This paper deals with approximation schemes for infinite horizon, discrete time, stochastic optimization problems. We construct finite horizon approximates that yield upper and lower estimates and whose optimal solutions converge to long-term optimal trajectories. The results extend those of [3] from the deterministic case to the stochastic
In many dynamic stochastic optimization problems in practice, the uncertain factors are best modeled...
In many dynamic stochastic optimization problems in practice, the uncertain factors are best modeled...
In many dynamic stochastic optimization problems in practice, the uncertain factors are best modeled...
Models for long-term planning often lead to infinite horizon stochastic programs that offer signific...
Models for long-term planning often lead to infinite horizon stochastic pro-grams that offer signifi...
We show how infinite horizon stochastic optimal control problems can be solved via studying their fi...
We show how infinite horizon stochastic optimal control problems can be solved via studying their fi...
The paper develops efficient and general stochastic approximation (SA) methods for improving the ope...
The paper develops efficient and general stochastic approximation (SA) methods for improving the ope...
The paper develops efficient and general stochastic approximation (SA) methods for improving the ope...
. Dynamic optimization problems, including optimal control problems, have typically relied on the so...
Planning horizon is a key issue in production planning. Different from previous approaches based on ...
Discrete-time stochastic optimal control problems are stated over a finite number of decision stages...
We consider the stochastic infinite horizon optimization problem which seeks to minimize average cos...
In many dynamic stochastic optimization problems in practice, the uncertain factors are best modeled...
In many dynamic stochastic optimization problems in practice, the uncertain factors are best modeled...
In many dynamic stochastic optimization problems in practice, the uncertain factors are best modeled...
In many dynamic stochastic optimization problems in practice, the uncertain factors are best modeled...
Models for long-term planning often lead to infinite horizon stochastic programs that offer signific...
Models for long-term planning often lead to infinite horizon stochastic pro-grams that offer signifi...
We show how infinite horizon stochastic optimal control problems can be solved via studying their fi...
We show how infinite horizon stochastic optimal control problems can be solved via studying their fi...
The paper develops efficient and general stochastic approximation (SA) methods for improving the ope...
The paper develops efficient and general stochastic approximation (SA) methods for improving the ope...
The paper develops efficient and general stochastic approximation (SA) methods for improving the ope...
. Dynamic optimization problems, including optimal control problems, have typically relied on the so...
Planning horizon is a key issue in production planning. Different from previous approaches based on ...
Discrete-time stochastic optimal control problems are stated over a finite number of decision stages...
We consider the stochastic infinite horizon optimization problem which seeks to minimize average cos...
In many dynamic stochastic optimization problems in practice, the uncertain factors are best modeled...
In many dynamic stochastic optimization problems in practice, the uncertain factors are best modeled...
In many dynamic stochastic optimization problems in practice, the uncertain factors are best modeled...
In many dynamic stochastic optimization problems in practice, the uncertain factors are best modeled...
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