The linear programming approach used in TOPSIS by Li does not differentiate the nature of criteria on the lines of benefit and cost and considered all the criteria as identical type with characteristics of benefit cri...
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In this paper linear programming was shortly characterized. The implementation of some basic functions for Octave have been presented. It is the GNU General Public License program for the optimization the cost-benefit...
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Solving linear programs by using entropic penalization has recently attracted new interest in the optimization community, since this strategy forms the basis for the fastest-known algorithms for the optimal transport ...
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By reformulating the linear multiplicative programming problem (LMP) as an equivalent nonconvex programming problem (EP), we present a new accelerating outcome space branch-and-bound algorithm for globally solving the...
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By reformulating the linear multiplicative programming problem (LMP) as an equivalent nonconvex programming problem (EP), we present a new accelerating outcome space branch-and-bound algorithm for globally solving the problem (LMP). Firstly, a linear relaxed programming problem is constructed, which can be used to compute the lower bound of the global optimal value of the problem (EP). Then, by subsequently subdividing the initial outcome space rectangle, and by solving a series of linear relaxed programming problems, the global optimal solution of the problem (LMP) can be obtained. Its worth mentioning that a new region reducing method and a new linear relaxed programming with a higher degree of tightness are also constructed to improve the computational efficiency in the presented algorithm. The global convergence of the presented algorithm is established, and its computational complexity is estimated. Finally, the numerical tests indicate that the presented algorithm has the higher computational efficiency than the known algorithms.
For benchmarking, a petroleum refining company is interested in how different market scenarios affect their competitors. This thesis is a feasibility study for the use of a multi-objective linear programming (MOLP) mo...
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For benchmarking, a petroleum refining company is interested in how different market scenarios affect their competitors. This thesis is a feasibility study for the use of a multi-objective linear programming (MOLP) model for analyzing the impact of market prices on competing petroleum refineries. linear programming (LP) models are widely used for optimizing petroleum refin- ery operation. The existing LP models can be utilized in the design of a MOLP model which makes it a particuarly desired model type. MOLP is a method for solving linear problems where multiple conflicting objective functions are opti- mized simultaneously. In this case, the different objective functions depict the profits of competing companies. Since there are several decision makers, this problem is different from those that have been extensively studied in open liter- ature. In this thesis, a MOLP model labeled the Refinery Ranking Model (RRM) is de- signed. The user sets the market parameters for the RRM which then determines the optimal purchases and sales for each refining company. The results indicate that MOLP can be used to analyze the market dynamics of competing refining companies. The RRM could be expanded to include dozens of refineries and still describe their detailed behavior well and with a very reasonable solution time.
A linear programming based framework is presented to derive finite blocklength converses for coding problems in information theory which is also extendable to network settings. In the point-to-point setting, the LP ba...
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A linear programming based framework is presented to derive finite blocklength converses for coding problems in information theory which is also extendable to network settings. In the point-to-point setting, the LP based framework recovers and in fact improves on almost all well-known finite blocklength converses for lossy joint source-channel coding, lossy source coding and channel coding. Moreover, the LP based framework is shown to be asymptotically tight for the averaged and compound channels under the maximum probability of error criterion. Further, for multiterminal Slepian-Wolf source coding problem, a systematic approach to synthesize new converses from considering point-to-point lossless source coding (with side-information at decoder) sub-problems is introduced. The method derives new finite blocklength converse for Slepian- Wolf coding which significantly improves on the converse of Miyake and Kanaya.
This paper focuses on spatial-temporal allocation of the sensors in multi-fighter cooperative detection. Airborne sensor must be coordinated efficiently to detect battlefield situation for finishing the operation whic...
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ISBN:
(纸本)9781538678879
This paper focuses on spatial-temporal allocation of the sensors in multi-fighter cooperative detection. Airborne sensor must be coordinated efficiently to detect battlefield situation for finishing the operation which is regarded as main facilities to get scene information. According to the relationship between detection information and sensor performance, an allocation method by linear programming is proposed for improving the sensors' ability entirely. Firstly the parameters of sensor detection performance is changed into a linear value for operation, then gain the information measurement matrix among sensors and targets or detection cell, finally transform the optimization problem into a linear programming to solve. The base of spatial-temporal allocation is the relationship that sensors' different ability from the object in the combat field, so the allocation result must be update terminal by the change of operation platform. In this way, a whole sensor system with a high performance consists of individual sensors.
linear programming has the capability to optimize multi-level maintenance operations. Although addressed in maintainability documentation and papers for over 50 years, it is still not a commonly used tool. With the ad...
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linear programming has the capability to optimize multi-level maintenance operations. Although addressed in maintainability documentation and papers for over 50 years, it is still not a commonly used tool. With the advent of Simplex Method Solvers in Excel, solutions to linear programming scenarios have become low cost and easily available. By addressing scenarios through identifying the primary goal and the constraints to the operations, linear programming is a highly useful tool for maintainability engineering and needs to be used on a more regular basis.
Index coding, a source coding problem over broadcast channels, has been a subject of both theoretical and practical interest since its introduction (by Birk and Kol, 1998). In short, the problem can be defined as foll...
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Index coding, a source coding problem over broadcast channels, has been a subject of both theoretical and practical interest since its introduction (by Birk and Kol, 1998). In short, the problem can be defined as follows: there is an input P, (p1,..., pn), a set of n clients who each desire a single entry pi of the input, and a broadcaster whose goal is to send as few messages as possible to all clients so that each one can recover its desired entry. Additionally, each client has some predetermined "side information," corresponding to certain entries of the input P, which we represent as the "side information graph" G. The graph G has a vertex vi for client i and a directed edge (vi, vj) indicating that client i knows the jth entry of the input. Given a fixed side information graph G, we are interested in determining or approximating the "broadcast rate" of index coding on the graph, i.e. the least number of messages the broadcaster can transmit so that every client recovers its desired information. The complexity of determining this broadcast rate in the most general case is open, and the best known approximations are barely better than the trivial O(n)-approximation corresponding to sending each client their information directly without performing any coding. Using index coding schemes based on linear programs (LPs), we take a two-pronged approach to approximating the broadcast rate. First, extending earlier work on planar graphs, we focus on approximating the broadcast rate for special graph families such as graphs with small chromatic number and disk graphs. In certain cases, we are able to show that simple LP-based schemes give constant-factor approximations of the broadcast rate, which seem extremely difficult to obtain in the general case. Second, we provide several LP-based schemes for the general case which are not constant-factor approximations, but which strictly improve on the best-known schemes. These can be viewed as both a strengthening of the constan
We introduce and study the infinite dimensional linear programming problem which along with its dual allows one to characterize the optimal value of the deterministic long-run average optimal control problem in the ge...
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