This paper presents an investigation on the computational complexity of stochastic optimization problems. We discuss a scenariobased model which captures the important classes of two-stage stochastic combinatorial optimization, two-stage stochastic linear programming, and two-stage stochastic integer linear programming. This model can also be used to handle chance constraints, which are used in...

In this paper, we investigate the routing optimization problem in wireless mesh networks. While existing works usually assume static and known traffic demand, we emphasize that the actual traffic is time-varying and difficult to measure. In light of this, we alternatively pursue a stochastic optimization framework where the expected network utility is maximized. For multi-path routing scenario,...

This paper addresses the class of nonlinear mixed integer stochastic programming problems. In particular, we consider two-stage problems with nonlinearities both in the objective function and constraints, pure integer first stage and mixed integer second stage variables. We exploit the specific problem structure to develop a global optimization algorithm. The basic idea is to decompose the orig...

We consider the problem of finding the minimizer of a function f : R → R of the form min f(w) = 1 n ∑ i fi(w). This problem has been studied intensively in recent years in machine learning research field. One typical but promising approach for large-scale data is stochastic optimization algorithm. SGDLibrary is a flexible, extensible and efficient pure-Matlab library of a collection of stochast...

In this paper, we consider a stochastic programming approach to multi-stage posttax portfolio optimization. Asset performance information is speci ed as a scenario tree generated by two alternative methods based on simulation and optimization. We assume three tax wrappers involving the same instruments for an eÆcient investment strategy and determine optimal allocations to di erent instruments ...

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 the expected value function is approximated by the corresponding sample average function. The obtained sample average optimization problem is solved, and the procedure is repeated several times until a stopping crite...

A probabilistic framework is proposed for the optimization of efficient switched control strategies for physical systems dominated by stochastic excitation. In this framework, the equation for the state trajectory is replaced with an equivalent equation for its probability distribution function in the constrained optimization setting. This allows for a large class of control rules to be conside...

This paper combines the analysis of convergence of maximum likelihood estimators with the analysis of the convergence of stochastic optimization algorithms, e.g. threshold accepting, to the theoretical estimator. We discuss the joint convergence of estimator and algorithm in a formal framework. An application to a GARCH model demonstrates the approach in practice by estimating actual rates of c...

1. Introduction Many important real-world problems contain stochastic elements and require optimization. Stochastic programming and simulation-based optimization are two approaches used to address this issue. We do not explicitly discuss other related areas including stochastic control, stochas-tic dynamic programming, and Markov decision processes. We consider a stochastic optimization problem...

Shape from shading is an ill-posed inverse problem for which there is no completely satisfactory solution in the existing literature. In this paper, we address shape from shading as an energy minimization problem. We first show that the deterministic approach provides efficient algorithms in terms of CPU time, but reaches its limits since the energy associated with shape from shading can contai...