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Register a new userA Graph Decomposition motivated by the Geometry of Randomized Rounding
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Stefan Steinerberger
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We introduce a graph decomposition which exists for all simple, connected graphs $G=(V,E)$. The decomposition $V = A cup B cup C$ is such that each vertex in $A$ has more neighbors in $B$ than in $A$ and vice versa. $C$ is `balanced: each $v in C$ has the same number of neighbours in $A$ and $B$. These decompositions arise naturally from the behavior of an associated dynamical system (`Randomized Rounding) on $(mathbb{S}^1)^{|V|}$. Connections to judicious partitions and the textsc{MaxCut} problem (in particular the Burer-Monteiro-Zhang heuristic) are being discussed.
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We study the $F$-decomposition threshold $delta_F$ for a given graph $F$. Here an $F$-decomposition of a graph $G$ is a collection of edge-disjoint copies of $F$ in $G$ which together cover every edge of $G$. (Such an $F$-decomposition can only exist if $G$ is $F$-divisible, i.e. if $e(F)mid e(G)$ and each vertex degree of $G$ can be expressed as a linear combination of the vertex degrees of $F$.) The $F$-decomposition threshold $delta_F$ is the smallest value ensuring that an $F$-divisible graph $G$ on $n$ vertices with $delta(G)ge(delta_F+o(1))n$ has an $F$-decomposition. Our main results imply the following for a given graph $F$, where $delta_F^ast$ is the fractional version of $delta_F$ and $chi:=chi(F)$: (i) $delta_Fle max{delta_F^ast,1-1/(chi+1)}$; (ii) if $chige 5$, then $delta_Fin{delta_F^{ast},1-1/chi,1-1/(chi+1)}$; (iii) we determine $delta_F$ if $F$ is bipartite. In particular, (i) implies that $delta_{K_r}=delta^ast_{K_r}$. Our proof involves further developments of the recent `iterative absorbing approach.
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CANDECOMP/PARAFAC (CP) decomposition has been widely used to deal with multi-way data. For real-time or large-scale tensors, based on the ideas of randomized-sampling CP decomposition algorithm and online CP decomposition algorithm, a novel CP decomposition algorithm called randomized online CP decomposition (ROCP) is proposed in this paper. The proposed algorithm can avoid forming full Khatri-Rao product, which leads to boost the speed largely and reduce memory usage. The experimental results on synthetic data and real-world data show the ROCP algorithm is able to cope with CP decomposition for large-scale tensors with arbitrary number of dimensions. In addition, ROCP can reduce the computing time and memory usage dramatically, especially for large-scale tensors.
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