Bounding separable recourse functions with limited distribution information
نویسندگان
چکیده
منابع مشابه
Newsvendor optimization with limited distribution information
We report preliminary results on stochastic optimization with limited distributional information. Lack of complete distribution calls for stochastically robust models that, after exploiting available limited or partial information, offer risk-shielded solutions, i.e., solutions that are insensitive to all possible distributions of random variables. We focus on the well-known newsvendor problem ...
متن کاملA tighter variant of Jensen's lower bound for stochastic programs and separable approximations to recourse functions
In this paper, we propose a new method to compute lower bounds on the optimal objective value of a stochastic program and show how this method can be used to construct separable approximations to the recourse functions. We show that our method yields tighter lower bounds than Jensen’s lower bound and it requires a reasonable amount of computational effort even for large problems. The fundamenta...
متن کاملBounding Duality Gap for Problems with Separable Objective
We consider the problem of minimizing a sum of non-convex functions over a compact domain, subject to linear inequality and equality constraints. Approximate solutions can be found by solving a convexified version of the problem, in which each function in the objective is replaced by its convex envelope. We propose a randomized algorithm to solve the convexified problem which finds an -suboptim...
متن کاملBounding the Duality Gap for Problems with Separable Objective
We consider the problem of minimizing a sum of non-convex functions over a compact domain, subject to linear inequality and equality constraints. We consider approximate solutions obtained by solving a convexified problem, in which each function in the objective is replaced by its convex envelope. We propose a randomized algorithm to solve the convexified problem which finds an -suboptimal solu...
متن کاملBounding duality gap for separable problems with linear constraints
We consider the problem of minimizing a sum of non-convex functions over a compact domain, subject to linear inequality and equality constraints. Approximate solutions can be found by solving a convexified version of the problem, in which each function in the objective is replaced by its convex envelope. We propose a randomized algorithm to solve the convexified problem which finds an ǫ-subopti...
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: Annals of Operations Research
سال: 1991
ISSN: 0254-5330,1572-9338
DOI: 10.1007/bf02204821