Results 1  10
of
133,858
Stochastic Optimization Problems in Telecommunications
"... We survey dierent optimization problems under uncertainty which arise in telecommunications. Three levels of decisions are distinguished: design of structural elements of telecommunication networks, top level design of telecommunication networks and design of optimal policies of telecommunication en ..."
Abstract
 Add to MetaCart
We survey dierent optimization problems under uncertainty which arise in telecommunications. Three levels of decisions are distinguished: design of structural elements of telecommunication networks, top level design of telecommunication networks and design of optimal policies of telecommunication
Epiconvergence of relaxed stochastic optimization problem
, 2013
"... In this paper we consider the relaxation of a dynamic stochastic optimization problem where we replace a stochastic constraint for example an almost sure constraint by a conditional expectation constraint. We show an epiconvergence result relying on the Kudo convergence of σ−algebra and continuity ..."
Abstract
 Add to MetaCart
In this paper we consider the relaxation of a dynamic stochastic optimization problem where we replace a stochastic constraint for example an almost sure constraint by a conditional expectation constraint. We show an epiconvergence result relying on the Kudo convergence of σ
Statistical inference of stochastic optimization problems
, 2000
"... We discuss in this paper statistical inference of Monte Carlo simulation based approximations of stochastic optimization problems, where the #true" objective function, and probably some of the constraints, are estimated, typically by averaging a random sample. The classical maximum likelihoo ..."
Abstract

Cited by 11 (1 self)
 Add to MetaCart
We discuss in this paper statistical inference of Monte Carlo simulation based approximations of stochastic optimization problems, where the #true" objective function, and probably some of the constraints, are estimated, typically by averaging a random sample. The classical maximum
Stochastic optimization problems with nondifferentiable cost functionals
 J. OPTIM. THEORY APPL
, 1973
"... In this paper, we examine a class of stochastic optimization problems characterized by nondifferentiability of the objective function. It is shown that, in many cases, the expected value of the objective function is differentiable and, thus, the resulting optimization problem can be solved by using ..."
Abstract

Cited by 18 (0 self)
 Add to MetaCart
In this paper, we examine a class of stochastic optimization problems characterized by nondifferentiability of the objective function. It is shown that, in many cases, the expected value of the objective function is differentiable and, thus, the resulting optimization problem can be solved by using
Boosted sampling: Approximation algorithms for stochastic optimization problems
 IN: 36TH STOC
, 2004
"... Several combinatorial optimization problems choose elements to minimize the total cost of constructing a feasible solution that satisfies requirements of clients. In the STEINER TREE problem, for example, edges must be chosen to connect terminals (clients); in VERTEX COVER, vertices must be chosen t ..."
Abstract

Cited by 98 (23 self)
 Add to MetaCart
Several combinatorial optimization problems choose elements to minimize the total cost of constructing a feasible solution that satisfies requirements of clients. In the STEINER TREE problem, for example, edges must be chosen to connect terminals (clients); in VERTEX COVER, vertices must be chosen
Hedging uncertainty: Approximation algorithms for stochastic optimization problems
 In Proceedings of the 10th International Conference on Integer Programming and Combinatorial Optimization
, 2004
"... We initiate the design of approximation algorithms for stochastic combinatorial optimization problems; we formulate the problems in the framework of twostage stochastic optimization, and provide nearly tight approximation algorithms. Our problems range from the simple (shortest path, vertex cover, ..."
Abstract

Cited by 77 (13 self)
 Add to MetaCart
We initiate the design of approximation algorithms for stochastic combinatorial optimization problems; we formulate the problems in the framework of twostage stochastic optimization, and provide nearly tight approximation algorithms. Our problems range from the simple (shortest path, vertex cover
A New Approach for Stochastic Optimization Problems
"... Stochastic optimization problems have long been difficult because of two basic insurmountable limits: the 1= p N convergence limit for value accuracy, and the NPHard limitation due to combinatorial explosion of search space. We discuss a new approach, called Ordinal Optimization, to circumvent the ..."
Abstract
 Add to MetaCart
Stochastic optimization problems have long been difficult because of two basic insurmountable limits: the 1= p N convergence limit for value accuracy, and the NPHard limitation due to combinatorial explosion of search space. We discuss a new approach, called Ordinal Optimization, to circumvent
ON THE SOLUTION OF STOCHASTIC OPTIMIZATION PROBLEMS IN IMPERFECT INFORMATION REGIMES
"... We consider the solution of a stochastic convex optimization problem E [ f (x;θ ∗,ξ)] in x over a closed and convex set X in a regime where θ ∗ is unavailable. Instead, θ ∗ may be learnt by minimizing a suitable metric E[g(θ;η)] in θ over a closed and convex set Θ. We present a coupled stochastic ap ..."
Abstract
 Add to MetaCart
We consider the solution of a stochastic convex optimization problem E [ f (x;θ ∗,ξ)] in x over a closed and convex set X in a regime where θ ∗ is unavailable. Instead, θ ∗ may be learnt by minimizing a suitable metric E[g(θ;η)] in θ over a closed and convex set Θ. We present a coupled stochastic
On the Undecidability of Probabilistic Planning and Related Stochastic Optimization Problems
 Artificial Intelligence
, 2003
"... Automated planning, the problem of how an agent achieves a goal given a repertoire of actions, is one of the foundational and most widely studied problems in the AI literature. The original formulation of the problem makes strong assumptions regarding the agent's knowledge and control over the ..."
Abstract

Cited by 74 (0 self)
 Add to MetaCart
Automated planning, the problem of how an agent achieves a goal given a repertoire of actions, is one of the foundational and most widely studied problems in the AI literature. The original formulation of the problem makes strong assumptions regarding the agent's knowledge and control over
Results 1  10
of
133,858