arXiv subjects

Suraj Kumar Sahoo

Publications and source records attributed to Suraj Kumar Sahoo.

4 recordsLinked to original sources

arXiv2026

Sharp quadratic $\chi$-binding functions for powers of bipartite graphs

For every natural number $r\geq 2$, we construct $r^{th}$ powers of bipartite graphs whose chromatic number is quadratic in their clique number, showing that the straightforward quadratic upper bound is best possible. We thereby settle an open problem posed by Chakraborty, Chandran, Jacob and Pillai [J. Graph Theory 112(3) (2026), 235-254] by establishing the sharpness of the quadratic bound for squares of bipartite graphs.

math.CO↗

arXiv2026

The boxicity of the compressed zero divisor graph of the ring of integers modulo N

The boxicity of a graph $G$, denoted by $box(G)$, is the minimum integer $d\geq 0$ such that $G$ is the intersection graph of axis-parallel boxes in $\mathbb{R}^d$. The class of zero divisor graphs introduced by Beck (1988) is a popular class of graphs and has been studied extensively by several researchers. Suppose $Z(R)$ is the set of zero divisors of a ring $R$. The zero divisor graph $\Gamma(R)$ for a ring $R $ is defined as the graph with the vertex set $V(\Gamma(R))=Z(R)$ and $E(\Gamma(R))=\{\{x,y\}\colon x,y\in Z(R)\text{ with }x\neq y\text{ and }x y=0\}$. One can define an equivalence relation $\sim$ on $V(\Gamma(R))$ such that for vertices $x$ and $y$, one has $x\sim y$ if and only if $x$ and $y$ have the same annihilator, i.e., $Ann(x)=Ann(y)$. The compressed zero divisor graph $\Gamma_E(R)$ for a ring $R$ is the simple graph obtained from $\Gamma(R)$ by retaining exactly one vertex from each equivalence class induced by $\sim$. In this paper, we completely answer two open questions posed in Discrete Applied Mathematics 391 (2026), pp. 127-136. Let $N=\prod_{i=1}^a p_i^{n_i}$ be the prime factorization of a positive integer $N$ and let $\mathbb{Z}_N$ be the ring of integers modulo $N$. We determine the exact boxicity of the compressed zero divisor graph $\Gamma_E(\mathbb{Z}_N)$. We show that when $a\geq 2$, $box(\Gamma_E(\mathbb{Z}_N))= a-1$ if and only if one of the following is true: $(i)$ $a\geq 2$ and $N$ is the product of two coprime integers $x$ and $y$ such that $x$ is a square-free integer and $y$ is the cube of a prime number; $(ii)$ $a\geq 3$ and $N$ is square-free; $(iii)$ $a\geq 2$, $N$ is cube-free, not square-free, and contains at least one prime divisor $p_i$ such that $n_i=1$. If $a=2$ and $n_1=n_2=1$, then $\Gamma_{E}(\mathbb{Z}_N)$ is a clique, and so, $box(\Gamma_{E}(\mathbb{Z}_N))=0$. In all other cases, $box(\Gamma_{E}(\mathbb{Z}_N))=a$.

cs.DM↗

arXiv2025

Learning Regularizers: Learning Optimizers that can Regularize

Learned Optimizers (LOs), a type of Meta-learning, have gained traction due to their ability to be parameterized and trained for efficient optimization. Traditional gradient-based methods incorporate explicit regularization techniques such as Sharpness-Aware Minimization (SAM), Gradient-norm Aware Minimization (GAM), and Gap-guided Sharpness-Aware Minimization (GSAM) to enhance generalization and convergence. In this work, we explore a fundamental question: \textbf{Can regularizers be learned?} We empirically demonstrate that LOs can be trained to learn and internalize the effects of traditional regularization techniques without explicitly applying them to the objective function. We validate this through extensive experiments on standard benchmarks (including MNIST, FMNIST, CIFAR and Neural Networks such as MLP, MLP-Relu and CNN), comparing LOs trained with and without access to explicit regularizers. Regularized LOs consistently outperform their unregularized counterparts in terms of test accuracy and generalization. Furthermore, we show that LOs retain and transfer these regularization effects to new optimization tasks by inherently seeking minima similar to those targeted by these regularizers. Our results suggest that LOs can inherently learn regularization properties, \textit{challenging the conventional necessity of explicit optimizee loss regularization.

cs.LG↗

arXiv2025

Boxicity of Zero Divisor Graphs

A $d$-dimensional box is the cartesian product $R_i\times\cdots\times R_d$ where each $R_i$ is a closed interval on the real line. The boxicity of a graph, denoted as $box(G)$, is the minimum integer $d\geq 0$ such that $G$ is the intersection graph of a collection of $d$-dimensional boxes. The study of graph classes associated with algebraic structures is a fascinating area where graph theory and algebra meet. A well-known class of graphs associated with rings is the class of zero divisor graphs introduced by Beck in 1988. Since then, this graph class has been studied extensively by several researchers. Denote by $Z(R)$ the set of zero divisors of a ring $R$. The zero divisor graph $\Gamma(R)$ for a ring $R$ is defined as the graph with the vertex set $V(\Gamma(R))=Z(R)$ and $E(\Gamma(R))=\{\{a_i,a_j\}:a_ia_j\in Z(R)\text{ and }a_ia_j=0 \}$. Let $N=\Pi_{i=1}^ap_i^{n_i}$ be the prime factorization of $N$. In Discrete Applied Mathematics 365 (2025), pp. 260-269, it was shown that $box(\Gamma(\mathbb{Z}_N))\leq\Pi_{i=1}^a(n_i+1)-\Pi_{i=1}^a(\lfloor n_i/2\rfloor+1)-1$. In this paper we exactly determine the boxicity of $\Gamma(\mathbb{Z}_N)$: We show that when $N\equiv 2\pmod 4$ and $N$ is not divisible by $p^3$ for any prime divisor $p$, we have $box(\Gamma(\mathbb{Z}_N))=a-1$. Otherwise $box(\Gamma(\mathbb{Z}_N))=a$. Suppose $R$ is a non-zero commutative ring with identity that is also a reduced ring and let $k$ be the size of the set of minimal prime ideals of $R$. In the same paper, it was showed that $box(\Gamma(R))\leq 2^k-2$. We improve this result by showing $\lfloor k/2\rfloor\leq box(\Gamma(R))\leq k$ with the same assumption on $R$. In this paper we also show that $a-1\leq\dim_{TH}(\Gamma(\mathbb{Z}_N))\leq a$ and $\lfloor k/2\rfloor\leq\dim_{TH}(\Gamma(R))\leq k$, where $\dim_{TH}$ is another dimensional parameter associated with graphs known as the threshold dimension.

cs.DM↗