Advances in Machine Learning and Mathematical Modeling for Optimization Problems

Dear Colleagues,

Machine learning and deep learning have made tremendous progress over the last decade and have become the de facto standard across a wide range of image, video, text, and sound processing domains, from object recognition to image generation. More recently, deep learning and deep reinforcement learning have begun to develop end-to-end training to solve more complex operation research and combinatorial optimization problems, such as covering problems, vehicle routing problems, traveling salesmen problems, scheduling problems, and other complex problems requiring more general simulations. These methods also sometimes include classic search and optimization algorithms to machine learning, such as Monte Carlo Tree Search in AlphaGO.

This Special Issue focuses on recent advances in machine learning and mathematical modeling for optimization problems. Topics include but are not limited to:

New combinations of algorithms or new deep neural network architectures or loss functions specifically adapted to solve graph and combinatorial optimization problems are particularly welcome.

Prof. Dr. Francois Rivest
Prof. Dr. Abdellah Chehri
Guest Editors

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