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A review of stochastic programming methods for optimization of process systems under uncertainty
Uncertainties are widespread in the optimization of process systems, such as uncertainties
in process technologies, prices, and customer demands. In this paper, we review the basic …
in process technologies, prices, and customer demands. In this paper, we review the basic …
Risk-averse autonomous systems: A brief history and recent developments from the perspective of optimal control
We present an historical overview about the connections between the analysis of risk and
the control of autonomous systems. We offer two main contributions. Our first contribution is …
the control of autonomous systems. We offer two main contributions. Our first contribution is …
Risk-sensitive and robust decision-making: a cvar optimization approach
In this paper we address the problem of decision making within a Markov decision process
(MDP) framework where risk and modeling errors are taken into account. Our approach is to …
(MDP) framework where risk and modeling errors are taken into account. Our approach is to …
[کتاب][B] Multistage stochastic optimization
The topic of this book is multistage stochastic optimization. Multistage reflects the fact that an
optimal decision is an entire strategy or policy, which is executed during subsequent instants …
optimal decision is an entire strategy or policy, which is executed during subsequent instants …
Risk aversion in multistage stochastic programming: A modeling and algorithmic perspective
We discuss the incorporation of risk measures into multistage stochastic programs. While
much attention has been recently devoted in the literature to this type of model, it appears …
much attention has been recently devoted in the literature to this type of model, it appears …
Entropic risk optimization in discounted MDPs
Abstract Risk-averse Markov Decision Processes (MDPs) have optimal policies that achieve
high returns with low variability, but these MDPs are often difficult to solve. Only a few …
high returns with low variability, but these MDPs are often difficult to solve. Only a few …
Risk-sensitive safety analysis using conditional value-at-risk
This article develops a safetyanalysis method for stochastic systems that is sensitive to the
possibility and severity of rare harmful outcomes. We define risk-sensitive safe sets as …
possibility and severity of rare harmful outcomes. We define risk-sensitive safe sets as …
Risk-averse bayes-adaptive reinforcement learning
In this work, we address risk-averse Bayes-adaptive reinforcement learning. We pose the
problem of optimising the conditional value at risk (CVaR) of the total return in Bayes …
problem of optimising the conditional value at risk (CVaR) of the total return in Bayes …
On dynamic programming decompositions of static risk measures in Markov decision processes
Optimizing static risk-averse objectives in Markov decision processes is difficult because
they do not admit standard dynamic programming equations common in Reinforcement …
they do not admit standard dynamic programming equations common in Reinforcement …
Constrained risk-averse Markov decision processes
We consider the problem of designing policies for Markov decision processes (MDPs) with
dynamic coherent risk objectives and constraints. We begin by formulating the problem in a …
dynamic coherent risk objectives and constraints. We begin by formulating the problem in a …