Data mining in an engineering design environment: OR applications from graph matching

Romanowski, Carol J.; Nagi, Rakesh; Sudit, Moises

doi:10.1016/j.cor.2005.01.025

Cited by 37 publications

(21 citation statements)

References 12 publications

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“…While isomorphism and homeomorphism have been classical problems in graph theory, a new graph matching capability called graph bundle matching is introduced by Romanowski et al [7,8]. There are various pattern recognition techniques to address graph matching problems.…”

Section: Literature Reviewmentioning

confidence: 99%

Enhancements to high level data fusion using graph matching and state space search

et al. 2010

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Section: Literature Reviewmentioning

confidence: 99%

Enhancements to high level data fusion using graph matching and state space search

et al. 2010

Self Cite

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“…In other words, complementary slackness is satisfied. Also, note that α s is feasible for the dual (10). This means that (m, α s ) satisfy the KKT conditions for (9) and so m is a solution of (9).…”

Section: A Stable Outcomes As Solutions Of Linear Programmentioning

confidence: 99%

“…We induce the following matching: outcome. Next, α s, * must be feasible for (10), which reveals that it is a stable allocation. Therefore, (M, α s, * ) is a stable outcome.…”

Section: A Stable Outcomes As Solutions Of Linear Programmentioning

confidence: 99%

Distributed Bargaining in Dyadic-Exchange Networks

Richert

Cortés

2016

IEEE Trans. Control Netw. Syst.

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This paper considers dyadic-exchange networks in which individual agents autonomously form coalitions of size two and agree on how to split a transferable utility. Valid results for this game include stable (if agents have no unilateral incentive to deviate), balanced (if matched agents obtain similar benefits from collaborating), or Nash (both stable and balanced) outcomes. We design provably-correct continuous-time algorithms to find each of these classes of outcomes in a distributed way. Our algorithmic design to find Nash bargaining solutions builds on the other two algorithms by having the dynamics for finding stable outcomes feeding into the one for finding balanced ones. Our technical approach to establish convergence and robustness combines notions and tools from optimization, graph theory, nonsmooth analysis, and Lyapunov stability theory and provides a useful framework for further extensions. We illustrate our results in a wireless communication scenario where single-antenna devices have the possibility of working as 2-antenna virtual devices to improve channel capacity.

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“…Some papers suggest clustering and association mining methods to define BOM. Romanowski et al (2005) propose a method that clusters design of products, represented as BOM, into product families. Srinivasan and Moon (1999) suggest an approach to define product families, which takes into account BOM and the fact that products may be used at different levels of the network.…”

Section: Bill Of Materialsmentioning

confidence: 99%