The solution of large-scale optimization and control problems increasingly relies on advanced iterative methods capable of exploiting problem structure and modern computing architectures. In particular, parallel algorithms based on domain decomposition and Parallel-in-Time (PinT) strategies have emerged as powerful approaches to address the computational challenges posed by high-dimensional and possibly time-dependent systems. This mini-symposium (DD30) is devoted to recent developments in iterative methods for large-scale problems, with a special emphasis on parallel solvers, domain decomposition techniques, and PinT algorithms. Contributions include theoretical advances in convergence and scalability, as well as novel algorithmic frameworks that enable efficient use of distributed and high-performance computing environments. Applications span optimal control, PDE-constrained optimization, and large dynamical systems. The session aims to provide a platform for exchanging ideas between researchers working on numerical analysis, scientific computing, and control, high-lighting the interplay between algorithmic innovation and practical implementation. Particular attention is given to methods that achieve strong scalability, robustness, and efficiency in high performance computing settings.