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arxiv: 1806.04988 · v2 · pith:Y254RZ24new · submitted 2018-06-13 · 🧮 math.CO

On the rank of a random binary matrix

classification 🧮 math.CO
keywords rankweightasymptoticallybasiscolumnscorrectestimatematrix
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We study the rank of the random $n\times m$ 0/1 matrix ${\bf A}_{n,m;k}$ where each column is chosen independently from the set $\Omega_{n,k}$ of 0/1 vectors with exactly $k$ 1's. Here 0/1 are the elements of the field $GF_2$. We obtain an asymptotically correct estimate for the rank in terms of $c,n,k$, assuming that $m=cn$. In addition, we assign i.i.d. $U[0,1]$ weights $X_{{\bf c}},{\bf c}\in\Omega_{n,k}$ and let the weight of a set of columns $C$ be $X(C)=\sum_{{\bf c}\in C}X_{{\bf c}}$. Let a basis be a set of $n-1_{k\text{even}}$ linearly independent columns. We obtain an asymptotically correct estimate for the minimum weight of a basis. This generalises the well-known result for $k=2$ viz. that the expected length of a minimum weight spanning tree tends to $\zeta(3)$.

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