intelligence. All rights reserved.
PY - 2021
Y1 - 2021
N2 - This paper surveys the recent attempts at leveraging machine learning to solve constrained optimization problems. It focuses on surveying the work on integrating combinatorial solvers and optimization methods with machine learning architectures. These approaches hold the promise to develop new hybrid machine learning and optimization methods to predict fast, approximate, solutions to combinatorial problems and to enable structural logical inference. This paper presents a conceptual review of the recent advancements in this emerging area.
AB - This paper surveys the recent attempts at leveraging machine learning to solve constrained optimization problems. It focuses on surveying the work on integrating combinatorial solvers and optimization methods with machine learning architectures. These approaches hold the promise to develop new hybrid machine learning and optimization methods to predict fast, approximate, solutions to combinatorial problems and to enable structural logical inference. This paper presents a conceptual review of the recent advancements in this emerging area.
UR - http://www.scopus.com/inward/record.url?scp=85118028991&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85118028991&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85118028991
T3 - IJCAI International Joint Conference on Artificial Intelligence
SP - 4475
EP - 4482
BT - Proceedings of the 30th International Joint Conference on Artificial Intelligence, IJCAI 2021
A2 - Zhou, Zhi-Hua
PB - International Joint Conferences on Artificial Intelligence
Y2 - 19 August 2021 through 27 August 2021
ER -