Editing Emerging Research Directions (section)

====Centralized Methods====
Here, we will discuss these techniques in more detail. Convex optimization is widely used and well-studied for its mathematical rigor and ability to support sub-optimal yet efficient solutions, such as through Lyapunov optimization for online decision-making. However, its practicality in real-world deployments is limited by the complexity of solving certain problems in parallel. Simpler methods like approximate algorithms—including Markov Decision Processes and k-means clustering—offer more flexibility and ease of implementation but often suffer from local optima and unreliable performance. Similarly, heuristic algorithms, often based on greedy strategies, provide quick and practical solutions but may also fail to reach the global optimum. Machine learning techniques, especially deep learning, scale effectively with hardware and can model complex non-linear patterns, yet they introduce challenges in explainability and require intensive training [4].