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Exploring new upper and lower bounds for the $A_α$-energy of graphs

Mainak Basunia, Pratima Panigrahi

Abstract

Let $G$ be a graph on $n$ vertices and $m$ edges. For $α\in [0,1]$, the $A_α$-matrix of $G$ is defined as $A_α(G) = αD(G) + (1- α) A(G)$, where $A(G)$ is the adjacency matrix and $D(G)$ is the degree diagonal matrix of $G$. If $ρ_1 \geq ρ_2 \ldots \geq ρ_n$ are the eigenvalues of $A_α(G)$, the $A_α$-energy of $G$ is defined as $E_{A_α}(G) = \sum_{i=1}^{n} |ρ_i -\frac{2αm}{n}|$. In this paper, we present novel upper and lower bounds for $E_{A_α}(G)$ in terms of standard graph invariants, showing that each bound is sharp and identifying the specific graphs attaining them. For selected bounds, we provide brief comparative analysis with existing results, observing improved estimates. Furthermore, we establish new relations between $E_{A_α}(G)$ and other well known graph energies, including adjacency, Laplacian, as well as the adjacency energy of the line graph.

Exploring new upper and lower bounds for the $A_α$-energy of graphs

Abstract

Let be a graph on vertices and edges. For , the -matrix of is defined as , where is the adjacency matrix and is the degree diagonal matrix of . If are the eigenvalues of , the -energy of is defined as . In this paper, we present novel upper and lower bounds for in terms of standard graph invariants, showing that each bound is sharp and identifying the specific graphs attaining them. For selected bounds, we provide brief comparative analysis with existing results, observing improved estimates. Furthermore, we establish new relations between and other well known graph energies, including adjacency, Laplacian, as well as the adjacency energy of the line graph.
Paper Structure (6 sections, 23 theorems, 25 equations, 1 table)

This paper contains 6 sections, 23 theorems, 25 equations, 1 table.

Key Result

Lemma 2.1

max_props_by_fan Given two real square matrices $M$ and $N$ of same order, $\mathcal{E}(M+N) \leq \mathcal{E}(M) + \mathcal{E}(N)$. Equality is satisfied only when there is an orthogonal matrix $P$ that guarantees that both the matrices $PM$ and $PN$ are positive semidefinite.

Theorems & Definitions (33)

  • Lemma 2.1
  • Lemma 2.2
  • Lemma 2.3
  • Lemma 2.4
  • Lemma 2.5
  • Lemma 2.6
  • Lemma 2.7
  • Lemma 2.8
  • Theorem 3.1
  • proof
  • ...and 23 more