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» On the analysis of the (1 1) memetic algorithm
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STOC
2004
ACM
134views Algorithms» more  STOC 2004»
14 years 8 months ago
Approximate max-integral-flow/min-multicut theorems
We establish several approximate max-integral-flow / minmulticut theorems. While in general this ratio can be very large, we prove strong approximation ratios in the case where th...
Kenji Obata
STOC
2003
ACM
188views Algorithms» more  STOC 2003»
14 years 8 months ago
Almost random graphs with simple hash functions
We describe a simple randomized construction for generating pairs of hash functions h1, h2 from a universe U to ranges V = [m] = {0, 1, . . . , m - 1} and W = [m] so that for ever...
Martin Dietzfelbinger, Philipp Woelfel
MP
2011
13 years 3 months ago
Smoothed analysis of condition numbers and complexity implications for linear programming
We perform a smoothed analysis of Renegar’s condition number for linear programming by analyzing the distribution of the distance to ill-posedness of a linear program subject to...
John Dunagan, Daniel A. Spielman, Shang-Hua Teng
ICFCA
2009
Springer
14 years 3 months ago
Factor Analysis of Incidence Data via Novel Decomposition of Matrices
Matrix decomposition methods provide representations of an object-variable data matrix by a product of two different matrices, one describing relationship between objects and hidd...
Radim Belohlávek, Vilém Vychodil
ISAAC
2009
Springer
175views Algorithms» more  ISAAC 2009»
14 years 3 months ago
Worst-Case and Smoothed Analysis of k-Means Clustering with Bregman Divergences
The k-means algorithm is the method of choice for clustering large-scale data sets and it performs exceedingly well in practice. Most of the theoretical work is restricted to the c...
Bodo Manthey, Heiko Röglin