Extremal Topologies for the Merrifield-Simmons Index on Dynamic Trees

Lecture Notes in Computer Science - Pattern Recognition ◽

10.1007/978-3-030-77004-4_7 ◽

2021 ◽

pp. 68-77

Author(s):

P. Bello ◽

M. Rodríguez ◽

G. De Ita

Keyword(s):

Dynamic Trees

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Dynamic Trees and Dynamic Point Location

SIAM Journal on Computing ◽

10.1137/s0097539793254376 ◽

1998 ◽

Vol 28 (2) ◽

pp. 612-636 ◽

Cited By ~ 11

Author(s):

Michael T. Goodrich ◽

Roberto Tamassia

Keyword(s):

Point Location ◽

Dynamic Trees

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Dynamic Trees

Encyclopedia of Algorithms ◽

10.1007/978-1-4939-2864-4_121 ◽

2016 ◽

pp. 605-609

Author(s):

Renato F. Werneck

Keyword(s):

Dynamic Trees

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ONLINE SCHEDULING OF DYNAMIC TREES

Parallel Processing Letters ◽

10.1142/s0129626495000564 ◽

1995 ◽

Vol 05 (04) ◽

pp. 635-646 ◽

Cited By ~ 9

Author(s):

MICHAEL A. PALIS ◽

JING-CHIOU LIOU ◽

SANGUTHEVAR RAJASEKARAN ◽

SUNIL SHENDE ◽

DAVID S.L. WEI

Keyword(s):

Lower Bound ◽

Competitive Ratio ◽

Scheduling Algorithm ◽

Optimal Schedule ◽

Online Scheduling ◽

The Other ◽

Static Case ◽

Scheduling Problem ◽

Dynamic Trees ◽

Dynamic Tree

The scheduling problem for dynamic tree-structured task graphs is studied and is shown to be inherently more difficult than the static case. It is shown that any online scheduling algorithm, deterministic or randomized, has competitive ratio Ω((1/g)/ log d(1/g)) for trees with granularity g and degree at most d. On the other hand, it is known that static trees with arbitrary granularity can be scheduled to within twice the optimal schedule. It is also shown that the lower bound is tight: there is a deterministic online tree scheduling algorithm that has competitive ratio O((1/g)/ log d(1/g)). Thus, randomization does not help.

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