There are quite a number of different metrics of network robustness. This paper addresses the rationality of four metrics of network robustness (the algebraic connectivity, the effective resistance, the average edge betweenness, and the efficiency) by investigating the robust growth of generalized meshes (GMs). First, a heuristic growth algorithm (the Proximity-Growth algorithm) is proposed. Th...

Interference is an important parameter that affects the performance of a network. By reducing interference, the capacity of the network is improved and this leads to an increased efficiency in communication. In this paper we analyze the interference of a robust channel assignment algorithm for multi-radio networks. This study is an extension to a previous work in which a channel assignment algo...

this paper proposes a robust portfolio optimization programming model with options. Under constrains of variance efficiency and shortfall preference structure, we derive optioned portfolios with the maximum expected return of robust counterpart. A numerical example using Monte Carlo illustrates some of the features and applications of this model.

proposing a robust designed facility location is one of the most effective ways to hedge against unexpected disruptions and failures in a transportation network system. this paper considers the combined facility location/network design problem with regard to transportation link disruptions and develops a mixed integer linear programming formulation to model it. with respect to the probability o...

A robust procedure is proposed, starting from least trimmed squares as the initial estimator. The asymptotic distribution of the two-step and multi-step estimators is derived. This allows the use with a pre-specified efficiency under normality. It is argued that the good performance, together with the simplicity of the procedure, should make this the robust estimator of choice for applied work.

The availability of massive volumes of data and recent advances in data collection and processing platforms have motivated the development of distributed machine learning algorithms. In numerous real-world applications large datasets are inevitably noisy and contain outliers. These outliers can dramatically degrade the performance of standard machine learning approaches such as regression trees...

This paper introduces a method for solving multi-objective optimization problems in uncertain environment. When the uncertainty factors of the optimization problem can be included into the mathematical model, through bounded intervals, [I]RMOA (Interval Robust Multi-objective Algorithm) can find an enclosure of the robust Pareto frontier. In this approach, the robust Pareto solutions are the on...