arXiv subjects

Amit Nir

Publications and source records attributed to Amit Nir.

3 recordsLinked to original sources

arXiv2026

Distributed Algorithms for Near-Equitable Coloring

For an $n$-vertex graph of maximum degree $\Delta$ and diameter $D$, an equitable $(\Delta+1)$-coloring is a vertex coloring where the frequency of each color (namely, the number of vertices it colors) are all equal to $\sigma=n/(\Delta+1)$ (up to rounding). The Hajnal-Szemer\'edi Theorem guarantees the existence of such a coloring for every graph, and an $O(n^2\Delta)$ time sequential algorithm is known for computing such a coloring. Here, we study near-equitable graph coloring in distributed networks. The main question of interest is how close one can remain to the desired palette size of $\Delta+1$ while computing, in few distributed rounds, a coloring whose frequencies are close to $\sigma$. It appears that these two conflicting parameters exhibit a tradeoff, which we attempt to explore. We present a suite of fast randomized distributed algorithms representing varying points on this tradeoff, analyze their properties, and study their time complexity in the sequential, CONGEST and Congested Clique (CC) models.

cs.DC↗

arXiv2026

Diameter-Free Distributed Frequency Control for Graph Coloring in the CONGEST Model

This paper presents two randomized proper-coloring algorithms that control color frequencies in the synchronous CONGEST model without paying a diameter-dependent coordination cost. Let $\lambda \geq 1$ denote the desired failure exponent. For every fixed $\delta > 0$, the first algorithm uses $\chi = \lceil (2+\delta)\Delta \rceil$ colors and, with probability at least $1 - n^{-\lambda}$, outputs a proper coloring that bounds the deviation of every color frequency from $n/\chi$ by $O_\delta(\sqrt{(\lambda+1)(n/\chi)\lg n} + (\lambda+1)\lg n)$. Under an explicit load condition, this additive guarantee yields two-sided relative balance. The second algorithm works with every $\chi > \Delta$ and gives a one-sided frequency cap controlled by the palette slack $\chi - \Delta$. In particular, it uses $\Delta + \lceil (\Delta+1)/\lceil \ln n \rceil \rceil$ colors and caps every used color class by $O((\lambda+1)(\sigma \lg^2 n + \lg n))$, where $\sigma = n/(\Delta+1)$. Both algorithms run in $O((\lambda+1)\lg n)$ rounds, with no dependence on the network diameter; for the first algorithm, the multiplicative constant in the time bound depends on $\delta$.

cs.DC↗

arXiv2020

Machine-learning Iterative Calculation of Entropy for Physical Systems

Characterizing the entropy of a system is a crucial, and often computationally costly, step in understanding its thermodynamics. It plays a key role in the study of phase transitions, pattern formation, protein folding and more. Current methods for entropy estimation suffer either from a high computational cost, lack of generality or inaccuracy, and inability to treat complex, strongly interacting systems. In this paper, we present a novel method, termed MICE, for calculating the entropy by iteratively dividing the system into smaller subsystems and estimating the mutual information between each pair of halves. The estimation is performed with a recently proposed machine learning algorithm which works with arbitrary network architectures that can be chosen to fit the structure and symmetries of the system at hand. We show that our method can calculate the entropy of various systems, both thermal and athermal, with state-of-the-art accuracy. Specifically, we study various classical spin systems, and identify the jamming point of a bidisperse mixture of soft disks. Lastly, we suggest that besides its role in estimating the entropy, the mutual information itself can provide an insightful diagnostic tool in the study of physical systems.

cond-mat.stat-mech↗