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Related papers: Risk sensitive optimal stopping

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This paper studies an optimal stopping problem for L\'evy processes. We give a justification of the form of the Snell envelope using standard results of optimal stopping. We also justify the convexity of the value function, and without a…

Probability · Mathematics 2008-12-18 Diana Dorobantu

We consider an optimal stopping time problem related with many models found in real options problems. The main goal of this work is to bring for the field of real options, different and more realistic pay-off functions, and negative…

Optimization and Control · Mathematics 2017-01-10 Manuel Guerra , Cláudia Nunes , Carlos Oliveira

For a discrete time Markov chain and in line with Strotz' consistent planning we develop a framework for problems of optimal stopping that are time-inconsistent due to the consideration of a non-linear function of an expected reward. We…

Optimization and Control · Mathematics 2020-01-23 Sören Christensen , Kristoffer Lindensjö

We study an optimal stopping problem when the state process is governed by a general Feller process. In particular, we examine viscosity properties of the associated value function with no a priori assumption on the stochastic differential…

Optimization and Control · Mathematics 2018-03-13 Suhang Dai , Olivier Menoukeu-Pamen

We present discrete-time approximation of optimal control policies for infinite horizon discounted/ergodic control problems for controlled diffusions in $\Rd$\,. In particular, our objective is to show near optimality of optimal policies…

Optimization and Control · Mathematics 2025-02-11 Somnath Pradhan , Serdar Yuksel

In this paper, we present an online reinforcement learning algorithm for constrained Markov decision processes with a safety constraint. Despite the necessary attention of the scientific community, considering stochastic stopping time, the…

Machine Learning · Computer Science 2024-03-26 Abhijit Mazumdar , Rafal Wisniewski , Manuela L. Bujorianu

We consider a change-point detection problem for a simple class of Piecewise Deterministic Markov Processes (PDMPs). A continuous-time PDMP is observed in discrete time and through noise, and the aim is to propose a numerical method to…

Optimization and Control · Mathematics 2017-09-28 Alice Cleynen , Benoîte de Saporta

We provide a characterization of an optimal stopping time for a class of finite horizon time-inconsistent optimal stopping problems (OSPs) of mean-field type, adapted to the Brownian filtration, including those related to mean-field…

Probability · Mathematics 2023-07-20 Boualem Djehiche , Mattia Martini

In this paper, which is a continuation of the previously published discrete time paper we develop a theory for continuous time stochastic control problems which, in various ways, are time inconsistent in the sense that they do not admit a…

Optimization and Control · Mathematics 2016-12-13 Tomas Björk , Mariana Khapko , Agatha Murgoci

This technical report is concerned with the convergence properties of what we call the split optimal policy iteration for coupled LQR problems; see section 3.1 in the manuscript. Interestingly, the iteration shows different convergence…

Optimization and Control · Mathematics 2014-04-22 Péter Koltai

In this paper we present a framework for risk-averse model predictive control (MPC) of linear systems affected by multiplicative uncertainty. Our key innovation is to consider time-consistent, dynamic risk metrics as objective functions to…

Optimization and Control · Mathematics 2015-11-24 Yin-Lam Chow , Marco Pavone

A classic reachability problem for safety of dynamic systems is to compute the set of initial states from which the state trajectory is guaranteed to stay inside a given constraint set over a given time horizon. In this paper, we leverage…

This work concerns controlled Markov chains with finite state and action spaces. The transition law satisfies the simultaneous Doeblin condition, and the performance of a control policy is measured by the (long-run) risk-sensitive average…

Probability · Mathematics 2007-05-23 Rolando Cavazos-Cadena , Daniel Hernandez-Hernandez

We consider the non-linear optimal multiple stopping problem under general conditions on the non-linear evaluation operators, which might depend on two time indices: the time of evaluation/assessment and the horizon (when the reward or loss…

Optimization and Control · Mathematics 2025-04-21 Miryana Grigorova , Marie-Claire Quenez , Peng Yuan

We investigate discrete-time mean-variance portfolio selection problems viewed as a Markov decision process. We transform the problems into a new model with deterministic transition function for which the Bellman optimality equation holds.…

Optimization and Control · Mathematics 2025-09-23 Nicole Bäuerle , Anna Jaśkiewicz

We study time-consistency questions for processes of monetary risk measures that depend on bounded discrete-time processes describing the evolution of financial values. The time horizon can be finite or infinite. We call a process of…

Probability · Mathematics 2008-12-10 Patrick Cheridito , Freddy Delbaen , Michael Kupper

We investigate an optimal stopping problem for the expected value of a discounted payoff on a regime-switching geometric Brownian motion under two constraints on the possible stopping times: only at exogenous random times and only during a…

Probability · Mathematics 2024-11-20 Takuji Arai , Masahiko Takenaka

For an infinite-horizon continuous-time optimal stopping problem under non-exponential discounting, we look for an optimal equilibrium, which generates larger values than any other equilibrium does on the entire state space. When the…

Optimization and Control · Mathematics 2021-07-15 Yu-Jui Huang , Zhou Zhou

The paper studies a class of multidimensional optimal stopping problems with infinite horizon for linear switching diffusions. There are two main novelties in the optimal problems considered: the underlying stochastic process has…

Probability · Mathematics 2021-08-02 Philip Ernst , Hongwei Mei

We present for the first time an asymptotic convergence analysis of two time-scale stochastic approximation driven by "controlled" Markov noise. In particular, the faster and slower recursions have non-additive controlled Markov noise…

Machine Learning · Computer Science 2020-12-03 Prasenjit Karmakar
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