In this extended abstract we discuss bounding techniques for a collection of interacting simple QN and SAN models that we have been developing. Each simple model produces bounded output when presented with bounded inputs. A collection of bounded models can be used to construct larger models that have the same bounding properties. Additionally, the constructed model can be decomposed at the boun...

Presented are two related numerical methods, one for the inverse eigenvalue problem for nonnegative or stochastic matrices and another for the inverse eigenvalue problem for symmetric nonnegative matrices. The methods are iterative in nature and utilize alternating projection ideas. For the symmetric problem, the main computational component of each iteration is an eigenvalue-eigenvector decomp...

in the present paper, we shall attempt to make a contribution to approximate analytical evaluation of the harmonic decomposition of an arbitrary continuous function. the basic assumption is that the class of functions that we investigate here, except the verification of dirichlet's principles, is concurrently able to be expanded in taylor's representation, over a particular interval o...

We study a class of chance-constrained two-stage stochastic optimization problems where second-stage feasible recourse decisions incur additional cost. In addition, we propose a new model, where “recovery” decisions are made for the infeasible scenarios to obtain feasible solutions to a relaxed second-stage problem. We develop decomposition algorithms with specialized optimality and feasibility...

Benders' decomposition is a well-known technique for solving large linear programs with a special structure. In particular it is a popular technique for solving multi-stage stochastic linear programming problems. Early termination in the subproblems generated during Benders' decomposition (assuming dual feasibility) produces valid cuts which are inexact in the sense that they are not as constra...

Handling uncertainty in natural inflow is an important part of a hydroelectric scheduling model. In a stochastic programming formulation, natural inflow may be modeled as a random vector with known distribution, but the size of the resulting mathematical program can be formidable. Decomposition-based algorithms take advantage of special structure and provide an attractive approach to such probl...

The synthesis of stochastic finite-state automata is possible using two kinds of components-deterministic finite-state automata and ergodic signal generators. The starting point in such a synthesis methods is a decomposition of stochastic matrix, its representation as a linear form of simple matrices which belong to the class of transition matrices of deterministic automata. New heuristic crite...

In this paper, we consider a stochastic realization problem with finite covariance data based on “LQ decomposition” in a Hilbert space, and re-derive a non-stationary finite-interval realization ([4, 5]). We develop a new algorithm of computing system matrices of the finiteinterval realization by LQ decomposition, followed by the SVD of a certain block matrix. Also, a stochastic subspace identi...

Adomian decomposition method (ADM) is applied to approximately solve stochastic fractional integrodifferential equations involving nonlocal initial condition. The convergence of the ADM for the considered problem is proved. The mean square error between approximate solution and accurate solution is also given. [Mahmoud M. El-Borai, M.A.Abdou, Mohamed Ibrahim M. Youssef. On Adomian’s Decompositi...