Width, Depth, and Space: Tradeoffs between Branching and Dynamic Programming
AROBOT Innovation, Taiwan
Royal Holloway, University of London, TW20 0EX, UK
Department of Computer Science, RWTH Aachen University, 52062 Aachen, Germany
Author to whom correspondence should be addressed.
Received: 31 March 2018 / Revised: 27 June 2018 / Accepted: 28 June 2018 / Published: 1 July 2018
is a well-established width measure which has recently seen a resurgence of interest. Since graphs of bounded treedepth are more restricted than graphs of bounded tree- or pathwidth, we are interested in the algorithmic utility of this additional structure. On the negative side, we show with a novel approach that the space consumption of any (single-pass) dynamic programming algorithm on treedepth decompositions of depth d
cannot be bounded by
for Vertex Cover
for Dominating Set
. This formalizes the common intuition that dynamic programming algorithms on graph decompositions necessarily consume a lot of space and complements known results of the time-complexity of problems restricted to low-treewidth classes. We then show that treedepth lends itself to the design of branching algorithms. Specifically, we design two novel algorithms for Dominating Set
on graphs of treedepth d
: A pure branching algorithm that runs in time
and uses space
and a hybrid of branching and dynamic programming that achieves a running time of
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MDPI and ACS Style
Chen, L.-H.; Reidl, F.; Rossmanith, P.; Sánchez Villaamil, F. Width, Depth, and Space: Tradeoffs between Branching and Dynamic Programming. Algorithms 2018, 11, 98.
Chen L-H, Reidl F, Rossmanith P, Sánchez Villaamil F. Width, Depth, and Space: Tradeoffs between Branching and Dynamic Programming. Algorithms. 2018; 11(7):98.
Chen, Li-Hsuan; Reidl, Felix; Rossmanith, Peter; Sánchez Villaamil, Fernando. 2018. "Width, Depth, and Space: Tradeoffs between Branching and Dynamic Programming." Algorithms 11, no. 7: 98.
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