Despite convex mathematical optimization methods be applied to general linear optimization problems it can also be applied in a special class of nonlinear optimization. As ambiguity and vagueness are natural and ever-present in real-life situations requiring solutions, it makes perfect sense to attempt to address them using fuzzy convex optimization. In this work, two fuzzy convex approaches ba...

This paper proposes a constrained stochastic successive convex approximation (CSSCA) algorithm to find a stationary point for a general non-convex stochastic optimization problem, whose objective and constraint functions are nonconvex and involve expectations over random states. The existing methods for non-convex stochastic optimization, such as the stochastic (average) gradient and stochastic...

Several important problems in control theory can be reformulated as convex optimization problems. From duality theory in convex optimization, dual problems can be derived for these convex optimization problems. These dual problems can in turn be reinter-preted in control or system theoretic terms, often yielding new results or new proofs for existing results from control theory. Moreover, the m...