in a robust parameter design (rpd) problem, the experimenter is interested to determine the values of con-trol factors such that responses will be robust or insensitive to variability of the noise factors. response sur-face methodology (rsm) is one of the effective methods that can be employed for this purpose. since quality of products or processes is usually evaluated through several quality ...

In this paper, the optimizations problems to seek robust solutions under uncertainty are considered. The light robust approach is one of the strong and new methods to achieve robust solutions under conditions of uncertainty. In this paper, we tried to improve the quality of the solutions obtained from the Light Robust method by introducing a revised approach. Considering the problem concerned, ...

Regularized estimators in the context of group variables have been applied successfully in model and feature selection in order to preserve interpretability. We formulate a Distributionally Robust Optimization (DRO) problem which recovers popular estimators, such as Group Square Root Lasso (GSRL). Our DRO formulation allows us to interpret GSRL as a game, in which we learn a regression paramete...

Submodular functions have applications throughout machine learning, but in many settings, we do not have direct access to the underlying function f . We focus on stochastic functions that are given as an expectation of functions over a distribution P . In practice, we often have only a limited set of samples fi from P . The standard approach indirectly optimizes f by maximizing the sum of fi. H...

The hub location–allocation problem under uncertainty is a real-world task arising in the areas such as public and freight transportation and telecommunication systems. In many applications, the demand is considered as inexact because of the forecasting inaccuracies or human’s unpredictability. This study addresses the robust uncapacitated multiple allocation hub location problem with a set of ...

In this paper we explain how to characterize the best approximation to any x in a Hilbert space X from the set C ∩ {x ∈ X : gi(x) ≤ 0, i = 1, 2, · · · ,m} in the face of data uncertainty in the convex constraints, gi(x) ≤ 0, i = 1, 2, · · · ,m, where C is a closed convex subset of X. Following the robust optimization approach, we establish Lagrange multiplier characterizations of the robust con...

The problems of non-normality or functional relationships between variables may often be simplified by an appropriate transformation. However, the evidence for transformations may sometimes depend crucially on one or a few observations. Therefore, the purpose of the paper is to develop a method that will not be influenced by potential outliers during the process of data transformations. The con...