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GECCO
2009
Springer
110views Optimization» more  GECCO 2009»
15 years 9 months ago
EMO shines a light on the holes of complexity space
Typical domains used in machine learning analyses only partially cover the complexity space, remaining a large proportion of problem difficulties that are not tested. Since the ac...
Núria Macià, Albert Orriols-Puig, Es...
DISOPT
2011
240views Education» more  DISOPT 2011»
14 years 11 months ago
On the directed Full Degree Spanning Tree problem
We study the parameterized complexity of a directed analog of the Full Degree Spanning Tree problem where, given a digraph D and a nonnegative integer k, the goal is to construct a...
Daniel Lokshtanov, Venkatesh Raman, Saket Saurabh,...
CPC
2006
110views more  CPC 2006»
15 years 4 months ago
Solving Sparse Random Instances of Max Cut and Max 2-CSP in Linear Expected Time
Abstract. We show that a maximum cut of a random graph below the giantcomponent threshold can be found in linear space and linear expected time by a simple algorithm. In fact, the ...
Alexander D. Scott, Gregory B. Sorkin
JDA
2007
129views more  JDA 2007»
15 years 4 months ago
Approximating the k-traveling repairman problem with repairtimes
Given an undirected graph G = (V,E) and a source vertex s ∈ V , the k-traveling repairman (KTR) problem, also known as the minimum latency problem, asks for k tours, each starti...
Raja Jothi, Balaji Raghavachari
SODA
2012
ACM
253views Algorithms» more  SODA 2012»
13 years 6 months ago
Kernelization of packing problems
Kernelization algorithms are polynomial-time reductions from a problem to itself that guarantee their output to have a size not exceeding some bound. For example, d-Set Matching f...
Holger Dell, Dániel Marx