We propose a randomized gradient method for the handling of a convex function whose gradient computation is demanding. The method bears a resemblance to the stochastic approximation family. But in contrast to stochastic ...

Several emerging applications, such as “Analytics of Things" and “Integrative Analytics" call for a fusion of statistical learning (SL) and stochastic optimization (SO). The Learning Enabled Optimization paradigm fuses ...

This paper develops an approximate method for solving multiperiod utility maximization investment models with downside risk control characterized by the minimum attainable wealth among all possible scenarios. The stochastic ...

This paper derives the mean-variance efficient frontier and optimal portfolio policies for a dynamic investment model. In the absence of arbitrage opportunities, the optimal expected portfolio value can be identified through ...

This paper addresses a general class of two-stage stochastic programs with integer recourse and discrete distributions. We exploit the structure of the value function of the second stage integer problem to develop a novel ...

In stochastic programming models we always face the problem of how to represent the random variables. This is particularly difficult with multidimensional distributions. We present an algorithm that produces a discrete ...

Because of incomplete information and also for the sake of numerical tractability one mostly solves an approximated stochastic program instead of the underlying ''true'' decision problem. However, without an additional ...

We consider two planning problems faced by an electricity distributor. Electricity can be obtained both from power plants and small generators such as hospitals and greenhouses, whereas the future demand for electricity ...

A two-stage stochastic programming model for the short- or mid-term cost-optimal electric power production planning is developed. We consider the power generation in a hydro-thermal generation system under uncertainty in ...

In this paper we consider stochastic programming problems where the objective function is given as an expected value function. With an optimal solution of such a (convex) problem we associate a condition number which ...