Computational Stochastic Control: Basic Foundations, Complexity and Techniques

Much research in control systems is purely mathematical, but advances in stochastic control problem solving can be used beyond the limits of where theoretical mathematics can help. Theoretical and computational mathematics are complementary. Computation is important where the problem is mathematically intractable, of high dimension as in stochastic dynamic programming or solving the problem is urgent as in competitive financial engineering predictions. Many advances in solving large scale control problems have been gained through technical improvements in computing hardware, but as many advances have been made in the development of new and better algorithms, the theoretical side of computation. Both analysis and computation are important in solving problems. Both rely on mathematics, but rely on them in different ways. An important part of educational training is general preparation for problem solving since the postgraduate job is uncertain in the current world