Thesis abstract:

«p»Incorporating predictive models into online algorithms improves their resilience and uniformity. It is especially pertinent in dynamic settings, where the capacity to react promptly to fresh data or revised projections can significantly enhance the efficiency of the optimisation process. Integrating predictive modelling and online decision-making frameworks is crucial for tackling the issues outlined in this project. This integration provides a means to achieve more efficient and effective solutions in submodular optimisation with cardinality constraints.«/p»