In this paper, we present a probabilistic numerical algorithm combining dynamic
programming, Monte Carlo simulations, and local basis regressions to solve nonstationary …

We develop a complete analysis of a general entry–exit–scrapping model. In particular, we
consider an investment project that operates within a random environment and yields a …

In this paper, we formulate and study an optimal switching problem under partial information.
In our model, the agent/manager/investor attempts to maximize the expected reward by …

We address a general optimal switching problem over finite horizon for a stochastic system
described by a differential equation driven by Brownian motion. The main novelty is the fact …

Abstract The Least-Squares Monte Carlo Method (LSM) has become the standard tool to
solve real options modeled as an optimal switching problem. The method has been shown …

Abstract The Autonomic Power System (APS) grand challenge was a multi-disciplinary
EPSRC-funded research project that examined novel techniques that would enable the …

This document is a synthesis of the research that I have been conducting, along with my co–
authors, since the defense of my PhD thesis. This includes several topics, which are …

In this paper we study a discrete-time optimal switching problem on a finite horizon. The
underlying model has a running reward, terminal reward, and signed (positive and negative) …

In this paper, we present a probabilistic numerical algorithm combining dynamic
programming, Monte Carlo simulations and local basis regressions to solve non-stationary …

The value of flexibility in operations for hydroelectric power plants is apparent from their
investment in multiple generators to allow variable intensity of operation. This flexibility …