Results 281 to 290 of about 469,123 (318)
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ACM SIGSAM Bulletin, 1987
The project is to use PROLOG to solve an optimization problem on a network. A brief introduction to networks includes the definitions necessary for the description of the optimal flow algorithm, which uses the painted network algorithm as a subroutine. An implementation of the painted network algorithm in PROLOG is given.
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The project is to use PROLOG to solve an optimization problem on a network. A brief introduction to networks includes the definitions necessary for the description of the optimal flow algorithm, which uses the painted network algorithm as a subroutine. An implementation of the painted network algorithm in PROLOG is given.
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Communication network optimization
Computer Communications, 1988Abstract The major facets of the branch of combinatorial optimization, known as communication network optimization, are surveyed. The basic principles are briefly investigated, followed by a discussion of location, connection and traffic routing problems.
V. M. Grout, Peter W. Sanders
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Optimization by neural networks
IEEE International Conference on Neural Networks, 1988The ability to map and solve a number of interesting problems on neural networks motivates a proposal for using neural networks as a highly parallel model for general-purpose computing. The author review this proposal, showing how to map combinational optimization problems, including graph K-partitioning, vertex cover, maximum independent set, maximum ...
J. Ramanujam, P. Sadayappan
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On the optimality of the OFDMA network
IEEE Communications Letters, 2005This letter studies the optimality of the OFDMA network. It is proved that in the multiuser multicarrier downlink system with independent decoding, OFDMA is the optimal multiple access scheme. It is also shown that the optimality of OFDMA holds for any adaptive modulation scheme whose rate-SNR/SINR function can be approximated as a convex function.
Guoqing Li, Hui Liu 0011
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Mathematical Programming, 1991
The paper treats a piping system, where the layout of the network is given but the diameters of the pipes should be chosen among a small number of different values. The cost of realizing the system should be minimized while keeping the energy heads at the nodes above some lower limits.
Claus Thorp Hansen +2 more
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The paper treats a piping system, where the layout of the network is given but the diameters of the pipes should be chosen among a small number of different values. The cost of realizing the system should be minimized while keeping the energy heads at the nodes above some lower limits.
Claus Thorp Hansen +2 more
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Parallel Processing Letters, 1998
Routing is the process, performed at each site of a communication network upon receipt of a message, that decides to which neighbor the message is to be sent or that the message is to be received locally. We present a general model of routing based on a decision tree representation of routing information.
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Routing is the process, performed at each site of a communication network upon receipt of a message, that decides to which neighbor the message is to be sent or that the message is to be received locally. We present a general model of routing based on a decision tree representation of routing information.
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2003
We prove that Batcher’s odd-even (m,n)-merging networks are exactly optimal for (m,n)=(3,4k+2) and (4,4k+2) for k ≥ 0 in terms of the number of comparators used. For other cases where m ≤ 4, the optimality of Batcher’s (m,n)-merging networks has been proved.
Kazuyuki Amano, Akira Maruoka
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We prove that Batcher’s odd-even (m,n)-merging networks are exactly optimal for (m,n)=(3,4k+2) and (4,4k+2) for k ≥ 0 in terms of the number of comparators used. For other cases where m ≤ 4, the optimality of Batcher’s (m,n)-merging networks has been proved.
Kazuyuki Amano, Akira Maruoka
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Annals of Operations Research, 1991
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Collaborative optimization networks
Proceedings of the 5th International Conference on Sustainable Information Engineering and Technology, 2020Collaborative optimization, both as a technique and as a social phenomenon, has assumed considerable importance, yet remains under-explored in the literature. This paper argues for the need for developing the computational infrastructure for supporting collaborative optimization at scale.
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IEEE Transactions on Circuits and Systems II: Express Briefs, 2008
Filters with optimal dynamic range may be generated through state transformation. Examples show that this optimization leads to a chip-area and power-consumption reduction up to 50%. The method is flexible enough to apply to any filter transfer function and any method to determine the dynamic range.
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Filters with optimal dynamic range may be generated through state transformation. Examples show that this optimization leads to a chip-area and power-consumption reduction up to 50%. The method is flexible enough to apply to any filter transfer function and any method to determine the dynamic range.
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