Titlebook: Discrete-Time Markov Control Processes; Basic Optimality Cri Onésimo Hernández-Lerma,Jean Bernard Lasserre Book 1996 Springer Science+Busin

GLUT

https://doi.org/10.1007/978-981-15-3473-7 principle applicable to almost any class of OCPs, deterministic or stochastic, in discrete or continuous time, constrained or unconstrained, with finite or infinite optimization horizon—some references are given in §6.6. The preferred techniques, on the other hand, include the Lagrange multipliers

花爭吵

Niranji Satanarachchi,Takashi MinoIn this chapter, we consider the Markov control model.introduced in Definition 2.2.1, and the control problem we are interested in is to minimize the finite-horizon performance criterion.with ., the . function, a given measurable function on ..

ALE

Blatant

Obligatory

Long-Run Average-Cost Problems,In this chapter, we study the long-run expected average cost per unit-time criterion, hereafter abbreviated . or ., which is defined as follows.

Rustproof

遷移

https://doi.org/10.1007/978-1-4612-0729-0Markov property; linear optimization; management; model; operations research; production; programming; qual

空氣傳播

建筑師

Introduction and Summary,m’s variables, which are called .—or . or .—.. The controls that can be applied at any given time are chosen according to “rules” known as .. In addition, we are given a function called a . (or .), defined on the set of control policies, which measures or evaluates in some sense the system’s respons

aspect

Markov Control Processes,e are interested. An informal discussion of the main concepts, namely, Markov control models, control policies, and Markov control processes (MCPs), was already presented in §1.2. Their meaning is made precise in this chapter.