Networks consisting of sensors of multiple transmission ranges are preferred for next-generation wireless sensor networks because of their functional advantages. However, such multiple transmission ranges enforce rest...
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Networks consisting of sensors of multiple transmission ranges are preferred for next-generation wireless sensor networks because of their functional advantages. However, such multiple transmission ranges enforce restrictions in signal transmissions. In this work, we conduct the signal flow analysis in such networks and address the energy-efficient signal aggregation techniques that rely on linear programming techniques. We introduce a mathematical program that captures the widely different characteristics associated with the networks with sensors of multiple transmission ranges and converts it into an integer linear program which is hard to solve. To solve the scalability issues, we devise a linear program relaxation method to arrive at a solution that reduces the total signal transmissions in the network. We accommodate efficient signal compaction techniques based on compressive sensing. The simulation results demonstrate the impressive performance of the proposed method in reducing transmissions and conserving energy.
In this note we report a simple characteristic of linear programming central trajectories which has a surprising consequence. Specifically, we show that given a bounded polyhedral setP with nonempty interior, the loga...
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In this note we report a simple characteristic of linear programming central trajectories which has a surprising consequence. Specifically, we show that given a bounded polyhedral setP with nonempty interior, the logarithmic barrier function (with no objective component) induces a vector field of negative Newton directions which flows from the center ofP, along central trajectories, to solutions of every possible linear program onP.
Given a linear program with a bounded p-dimensional feasible region let the objective vector range over an s-sphere, that is, an s-dimensional sphere centered at the origin where s does not exceed p-1. If the feasible...
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Given a linear program with a bounded p-dimensional feasible region let the objective vector range over an s-sphere, that is, an s-dimensional sphere centered at the origin where s does not exceed p-1. If the feasible region and the sphere are in general position with respect to each other, then the corresponding set of all optimal solutions is a topological s-sphere. Similar results are developed for unbounded feasible regions and hemispheres of objective vectors.
As an emerging non-parametric modeling technique, the methodology of support vector regression blazed a new trail in identifying complex nonlinear systems with superior generalization capability and sparsity. Neverthe...
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As an emerging non-parametric modeling technique, the methodology of support vector regression blazed a new trail in identifying complex nonlinear systems with superior generalization capability and sparsity. Nevertheless, the conventional quadratic programming support vector regression can easily lead to representation redundancy and expensive computational cost. In this paper, by using the l(1) norm minimization and taking account of the different characteristics of autoregression (AR) and the moving average (MA), an innovative nonlinear dynamical system identification approach, linear programming SVM-ARMA(2K), is developed to enhance flexibility and secure model sparsity in identifying nonlinear dynamical systems. To demonstrate the potential and practicality of the proposed approach, the proposed strategy is applied to identify a representative dynamical engine model. Note to Practitioners-The identification of complex nonlinear engine systems poses challenges on the efficiency, flexibility and generalization capability of the identification algorithms. Inspired by the triumphs of support vector learning methodology in pattern recognition and regression analysis, an innovational nonlinear systems identification algorithm, LP-SVM-ARMA(2K) was developed in this paper as a significant stride toward the fusion of modern machine learning essentials into practical identification strategy for industrial systems. In particular, the possible generalization of LP-SVM-ARMA(2K) via more complex composite kernel function sis also discussed to meet the diversity of industrial practice.
A new linear programming model is formulated for the optimal design of dynamic systems. It is based on the multistage process, in which some outputs of one stage are the inputs to the next stage. This scheme could be ...
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A new linear programming model is formulated for the optimal design of dynamic systems. It is based on the multistage process, in which some outputs of one stage are the inputs to the next stage. This scheme could be used for forecasting and replanning the dynamic models. The computational properties show that the proposed algorithm is four times faster than the well-known simplex linear programming technique. The developed algorithm is tested on real records, obtained from an economic production system.
We establish that deterministic long run average problems of optimal control are "asymptotically equivalent" to infinite-dimensional linear programming problems (LPPs) and we establish that these LPPs can be...
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We establish that deterministic long run average problems of optimal control are "asymptotically equivalent" to infinite-dimensional linear programming problems (LPPs) and we establish that these LPPs can be approximated by finite-dimensional LPPs, the solutions of which can be used for construction of the optimal controls. General results are illustrated with numerical examples.
We consider linear programming (continuous or integer) where some matrix entries are decision parameters. If the variables are nonnegative the problem can be easily solved in two phases. It is shown that direct costs ...
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We consider linear programming (continuous or integer) where some matrix entries are decision parameters. If the variables are nonnegative the problem can be easily solved in two phases. It is shown that direct costs on the matrix entries make the problem NP-hard. Finally, a strong duality result is provided. (C) 2004 Elsevier B.V. All rights reserved.
This paper is a survey of the principal results in the theory of linear programming in reflexive linear topological spaces. We begin with a brief review of the significant results for ordinary linear programming in Eu...
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This paper is a survey of the principal results in the theory of linear programming in reflexive linear topological spaces. We begin with a brief review of the significant results for ordinary linear programming in Euclidean space. With this as a basis for comparison, for the general case we present a complete and self-contained account of three topics: (a) the classification scheme relating the properties of primal and dual programs, (b) the duality theory relevant to the problem of duality gaps between solutions of primal and dual programs, and (c) a “marginal cost” interpretation of solutions to the dual program.
Adding an additional degree of non-membership, K. T. Atanassov introduced the concept of the intuitionistic fuzzy (IF) set (IF-set), which has rarely been applied to the game theory yet. The aim of this paper is to de...
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Adding an additional degree of non-membership, K. T. Atanassov introduced the concept of the intuitionistic fuzzy (IF) set (IF-set), which has rarely been applied to the game theory yet. The aim of this paper is to develop the concept and methodology of matrix games with IF-set goals in which goals of players are expressed with IF-sets and payoffs are expressed with real numbers rather than IF-sets. In this methodology, the concepts of IF-set goals and the solutions of matrix games with IF-set goals are proposed. It is proven that solutions of matrix games with IF-set goals can be obtained through solving the developed auxiliary linear programming models, which are the generalization of matrix games with fuzzy goals. The proposed methodology is illustrated with a numerical example. Furthermore, comparison analysis of the proposed methodology is conducted to show its advantages over matrix games with fuzzy goals.
In this paper, two linear programming formulations of the convex hull problem are presented. Each formulation is the dual of the other. linear programming problems that identify a face of the convex hull are also disc...
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In this paper, two linear programming formulations of the convex hull problem are presented. Each formulation is the dual of the other. linear programming problems that identify a face of the convex hull are also discussed. An efficient algorithm is developed. Computational results obtained on an IBM 3090 are presented.
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