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Huiya Yan

Publications and source records attributed to Huiya Yan.

2 recordsLinked to original sources

Interchange graphs of (0,1)-matrices are maximally Hamiltonian

For integer vectors R,S let A(R,S) denote the class of (0,1)-matrices with row sum vector R and column sum vector S. Its interchange graph G(R,S) has A(R,S) as its vertex set, two matrices being adjacent when they differ by a single 2 x 2 interchange. Brualdi asked whether G(R,S) is Hamiltonian for every R,S. We prove the stronger statement that G(R,S) is maximally Hamiltonian: Hamilton-laceable when bipartite, and Hamilton-connected when not. The proof is a structural induction on the number of matrices in the class, organized by the structure theory of interchange graphs. Deleting inactive lines and splitting invariant positions expresses any class as a Cartesian product, reducing the argument to the prime factors. The bipartite classes are products of complete transposition graphs; we settle them together, without induction, by proving they are paired 2-disjoint-path-coverable and hence Hamilton-laceable, using a recent theorem of Coleman, Fischberg, Gong, Harrington and Wong on paired disjoint path covers. The non-bipartite classes divide into three cases: products assembled from smaller factors, a base of Johnson graphs and small classes, and the large prime classes, treated by a pivot-and-fiber construction whose line quotients are matroid base-exchange graphs. The complete argument has been machine-checked in the Lean 4 proof assistant from first principles together with seven cited results of the literature; the disjoint-path-cover results it imports are themselves proved within the formalization.

math.CO

General Capacity for Deterministic Dissemination in Wireless Ad Hoc Networks

In this paper, we study capacity scaling laws of the deterministic dissemination (DD) in random wireless networks under the generalized physical model (GphyM). This is truly not a new topic. Our motivation to readdress this issue is two-fold: Firstly, we aim to propose a more general result to unify the network capacity for general homogeneous random models by investigating the impacts of different parameters of the system on the network capacity. Secondly, we target to close the open gaps between the upper and the lower bounds on the network capacity in the literature. The generality of this work lies in three aspects: (1) We study the homogeneous random network of a general node density $λ\in [1,n]$, rather than either random dense network (RDN, $λ=n$) or random extended network (REN, $λ=1$) as in the literature. (2) We address the general deterministic dissemination sessions, \ie, the general multicast sessions, which unify the capacities for unicast and broadcast sessions by setting the number of destinations for each session as a general value $n_d\in[1,n]$. (3) We allow the number of sessions to change in the range $n_s\in(1,n]$, instead of assuming that $n_s=Θ(n)$ as in the literature. We derive the general upper bounds on the capacity for the arbitrary case of $(λ, n_d, n_s)$ by introducing the Poisson Boolean model of continuum percolation, and prove that they are tight according to the existing general lower bounds constructed in the literature.

cs.IT