The theory of Markov decision processes focuses on controlled Markov chains in discrete
time. The authors establish the theory for general state and action spaces and at the same …

The paper provides an overview of the theory and applications of risk-sensitive Markov
decision processes. The term'risk-sensitive'refers here to the use of the Optimized Certainty …

We consider several multiperiod portfolio optimization models where the market consists of
a riskless asset and several risky assets. The returns in any period are random with a mean …

This paper deals with discrete-time Markov control processes on a general state space. A
long-run risk-sensitive average cost criterion is used as a performance measure. The one …

We consider constructing optimal strategies for risk-sensitive portfolio optimization problems
on an infinite time horizon for general factor models, where the mean returns and the …

We consider a multiperiod mean-variance model where the model parameters change
according to a stochastic market. The mean vector and covariance matrix of the random …

In this paper, we consider the optimal portfolio selection problem where the investor
maximizes the expected utility of the terminal wealth. The utility function belongs to the …

ABSTRACT A multiobjective, multidisciplinary design optimization methodology for
mathematically modeling the distributed satellite system (DSS) conceptual design problem …

OPTIMIZATION is defined as the process of achieving the most favorable system condition
on the basis of a metric or set of metrics. Within the past 50 years, different optimization …

We consider the optimal portfolio selection problem in a multiple period setting where the
investor maximizes the expected utility of the terminal wealth in a stochastic market. The …