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A framework based on advanced AI techniques can solve complex, computationally intensive problems faster and in a more scalable way than state-of-the-art methods, according to a study led by engineers at the University of California San Diego. In the paper, which was in , researchers present HypOp, a framework that uses unsupervised learning and hypergraph neural networks. The framework is able to solve combinatorial optimization problems significantly faster than existing methods.

HypOp is also able to solve certain combinatorial problems that can't be solved as effectively by prior methods. "In this paper, we tackle the difficult task of addressing combinatorial optimization problems that are paramount in many fields of science and engineering," said Nasimeh Heydaribeni, the paper's corresponding author and a postdoctoral scholar in the UC San Diego Department of Electrical and Computer Engineering. She is part of the research group of Professor Farinaz Koushanfar, who co-directs the Center for Machine-Intelligence, Computing and Security at the UC San Diego Jacobs School of Engineering.



Professor Tina Eliassi-Rad from Northeastern University also collaborated with the UC San Diego team on this project. One example of a relatively simple combinatorial problem is figuring out how many and what kind of goods to stock at specific warehouses in order to consume the least amount of gas when delivering these goods. HypOp can be applied to a broad spectrum of challenging real-worl.

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