The process of solving optimisation problems thus is not only mathematically intensive but also time-consuming with the whole process being achieved within a long time scale and without adhering to any time constraint. Traditional convex optimisation methods are currently expensive and extremely time-consuming.
Thanks to the increased computing power, modern algorithms, and new coding approaches, the prospects of real-time optimization, in which problems can be strictly solved on micro/milli-second time-scales, have become more evident. Real-time optimization has been deployed in practice as a result of essential requirements of system improvement, intelligent business models, and dynamic resource allocation.
With the demand for real-time responses in the future, online optimization will become one of the most cutting-edge technologies beside machine learning, big-data analytics, cloud technology, and IoT networks, not to mention that any of these technologies is essentially subject to a compact physical or virtual form of a solution of an optimization problem.