The Experts below are selected from a list of 240 Experts worldwide ranked by ideXlab platform
Eric M. Rains - One of the best experts on this subject based on the ideXlab platform.
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Monotonicity of the quantum Linear Programming bound
IEEE Transactions on Information Theory, 1999Co-Authors: Eric M. RainsAbstract:The most powerful technique known at present for bounding the size of quantum codes of prescribed minimum distance is the quantum Linear Programming bound. Unlike the classical Linear Programming bound, it is not immediately obvious that if the quantum Linear Programming constraints are satisfiable for dimension K, then the constraints can be satisfied for all lower dimensions. We show that the quantum Linear Programming bound is monotonic in this sense, and give an explicitly monotonic reformulation.
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Monotonicity of the quantum Linear Programming bound
arXiv: Quantum Physics, 1998Co-Authors: Eric M. RainsAbstract:The most powerful technique known at present for bounding the size of quantum codes of prescribed minimum distance is the quantum Linear Programming bound. Unlike the classical Linear Programming bound, it is not immediately obvious that if the quantum Linear Programming constraints are satisfiable for dimension K, that the constraints can be satisfied for all lower dimensions. We show that the quantum Linear Programming bound is indeed monotonic in this sense, and give an explicitly monotonic reformulation.
Qiao Zhong - One of the best experts on this subject based on the ideXlab platform.
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On fuzzy random Linear Programming
Fuzzy Sets and Systems, 1994Co-Authors: Qiao Zhong, Zhang Yue, Wang GuangyuanAbstract:Abstract The fuzzy random Linear Programming having fuzzy random variable coefficients and the decision vector of fuzzy pseudorandom variables is studied. The first results show the fact that a fuzzy pseudorandom (resp. fuzzy random) optimization solution of a fuzzy random Linear Programmings. The subsequent conclusions present methods random) optimization solutions of relative random Linear Programmings. The subsequent conclusions present methods that a fuzzy pseudorandom (resp. fuzzy random) optimization solution of a fuzzy random Linear Programming is structured by a class of pseudorandom (resp. random) optimization solutions of relative random Linear Programmings. Finally, some theorems are proved which solve the problems finding the fuzzy probability distribution function and fuzzy mathematical expectation of optimization value for a fuzzy random Linear Programming. These results reveal properties of a fuzzy random Linear Programming, and give the solution for a fuzzy random Linear Programming.
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Linear Programming with fuzzy random variable coefficients
Fuzzy Sets and Systems, 1993Co-Authors: Wang Guangyuan, Qiao ZhongAbstract:Abstract Linear Programming with fuzzy random variable coefficients is introduced by discussing a practical engineering problem. In order to study the solution of the Linear Programming with fuzzy random variable coefficients, we discuss the simplex algorithm for Linear Programming with random variable coefficients. Furthermore, the solution and distribution problem of this new fuzzy random Programming are studied.
Wang Guangyuan - One of the best experts on this subject based on the ideXlab platform.
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On fuzzy random Linear Programming
Fuzzy Sets and Systems, 1994Co-Authors: Qiao Zhong, Zhang Yue, Wang GuangyuanAbstract:Abstract The fuzzy random Linear Programming having fuzzy random variable coefficients and the decision vector of fuzzy pseudorandom variables is studied. The first results show the fact that a fuzzy pseudorandom (resp. fuzzy random) optimization solution of a fuzzy random Linear Programmings. The subsequent conclusions present methods random) optimization solutions of relative random Linear Programmings. The subsequent conclusions present methods that a fuzzy pseudorandom (resp. fuzzy random) optimization solution of a fuzzy random Linear Programming is structured by a class of pseudorandom (resp. random) optimization solutions of relative random Linear Programmings. Finally, some theorems are proved which solve the problems finding the fuzzy probability distribution function and fuzzy mathematical expectation of optimization value for a fuzzy random Linear Programming. These results reveal properties of a fuzzy random Linear Programming, and give the solution for a fuzzy random Linear Programming.
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Linear Programming with fuzzy random variable coefficients
Fuzzy Sets and Systems, 1993Co-Authors: Wang Guangyuan, Qiao ZhongAbstract:Abstract Linear Programming with fuzzy random variable coefficients is introduced by discussing a practical engineering problem. In order to study the solution of the Linear Programming with fuzzy random variable coefficients, we discuss the simplex algorithm for Linear Programming with random variable coefficients. Furthermore, the solution and distribution problem of this new fuzzy random Programming are studied.
K. Ganesan - One of the best experts on this subject based on the ideXlab platform.
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Duality Theory for Interval Linear Programming Problems
IOSR Journal of Mathematics, 2012Co-Authors: G. Ramesh, K. GanesanAbstract:We define the primal and dual Linear Programming problems involving interval numbers as the way of traditional Linear Programming problems. We discuss the solution concepts of primal and dual Linear Programming problems involving interval numbers without converting them to classical Linear Programming problems. By introducing new arithmetic operations between interval numbers, we prove the weak and strong duality theorems. Complementary slackness theorem is also proved. A numerical example is provided to illustrate the theory developed in this paper.
G. Ramesh - One of the best experts on this subject based on the ideXlab platform.
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Duality Theory for Interval Linear Programming Problems
IOSR Journal of Mathematics, 2012Co-Authors: G. Ramesh, K. GanesanAbstract:We define the primal and dual Linear Programming problems involving interval numbers as the way of traditional Linear Programming problems. We discuss the solution concepts of primal and dual Linear Programming problems involving interval numbers without converting them to classical Linear Programming problems. By introducing new arithmetic operations between interval numbers, we prove the weak and strong duality theorems. Complementary slackness theorem is also proved. A numerical example is provided to illustrate the theory developed in this paper.
