Chance-constrained programming (CCP) is one of the most difficult classes of optimization
problems that has attracted the attention of researchers since the 1950s. In this survey, we …

Nonlinear model predictive control (NMPC) is one of the few control methods that can
handle multivariable nonlinear control systems with constraints. Gaussian processes (GPs) …

We introduce a new method for solving nonlinear continuous optimization problems with
chance constraints. Our method is based on a reformulation of the probabilistic constraint as …

Chance-constrained programming (CCP) is one of the most difficult classes of optimization
problems that has attracted the attention of researchers since the 1950s. In this survey, we …

In this paper, we consider chance constrained optimization of elliptic partial differential
equation (CCPDE) systems with random parameters and constrained state variables. We …

Chance constraints provide a principled framework to mitigate the risk of high-impact
extreme events by modifying the controllable properties of a system. The low probability and …

We consider nonlinear optimization problems with cardinality constraints. Based on a
continuous reformulation, we introduce second-order necessary and sufficient optimality …

We consider stochastic programs with joint chance constraints with discrete random
distribution. We reformulate the problem by adding auxiliary variables. Since the resulting …

We propose a stochastic approximation method for approximating the efficient frontier of
chance-constrained nonlinear programs. Our approach is based on a bi-objective viewpoint …

In this paper, we consider the maximizing of the probability P ζ∣ ζ∈ K (x) over a closed and
convex set X, a special case of the chance-constrained optimization problem. Suppose K …