Global optimisation methods and algorithms are pivotal in addressing complex problems where the objective function is often non‐convex, multi‐modal, or even presented as a black‐box with expensive ...

Interior-point methods constitute a robust class of algorithms for solving constrained optimisation problems by exploring the interior of the feasible region instead of traversing its boundary. These ...

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 ...

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 ...

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 ...

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

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

Computer-aided simulation and optimisation methods offer comprehensive possibilities for analysing and improving thermoelectric generators (TEG). Our technical expertise and access to extensive ...

Modern optimization theory, algorithms, and applications in process engineering. Topics include the fundamentals of linear programming, integer programming, nonlinear programming, mixed-integer ...