The wide applicability of chance–constrained programming, together with advances in convex optimization and probability theory, has created a surge of interest in finding efficient methods for processing chance constraints in recent years. One of the successes is the development of so–called safe tractable approximations of chance–constrained programs, where a chance constraint is replaced by a...

Chemical process optimization and control are affected by (1) plant-model mismatch, (2) disturbances, (3) constraints for safe operation. Reinforcement learning policy would be a natural way to solve this due its ability address stochasticity, directly account the effect of future uncertainty feedback in proper closed-loop manner; all without need an inner loop. One main reasons why reinforceme...

Properties of phonological systems may derive from both comprehension and production constraints. In this study, we test the extent to which general purpose constraints from sequence production are manifested in repetitions of phonemes within words. We find that near repetitions of phonemes occur less than expected by chance within the vocabularies of four studied languages: Dutch, English, Fre...

Optimization problems face random constraint violations when uncertainty arises in constraint parameters. Effective ways of controlling such violations include risk constraints, e.g., chance constraints and conditional Value-at-Risk (CVaR) constraints. This paper studies these two types of risk constraints when the probability distribution of the uncertain parameters is ambiguous. In particular...

In this paper we aim at output analysis with respect to changes of the probability distribution for problems with probabilistic (chance) constraints. The perturbations are modeled via contamination of the initial probability distribution. Dependence of the set of solutions on the probability distribution rules out the straightforward construction of the convexity-based global contamination boun...

We extend the theory of penalty functions to stochastic programming problems with nonlinear inequality constraints dependent on a random vector with known distribution. We show that the problems with penalty objective, penalty constraints and chance constraints are asymptotically equivalent under discretely distributed random parts. This is a complementary result to Branda (2012a), Branda and D...

Many CSPs can be effectively represented and efficiently solved using matrix models, in which the matrices may have symmetry between their rows and/or columns. Eliminating all such symmetry can be very costly as there are in general exponentially many symmetries. Cost-effective methods have been proposed to break much of the symmetry, if not all. In this paper, we continue with this line of res...