This paper is the continuation of the paper entitled “Hereditary portfolio optimization with taxes and fixed plus proportional transaction costs I” that treats an infinite-time horizon hereditary portfolio optimization problem in a market that consists of one savings account and one stock account. Within the solvency region, the investor is allowed to consume from the savings account and can ma...

This paper discusses multi-period stochastic joint inventory and pricing models when the decision maker is risk and ambiguity averse. We study infinite horizon models with discounted and long run average optimization criteria. The main result of this paper is to establish the optimality of stationary (s, S, p) policies for the infinite horizon inventory and pricing models, for which the existin...

We present a decomposition approach for integrated assessment modeling of climate policy based on a linear approximation of the climate system. Our objective is to demonstrate the usefulness of decomposition for integrated assessment models posed in a complementarity format. First, the complementarity formulation cum decomposition permits a precise representation of post-terminal damages thereb...

In this thesis we study receding horizon control strategies for stochastic discrete-time linear systems with additive noise. This study is divided into two parts: First, we compare the performance of different receding horizon control techniques under constrained and unconstrained control input settings for some representative systems. It appears from our simulations that while in an unconstrai...

In the past decade the field of nonlinear model predictive control (NMPC) has witnessed steadily increasing attention from control practitioners. Its popularity comes from the fact that today’s processes need to be operated under much tighter performance specifications while at the same time more and more constraints, stemming for example from environmental and safety considerations, need to be...

In this paper we study a Markov decision process with a non-linear discount function. Our approach is in spirit of the von Neumann-Morgenstern concept and is based on the notion of expectation. First, we define a utility on the space of trajectories of the process in the finite and infinite time horizon and then take their expected values. It turns out that the associated optimization problem l...

Decentralized partially observable Markov decision processes (DEC-POMDPs) are used to plan policies for multiple agents that must maximize a joint reward function but do not communicate with each other. The agents act under uncertainty about each other and the environment. This planning task arises in optimization of wireless networks, and other scenarios where communication between agents is r...