Monte Carlo sampling methods
A Shapiro - Handbooks in operations research and management …, 2003 - Elsevier
In this chapter we discuss Monte Carlo sampling methods for solving large scale stochastic
programming problems. We concentrate on the “exterior” approach where a random sample …
programming problems. We concentrate on the “exterior” approach where a random sample …
Monte Carlo sampling-based methods for stochastic optimization
This paper surveys the use of Monte Carlo sampling-based methods for stochastic
optimization problems. Such methods are required when—as it often happens in practice …
optimization problems. Such methods are required when—as it often happens in practice …
[LIVRE][B] The cross-entropy method: a unified approach to combinatorial optimization, Monte-Carlo simulation, and machine learning
RY Rubinstein, DP Kroese - 2004 - Springer
This book is a comprehensive and accessible introduction to the cross-entropy (CE) method.
The CE method started life around 1997 when the first author proposed an adaptive …
The CE method started life around 1997 when the first author proposed an adaptive …
[LIVRE][B] Theory of random sets
I Molchanov, IS Molchanov - 2005 - Springer
Stochastic geometry is a relatively new branch of mathematics. Although its predecessors
such as geometric probability date back to the 18th century, the formal concept of a random …
such as geometric probability date back to the 18th century, the formal concept of a random …
International Series in Operations Research & Management Science
FS Hillier, CC Price - 2005 - Springer
The beginning of stochastic programming, and in particular stochastic linear programming
(SLP), dates back to the 50's and early 60's of the last century. Pioneers who—at that time …
(SLP), dates back to the 50's and early 60's of the last century. Pioneers who—at that time …
On the rate of convergence of optimal solutions of Monte Carlo approximations of stochastic programs
In this paper we discuss Monte Carlo simulation based approximations of astochastic
programming problem. We show that if the corresponding random functions are convex …
programming problem. We show that if the corresponding random functions are convex …
Stochastic improvement of cyclic railway timetables
Real-time railway operations are subject to stochastic disturbances. Thus a timetable should
be designed in such a way that it can cope with these disturbances as well as possible. For …
be designed in such a way that it can cope with these disturbances as well as possible. For …
Stability of stochastic programming problems
W Römisch - Handbooks in operations research and management …, 2003 - Elsevier
The behaviour of stochastic programming problems is studied in case of the underlying
probability distribution being perturbed and approximated, respectively. Most of the …
probability distribution being perturbed and approximated, respectively. Most of the …
A stochastic model for the integrated optimization on metro timetable and speed profile with uncertain train mass
The integrated timetable and speed profile optimization model has recently attracted more
attention because of its good achievements on energy conservation in metro systems …
attention because of its good achievements on energy conservation in metro systems …
Quantitative stability in stochastic programming: The method of probability metrics
Quantitative stability of optimal values and solution sets to stochastic programming problems
is studied when the underlying probability distribution varies in some metric space of …
is studied when the underlying probability distribution varies in some metric space of …