Topic
Stochastic programming
About: Stochastic programming is a research topic. Over the lifetime, 12343 publications have been published within this topic receiving 421049 citations.
Papers published on a yearly basis
Papers
More filters
••
TL;DR: The main conclusion of this study is that for an oil producing country with oil processing capabilities, the impact of economic uncertainties may be tolerated by an appropriate balance between crude exports and processing capacities.
146 citations
••
TL;DR: The paper addresses a variety of high-dimensional Markov chain Monte Carlo methods as well as deterministic surrogate methods, such as variational Bayes, the Bethe approach, belief and expectation propagation and approximate message passing algorithms.
Abstract: Modern signal processing (SP) methods rely very heavily on probability and statistics to solve challenging SP problems. SP methods are now expected to deal with ever more complex models, requiring ever more sophisticated computational inference techniques. This has driven the development of statistical SP methods based on stochastic simulation and optimization. Stochastic simulation and optimization algorithms are computationally intensive tools for performing statistical inference in models that are analytically intractable and beyond the scope of deterministic inference methods. They have been recently successfully applied to many difficult problems involving complex statistical models and sophisticated (often Bayesian) statistical inference techniques. This survey paper offers an introduction to stochastic simulation and optimization methods in signal and image processing. The paper addresses a variety of high-dimensional Markov chain Monte Carlo (MCMC) methods as well as deterministic surrogate methods, such as variational Bayes, the Bethe approach, belief and expectation propagation and approximate message passing algorithms. It also discusses a range of optimization methods that have been adopted to solve stochastic problems, as well as stochastic methods for deterministic optimization. Subsequently, areas of overlap between simulation and optimization, in particular optimization-within-MCMC and MCMC-driven optimization are discussed.
146 citations
••
TL;DR: In this article, a new method for the optimal transmission system expansion planning based on chance constrained programming is presented with several uncertain factors such as the locations and capacities of new power plants as well as demand growth well taken into account.
145 citations
••
TL;DR: This paper forms the power and channel allocation problem as a mixed-integer programming problem under constraints as well as a discrete stochastic optimization method, which has low computational complexity and fast convergence to approximate to the optimal solution.
Abstract: Resources in cognitive radio networks (CRNs) should dynamically be allocated according to the sensed radio environment Although some work has been done for dynamic resource allocation in CRNs, many works assume that the radio environment can perfectly be sensed However, in practice, it is difficult for the secondary network to have the perfect knowledge of a dynamic radio environment in CRNs In this paper, we study the dynamic resource allocation problem for heterogeneous services in CRNs with imperfect channel sensing We formulate the power and channel allocation problem as a mixed-integer programming problem under constraints The computational complexity is enormous to solve the problem To reduce the computational complexity, we tackle this problem in two steps First, we solve the optimal power allocation problem using the Lagrangian dual method under the assumption of known channel allocation Next, we solve the joint power and channel allocation problem using the discrete stochastic optimization method, which has low computational complexity and fast convergence to approximate to the optimal solution Another advantage of this method is that it can track the changing radio environment to dynamically allocate the resources Simulation results are presented to demonstrate the effectiveness of the proposed scheme
145 citations
••
TL;DR: This work proposes the use of sequences of separable, piecewise linear approximations for solving nondifferentiable stochastic optimization problems, and proves the convergence of several versions of such methods when the objective function is separable and has integer break points.
Abstract: We propose the use of sequences of separable, piecewise linear approximations for solving nondifferentiable stochastic optimization problems. The approximations are constructed adaptively using a combination of stochastic subgradient information and possibly sample information on the objective function itself. We prove the convergence of several versions of such methods when the objective function is separable and has integer break points, and we illustrate their behavior on numerical examples. We then demonstrate the performance on nonseparable problems that arise in the context of two-stage stochastic programming problems, and demonstrate that these techniques provide near-optimal solutions with a very fast rate of convergence compared with other solution techniques.
145 citations