Stochastic programming has been applied to many domains and problems that involve uncertainty, such as production planning, portfolio optimization, energy management, and transportation and logistics.

Stochastic programming problems can be classified into three main types, depending on how scenarios are incorporated into the model. Two-stage problems feature two groups of decision variables ...

Traffic Matrices, which capture the network traffic volumes among end-points, are widely used in Internet Engineering. A traffic matrix is generally estimated from link loads because it is impossible ...

6. Conclusion. In this paper, we study the fast numerical methods for solving the stochastic linear complementarity problems. Firstly, we convert the expected value formulation of stochastic linear ...

Matrix equations, particularly those with structures such as Toeplitz or quasi-Toeplitz, emerge naturally when characterising the evolution and equilibrium properties of Markov models.

Nonstochastic Technological Matrix. Revue Roumaine de Mathématique Pures et Appliquées, 11, 713-725. has been cited by the following article: ... a closed form expression for the cumulative ...

We employ a stochastic programming approach to solve these problems, which has advantages over other methods and captures the underlying institutional structure of the sidecar. We detail the method ...