The field of optimization and stability analysis is witnessing significant developments, with a focus on improving the efficiency and robustness of algorithms. Researchers are exploring new approaches to adapt to changing environments and function types, leading to the development of universal algorithms that can handle multiple types of convex functions simultaneously. Additionally, there is a growing interest in applying optimization techniques to real-world problems, such as power system voltage stability and nonlinear autonomous systems. Notable papers in this area include: Dual Adaptivity: Universal Algorithms for Minimizing the Adaptive Regret of Convex Functions, which proposes a meta-expert framework for dual adaptive algorithms. Learning to optimize with guarantees: a complete characterization of linearly convergent algorithms, which characterizes the class of algorithms that achieve linear convergence for classes of nonsmooth composite optimization problems.