Global optimisation stands as a pivotal discipline in applied mathematics and engineering, providing robust methodologies for determining the absolute best solution among myriad local optima. Recent ...

Database optimization has long relied on traditional methods that struggle with the complexities of modern data environments. These methods often fail to efficiently handle large-scale data, complex ...

An important aspect in software engineering is the ability to distinguish between premature, unnecessary, and necessary ...

This course offers an introduction to mathematical nonlinear optimization with applications in data science. The theoretical foundation and the fundamental algorithms for nonlinear optimization are ...

The power generation sector has been challenged by trends in digitalization, growth of intermittent renewables, equipment performance improvements, new generation, and storage technologies.

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Machine learning (ML) and deep learning (DL) as two well-known methods of artificial intelligence (AI) have emerged as powerful tools in extracting insights and patterns from vast amounts of data. In ...

Electromagnetic (EM) wave front modulation has important significance in both scientific researches and industrial applications. However, conventional dielectric materials have limited choices of ...

In the domain of metamaterials, the push toward automated design has been accelerated by advances in generative machine learning. The advent of deep ...

where \(\mathsf{G}(\cdot)\) is some convex operator and \(\mathcal{F}\) is as set of feasible input distributions. Examples of such an optimization problem include finding capacity in information ...