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Let X1, X2, ... be a discrete-time stochastic process with a distribution Pθ, θ ∈ Θ, where Θ

modeling and simulation, which allow to optimize the design and process parameters of rotary furnaces.

utilized different decomposition principles. The known decomposition methods in discrete optimization

methods for the constructed discrete optimal control problems are proved. The rate of convergence

the underlying nonconvex optimization problem associated with nonnegative CPD. Additionally, a discrete

is implemented as a discrete medium which is a stack of figured layers of certain geometry, similar to flower

For a triple of Hilbert spaces {V, H, V*}, we study a discrete and a semidiscrete scheme

constraints. A discrete optimal control problem is approximated by a minimization problem with penalized state

Let X1, X2, ... be a discrete-time stochastic process with a distribution Pθ, θ ∈ Θ, where Θ

Optimal control problems are applied to a variety of dynamical systems with a random law of motion

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