ABSTRACT. In adversarial environments, disabling the communication capabilities of the enemy is a high priority. We introduce the problem of determining the optimal number and locations for a set of jamming devices in order to neutralize a wireless communication network. This problem is known as the WIRELESS NETWORK JAMMING PROBLEM. We develop several mathematical programming formulations based on covering the communication nodes and limiting the connectivity index of the nodes. Two case studies are presented comparing the formulations with the addition of various percentile constraints. Finally, directions of further research are addressed.
ABSTRACT. This paper describes a problem of interdicting/jamming wireless communication networks in uncertain environments. Jamming communication networks is an important problem with many applications, but has received relatively little attention in the literature. Most of the work on network interdiction is focused on preventing jamming and analyzing network vulnerabilities. Here, we consider the case where there is no information about the network to be jammed. Thus, the problem is reduced to jamming all points in the area of interest. The optimal solution will determine the locations of the minimum number of jamming devices required to suppress the network. We consider a subproblem which places jamming devices on the nodes of a uniform grid over the area of interest. The objective here is to determine the maximum grid step size. We derive upper and lower bounds for this problem and provide a convergence result. Further, we prove that due to the cumulative effect of the jamming devices, the proposed method produces better solutions than the classical technique of covering the region with uniform circles.
This paper applies risk management methodologies to the optimization of a portfolio of hedge funds (fund of funds). We compare risk management techniques based on two recently developed risk measures, conditional value at risk (CVaR) and conditional drawdown at risk (CDaR) [12,35,36]. Both risk management techniques utilize stochastic programming approaches and allow for construction of linear portfolio rebalancing strategies and, as a result, have proven their high efficiency in various portfolio management applications [5, 12,27,35,36]. The choice of hedge funds, as a subject for the portfolio optimization strategy, was stimulated by a strong interest in this class of assets among both practitioners and scholars, as well as by challenges related to relatively small data sets available for hedge funds.Recent studies 1 of the hedge funds industry are mostly concentrated on the classification of hedge funds and the relevant investigation of their activity. However, this paper is focused on possible realization of investment opportunities existing in this market from the viewpoint of portfolio rebalancing strategies. (For an extensive discussion of stochastic programming approaches to hedge fund management, see [46].)Hedge funds are investment pools employing sophisticated trading and arbitrage techniques, including leverage and short selling, wide usage of derivative securities, etc. Generally, hedge funds restrict share ownership to high-net-worth individuals and institutions and are not allowed to offer their securities to the general public. Many hedge funds are limited to 99 investors. This private nature of hedge funds has resulted in few regulations and disclosure requirements, compared, for example, with mutual funds. (However, stricter
We propose the use of regression models as a tool to reduce time and cost associated with the development and selection of new metallic alloys. A multiple regression model is developed which can accurately predict tensile yield strength of high strength low alloy steel based on its chemical composition and processing parameters. Quantile regression is used to model the fracture toughness response as measured by Charpy V-Notch (CVN) values, which exhibits substantial variability and is therefore not usefully modelled via standard regression with its focus on the mean. Using Monte-Carlo simulation, we determine that the three CVN values corresponding to each steel specimen can be plausibly modelled as observations from the 20th, 50th and 80th percentiles of the CVN distribution. Separate quantile regression models fitted at each of these percentile levels prove sufficiently accurate for ranking steels and selecting the best combinations of composition and processing parameters.
Technological processes in the energy sector and engineering require the calculation of temperature regime of functioning of different constructions. Mathematical model of thermal loading of constructions is reduced to a non-stationary initial-boundary value problem of thermal conductivity. The article examines the formulation of the non-stationary initial-boundary value problem of thermal conductivity in the form of a boundary integral equation, analyzes the singular equation and builds the fundamental solution. To build the integral representation of the solution the method of weighted residuals is used. The correctness of the obtained integral representation of the solution in Minkowski space is confirmed. Singularity of the fundamental solution is investigated. The boundary integral equation and fundamental solution for axially symmetric domain for internal problem is built. The results of the article can be useful for numerical implementation of boundary element method.
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