Optimization problems often involve situations in which the user's goal is to minimize and/or maximize not a single objective function, but several, usually conflicting, functions simultaneously. Such ...

Abstract: The challenge of dynamic multimodal optimization problems (DMMOPs) lies in tracking multiple global or locally acceptable optimal solutions in environments that change over time. Typically, ...

A line of engineering research seeks to develop computers that can tackle a class of challenges called combinatorial optimization problems. These are common in real-world applications such as ...

Solving optimization problems is challenging for existing digital computers and even for future quantum hardware. The practical importance of diverse problems, from healthcare to financial ...

We present OPT-BENCH, a benchmark comprising 20 machine learning tasks and 10 NP problems, specifically designed to assess large language models’ (LLMs) ability to solve problems with large search ...

Alessio Figalli studies optimal transport, a field of math that ranges from the movements of clouds to the workings of chatbots. By Siobhan Roberts The words “optimal” and “optimize” derive from the ...

Abstract: The use of renewable energy for power generation is receiving increasing attention. However, the high uncertainty of renewable energy increases the difficulty of maintaining stable operation ...

1 School of Mathematics and Statistics, Fuzhou University, Fuzhou, China. 2 College of Computer and Data Science, Fuzhou University, Fuzhou, China. In this paper, we use Physics-Informed Neural ...

We present a joint multi-robot trajectory optimizer that can compute trajectories for tens of robots in aerial swarms within a small fraction of a second. The computational efficiency of our approach ...