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 …
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 …
Statistics of robust optimization: A generalized empirical likelihood approach
We study statistical inference and distributionally robust solution methods for stochastic
optimization problems, focusing on confidence intervals for optimal values and solutions that …
optimization problems, focusing on confidence intervals for optimal values and solutions that …
[LIVRE][B] Variational analysis
RT Rockafellar, RJB Wets - 2009 - books.google.com
From its origins in the minimization of integral functionals, the notion of'variations' has
evolved greatly in connection with applications in optimization, equilibrium, and control. It …
evolved greatly in connection with applications in optimization, equilibrium, and control. It …
[LIVRE][B] Lectures on stochastic programming: modeling and theory
This is a substantial revision of the previous edition with added new material. The
presentation of Chapter 6 is updated. In particular the Interchangeability Principle for risk …
presentation of Chapter 6 is updated. In particular the Interchangeability Principle for risk …
[LIVRE][B] Introduction to stochastic programming
JR Birge, F Louveaux - 2011 - books.google.com
The aim of stochastic programming is to find optimal decisions in problems which involve
uncertain data. This field is currently develo** rapidly with contributions from many …
uncertain data. This field is currently develo** rapidly with contributions from many …
[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 …
Monte Carlo bounding techniques for determining solution quality in stochastic programs
A stochastic program SP with solution value z∗ can be approximately solved by sampling n
realizations of the program's stochastic parameters, and by solving the resulting …
realizations of the program's stochastic parameters, and by solving the resulting …
[LIVRE][B] Modern nonconvex nondifferentiable optimization
Mathematical optimization has always been at the heart of engineering, statistics, and
economics. In these applied domains, optimization concepts and methods have often been …
economics. In these applied domains, optimization concepts and methods have often been …
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 …