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Convex optimization / Operations research / Linear programming / Mathematical optimization / Approximation algorithm / Computational complexity theory / Optimization problem / Duality / Algorithm / Theoretical computer science / Applied mathematics / Mathematics


CCCG 2014, Halifax, Nova Scotia, August 11–13, 2014 The Generalized Minimum Manhattan Network Problem (GMMN) – Scale-Diversity Aware Approximation and a Primal-Dual Algorithm Stefan Funke∗
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Document Date: 2014-06-21 05:15:26

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Halifax / /

Company

SIAM Journal / The primal LP / Intel / /

Country

Germany / /

Currency

pence / /

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Facility

University of Stuttgart / /

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minimum cost rectilinear network / primaldual algorithm / minimum cost network / feasible networks / approximation algorithm / rooted partial network / depth-first search / naive deep first search approach / approximate solution / feasible network / fractional solution / approximation algorithms / partial networks / partial network / /

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U2 / /

Organization

University of Stuttgart / /

Person

Sailesh K. Rao / Kamal Jain / Frank K. Hwang / Xavier Mu˜ noz / Combinatorica / Bing Lu / Joachim Spoerhase / Weiping Shi / Martin P. Seybold / Sebastian Seibert / Aparna Das / Joachim Gudmundsson / Timothy Law Snyder / Chen Su / Alexander Wolff / Francis Y. L. Chin / Walter Unger / Karim Nouioua / Birgit Engels / Victor Chepoi / David P. Williamson / Algorithmica / Hanan Grid / Martin Zachariasen / Christos Levcopoulos / Peter W. Shor / Stephen G. Kobourov / David B. Shmoys / /

ProvinceOrState

Nova Scotia / /

PublishedMedium

SIAM Journal on Computing / Theoretical Computer Science / /

Technology

known d-RSA approximation algorithm / primal-dual algorithm / approximation algorithm / primaldual algorithm / approximation algorithms / second algorithm / rounding algorithm / 2-GMMN The algorithm / 2014 2.4 An Algorithm / two algorithms / 2 Approximation Algorithm / /

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