Stochastic programming models static or dynamic optimal decision making under uncertainty. In contrast to deterministic mathematical programming, stochastic programming generally uses expectation functionals in objective or constraints over known or partially known distributions of the problem data. Its features many decision variables under complicated constraints over discrete time periods. Its...
At the heart of nonlinear optimization methods lies the solution of linear systems of equations. As the size of the problem increases, it is imperative to use iterative methods, such as the conjugate gradient algorithm, to solve these linear systems. In the context of constrained optimization, it has proved to...