Numerical evaluation of approximation methods in stochastic programming
نویسندگان
چکیده
منابع مشابه
Numerical Evaluation of Approximation Methods in Stochastic Programming
We study an approach for the evaluation of approximation and solution methods for multistage linear stochastic programs by measuring the performance of the obtained solutions on a set of out-of-sample scenarios. The main point of the approach is to restore the feasibility of solutions to an approximate problem along the out-of-sample scenarios. For this purpose, we consider and compare differen...
متن کاملPenalty methods with stochastic approximation for stochastic nonlinear programming
In this paper, we propose a class of penalty methods with stochastic approximation for solving stochastic nonlinear programming problems. We assume that only noisy gradients or function values of the objective function are available via calls to a stochastic first-order or zeroth-order oracle. In each iteration of the proposed methods, we minimize an exact penalty function which is nonsmooth an...
متن کاملApproximation in Stochastic Integer Programming
Approximation algorithms are the prevalent solution methods in the field of stochastic programming. Problems in this field are very hard to solve. Indeed, most of the research in this field has concentrated on designing solution methods that approximate the optimal solutions. However, efficiency in the complexity theoretical sense is usually not taken into account. Quality statements mostly rem...
متن کاملApproximation of Stochastic Programming Problems
In Stochastic Programming, the aim is often the optimization of a criterion function that can be written as an integral or mean functional with respect to a probability measure P. When this functional cannot be computed in closed form, it is customary to approximate it through an empirical mean functional based on a random Monte Carlo sample. Several improved methods have been proposed, using q...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Optimization
سال: 2010
ISSN: 0233-1934,1029-4945
DOI: 10.1080/02331931003700756