403 search results - page 1 / 81 » Sampling Bounds for Stochastic Optimization |
LION
2009
Springer
13 years 11 months ago
2009
Springer
Beam-ACO algorithms are hybrid methods that combine the metaheuristic ant colony optimization with beam search. They heavily rely on accurate and computationally inexpensive boundi...
APPROX
2005
Springer
13 years 10 months ago
2005
Springer
A large class of stochastic optimization problems can be modeled as minimizing an objective function f that depends on a choice of a vector x ∈ X, as well as on a random external...
SIAMJO
2008
13 years 4 months ago
2008
We study approximations of optimization problems with probabilistic constraints in which the original distribution of the underlying random vector is replaced with an empirical dis...
MP
2006
13 years 4 months ago
2006
Determining if a solution is optimal or near optimal is fundamental in optimization theory, algorithms, and computation. For instance, Karush-Kuhn-Tucker conditions provide necessa...
SIAMJO
2002
13 years 4 months ago
2002
In this paper we study a Monte Carlo simulation based approach to stochastic discrete optimization problems. The basic idea of such methods is that a random sample is generated and...