A group of researchers at the Massachusetts Institute of Technology have devised a potentially more effective way of helping computers solve some of the toughest optimization problems they face. Their ...

Conventional quantum algorithms are not feasible for solving combinatorial optimization problems (COPs) with constraints in the operation time of quantum computers. To address this issue, researchers ...

Research teams from energy giant ExxonMobil and IBM have been working together to find quantum solutions to one of the most complex problems of our time: managing the tens of thousands of merchant ...

In the lower part of the figure, it can be seen that L2O leverages on a set of training problem instances from the target optimization problem class to gain knowledge. This knowledge can help identify ...

Tsukuba, Japan—Distributed constraint optimization problems are crucial for modeling cooperative-multiagent systems. Asynchronous Distributed OPTimization (ADOPT) is a well-known algorithm for solving ...

Researchers have developed a new, data-driven machine-learning technique that speeds up software programs used to solve complex optimization problems that can have millions of potential solutions.

This course examines formulation and solution of applicable optimization models, including linear, integer, nonlinear, and network problems, efficient algorithm methods, and use of computer modeling ...