Introduction to Stochastic Dynamic Programming presents the basic theory and examines the scope of applications of stochastic dynamic programming. The book begins with a chapter on various finite-stage models, illustrating the wide range of applications of stochastic dynamic programming. Subsequent chapters study infinite-stage models: discounting future returns, minimizing nonnegative costs, maximizing nonnegative returns, and maximizing the long-run average return. Each of these chapters first considers whether an optimal policy need exist-providing counterexamples where appropriate-and th
The course covers a variety of topics in stochastic optimization. To begin with, some ap-proaches to...
This paper addresses the optimality of stochastic control strategies based on the infinite horizon a...
International audienceIn a stochastic controlled dynamical system, stochastic dynamic programming is...
This book explores discrete-time dynamic optimization and provides a detailed introduction to both d...
Title: Stochastic Dynamic Programming Problems: Theory and Applications Author: Gabriel Lendel Depar...
This course covers the basic models and solution techniques for problems of sequential decision maki...
This text gives a comprehensive coverage of how optimization problems involving decisions and uncert...
AbstractA sequential decision model is developed in the context of which three principles of optimal...
2017-07-19Dynamic programming has become a common method in practice in solving optimization problem...
Summary. In this paper it is demonstrated how necessary and sufficient conditions for optimality of ...
This paper develops a new method for constructing approximate solutions to discrete time, infinite h...
We consider a class of multistage stochastic linear programs in which at each stage a coherent risk ...
This dissertation analysis a monopoly firm model by use of dynamic programming. A general result is ...
This paper explores sufficient conditions for a continuous stationary Markov optimal policy and a co...
Dynamic programming is a mathematical technique which provides a systematic procedure for determinin...
The course covers a variety of topics in stochastic optimization. To begin with, some ap-proaches to...
This paper addresses the optimality of stochastic control strategies based on the infinite horizon a...
International audienceIn a stochastic controlled dynamical system, stochastic dynamic programming is...
This book explores discrete-time dynamic optimization and provides a detailed introduction to both d...
Title: Stochastic Dynamic Programming Problems: Theory and Applications Author: Gabriel Lendel Depar...
This course covers the basic models and solution techniques for problems of sequential decision maki...
This text gives a comprehensive coverage of how optimization problems involving decisions and uncert...
AbstractA sequential decision model is developed in the context of which three principles of optimal...
2017-07-19Dynamic programming has become a common method in practice in solving optimization problem...
Summary. In this paper it is demonstrated how necessary and sufficient conditions for optimality of ...
This paper develops a new method for constructing approximate solutions to discrete time, infinite h...
We consider a class of multistage stochastic linear programs in which at each stage a coherent risk ...
This dissertation analysis a monopoly firm model by use of dynamic programming. A general result is ...
This paper explores sufficient conditions for a continuous stationary Markov optimal policy and a co...
Dynamic programming is a mathematical technique which provides a systematic procedure for determinin...
The course covers a variety of topics in stochastic optimization. To begin with, some ap-proaches to...
This paper addresses the optimality of stochastic control strategies based on the infinite horizon a...
International audienceIn a stochastic controlled dynamical system, stochastic dynamic programming is...