The constant scaling of AI applications and other digital technologies across industries is beginning to tax the energy grid ...

This article investigates the problem of distributed convex optimization under constrained communication. A novel stochastic event-triggering algorithm is shown ...

In a world where machines can process infinite data points, will our greatest competitive advantage become our ability to ...

K-Means Algorithm, Influenza Transmission, Cluster Analysis, Urban Characteristics Share and Cite: Ye, S. (2025) Application ...

Faster topology optimization: An emerging industrial design technique gets a speed boost A new algorithm helps topology optimizers skip unnecessary iterations, making optimization and design faster, ...

Primal-dual methods in online optimization give several of the state-of-the art results in both of the most common models: adversarial and stochastic/random order. Here we try to provide a more ...

Sequential randomized algorithms are considered for robust convex optimization which minimizes a linear objective function subject to a parameter dependent convex constraint. Employing convex ...

In a recent review article in Light| Science & Applications, researchers provided a comprehensive overview of non-convex optimization algorithms used in computer-generated holography (CGH).

Another key feature is that it includes programming implementation of a variety of machine learning algorithms inspired by optimization fundamentals, together with a brief tutorial of the used ...