We investigate constrained optimal control problems for linear stochastic dynamical systems evolving in discrete time. We consider minimization of an expected value cost subject to probabilistic constraints. We study the convexity of a finite-horizon optimization problem in the case where the control policies are affine functions of the disturbance input. We propose an expectation-based method ...

In the paper we consider the chance-constrained version of an affinely perturbed linear matrix inequality (LMI) constraint, assuming the primitive perturbations to be independent with light-tail distributions (e.g., bounded or Gaussian). Constraints of this type, playing a central role in chance-constrained linear/conic quadratic/semidefinite programming, are typically computationally intractab...

We describe a framework and an algorithm for solving hybrid influence diagrams with discrete, continuous, and deterministic chance variables, and discrete and continuous decision variables. A continuous chance variable in an influence diagram is said to be deterministic if its conditional distributions have zero variances. The solution algorithm is an extension of Shenoy’s fusion algorithm for ...

The water environment restoration project portfolio (WERP) selection is discussed in this paper. By complying with the analysis of project’s multidimensional property and operation mode, paper develops chance constraint management WERP from perspectives public service enterprise operation. In addition, multi-objective mixed integer linear programming model constructed by combining expectation m...

In this paper, we deal with a hydraulic reservoir optimization problem with uncertainty on inflows in a joint chance constrained programming setting. In particular, we will consider inflows with a persistency effect, following a causal time series model, and examine the impact of the ”Gaussian” assumption for such inflows. We present an iterative algorithm for solving similarly structured joint...

In this paper, a novel approach for multi response optimization is presented. In the proposed approach, response variables in treatments combination occur with a certain probability. Moreover, we assume that each treatment has a network style. Because of the probabilistic nature of treatment combination, the proposed approach can compute the efficiency of each treatment under the desirable reli...

We propose a method for learning constraints represented as Gaussian processes (GPs) from locally-optimal demonstrations. Our approach uses the Karush-Kuhn-Tucker (KKT) optimality conditions to determine where on demonstrations constraint is tight, and scaling of gradient at those states. then train GP representation which consistent with generalizes this information. further show that uncertai...

A large number of problems in production planning and scheduling, location, transportation, finance, and engineering design require that decisions be made in the presence of uncertainty. Uncertainty, for instance, governs the prices of fuels, the availability of electricity, and the demand for chemicals. A key difficulty in optimization under uncertainty is in dealing with an uncertainty space ...