arXiv subjects

Udrea Păun

Publications and source records attributed to Udrea Păun.

3 recordsLinked to original sources

arXiv2025

$G$ Method and Finite-Time Consensus

We give an extension of the $G$ method, with results, the extension and results being partly suggested by the finite Markov chains and specially by the finite-time consensus problem for the DeGroot model and that for the DeGroot model on distributed systems. For the (homogeneous and nonhomogeneous) DeGroot model, using the $G$ method, a result for reaching a partial or total consensus in a finite time is given. Further, we consider a special submodel/case of the DeGroot model, with examples and comments -- a subset/subgroup property is discovered. For the DeGroot model on distributed systems, using the $G$ method too, we have a result for reaching a partial or total (distributed) consensus in a finite time similar to that for the DeGroot model for reaching a partial or total consensus in a finite time. Then we show that for any connected graph having $2^{m}$ vertices, $m\geq 1,$ and a spanning subgraph isomorphic to the $m$-cube graph, distributed averaging is performed in $m$ steps -- this result can be extended -- research work -- for any graph with $n_{1}n_{2}...n_{t}$ vertices under certain conditions, where $t,$ $n_{1},n_{2},...,n_{t}\geq 2,$ and, in this case, distributed averaging is performed in $t$ steps.

math.PR↗

arXiv2024

$G$ Method in Action: Pivot$^{\text{+}}$ Algorithm for Self-avoiding Walk

The pivot algorithm -- we also call it the pivot chain -- is an algorithm for approximately uniform sampling from $Ω_{N},$ the set of $N$-step self-avoiding walks on $\mathbb{Z}^{d}$ ($N,$ $d\geq 1$). Based on this algorithm and the $G$ method, we construct another algorithm/chain, called the pivot$^{\text{+}}$ algorithm/chain, for approximately uniform sampling from $Ω_{N},$ here, $N\geq 2$. The pivot$^{\text{+}}$ algorithm samples the pivot from the set $\left\{ 1,2,...,N-1\right\} $ according to the uniform probability distribution on this set while the pivot algorithm samples the pivot from the set $\left\{0,1,2,...,N-1\right\} $ according to the uniform probability distribution on this set, so, on the pivot, the pivot$^{\text{+}}$ algorithm is better than the pivot algorithm. Further, we obtain another important thing, namely, the pivot$^{\text{+}}$ algorithm/chain enters, at time $1$, a set $2d$ times smaller than $Ω_{N},$ and stays forever in this set, so, at times $1,2,...$ we work with a chain having a state space $2d$ times smaller than $Ω_{N}$. As to the speed of convergence, we conjecture that the pivot$^{\text{+}}$ algorithm/chain is faster than the pivot algorithm/chain.

math.PR↗

arXiv2023

$G^{+}$ Method in Action: New Classes of Nonnegative Matrices with Results

The $G^{+}$ method is a new method, a powerful one, for the study of (homogeneous and nonhomogeneous) products of nonnegative matrices -- for problems on the products of nonnegative matrices. To study such products, new classes of matrices are introduced: that of the sum-positive matrices, that of the $\left[ Δ\right] $-positive matrices on partitions (of the column index sets), that of the $g_{k}^{+}$-matrices... On the other hand, the $g_{k}^{+}$-matrices lead to necessary and sufficient conditions for the $k$-connected graphs. Using the $G^{+}$ method, we prove old and new results (Wielandt Theorem and a generalization of it, etc.) on the products of nonnegative matrices -- mainly, sum-positive, $\left[ Δ\right] $-positive on partitions, irreducible, primitive, reducible, fully indecomposable, scrambling, or Sarymsakov matrices, in some cases the matrices being, moreover, $g_{k}^{+}$-matrices (not only irreducible).

math.CO↗