Integer Factorization: Another perspective
- URL: http://arxiv.org/abs/2507.07055v1
- Date: Wed, 09 Jul 2025 17:20:49 GMT
- Title: Integer Factorization: Another perspective
- Authors: Gilda Rech Bansimba, Regis Freguin Babindamana,
- Abstract summary: integer factorization is a fundamental problem in number theory and computer science.<n>We propose innovative approaches to reformulate this problem, thereby offering new perspectives.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Integer factorization is a fundamental problem in algorithmic number theory and computer science. It is considered as a one way or trapdoor function in the (RSA) cryptosystem. To date, from elementary trial division to sophisticated methods like the General Number Field Sieve, no known algorithm can break the problem in polynomial time, while its proved that Shor's algorithm could on a quantum computer. In this paper, we recall some factorization algorithms and then approach the problem under different angles. Firstly, we take the problem from the ring $\displaystyle\left(\mathbb{Z}, \text{+}, \cdot\right)$ to the Lebesgue space $\mathcal{L}^{1}\left(X\right)$ where $X$ can be $\mathbb{Q}$ or any given interval setting. From this first perspective, integer factorization becomes equivalent to finding the perimeter of a rectangle whose area is known. In this case, it is equivalent to either finding bounds of integrals or finding primitives for some given bounds. Secondly, we take the problem from the ring $\displaystyle\left(\mathbb{Z}, \text{+}, \cdot\right) $ to the ring of matrices $\left( M_{2}\text{(}\mathbb{Z}\text{)}, \ \text{+} \ \cdot\right)$ and show that this problem is equivalent to matrix decomposition, and therefore present some possible computing algorithms, particularly using Gr\"obner basis and through matrix diagonalization. Finally, we address the problem depending on algebraic forms of factors and show that this problem is equivalent to finding small roots of a bivariate polynomial through coppersmith's method. The aim of this study is to propose innovative methodological approaches to reformulate this problem, thereby offering new perspectives.
Related papers
- Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure [16.324043075920564]
We provide new high-accuracy randomized algorithms for solving linear systems and regression problems.<n>Our algorithms nearly-match a natural complexity limit under dense inputs for these problems.<n>We show how to obtain these running times even under the weaker assumption that all but $k$ of the singular values have a bounded generalized mean.
arXiv Detail & Related papers (2025-07-15T20:48:30Z) - Highway to Hull: An Algorithm for Solving the General Matrix Code Equivalence Problem [4.450536872346658]
We present a novel algorithm which solves the matrix code equivalence problem in the general case.<n>Our algorithm achieves the same time complexity as Narayanan emphet al. but with a lower space complexity.
arXiv Detail & Related papers (2025-04-01T22:39:31Z) - The Communication Complexity of Approximating Matrix Rank [50.6867896228563]
We show that this problem has randomized communication complexity $Omega(frac1kcdot n2log|mathbbF|)$.
As an application, we obtain an $Omega(frac1kcdot n2log|mathbbF|)$ space lower bound for any streaming algorithm with $k$ passes.
arXiv Detail & Related papers (2024-10-26T06:21:42Z) - Provably learning a multi-head attention layer [55.2904547651831]
Multi-head attention layer is one of the key components of the transformer architecture that sets it apart from traditional feed-forward models.
In this work, we initiate the study of provably learning a multi-head attention layer from random examples.
We prove computational lower bounds showing that in the worst case, exponential dependence on $m$ is unavoidable.
arXiv Detail & Related papers (2024-02-06T15:39:09Z) - Fast $(1+\varepsilon)$-Approximation Algorithms for Binary Matrix
Factorization [54.29685789885059]
We introduce efficient $(1+varepsilon)$-approximation algorithms for the binary matrix factorization (BMF) problem.
The goal is to approximate $mathbfA$ as a product of low-rank factors.
Our techniques generalize to other common variants of the BMF problem.
arXiv Detail & Related papers (2023-06-02T18:55:27Z) - Learning a Single Neuron with Adversarial Label Noise via Gradient
Descent [50.659479930171585]
We study a function of the form $mathbfxmapstosigma(mathbfwcdotmathbfx)$ for monotone activations.
The goal of the learner is to output a hypothesis vector $mathbfw$ that $F(mathbbw)=C, epsilon$ with high probability.
arXiv Detail & Related papers (2022-06-17T17:55:43Z) - Feature Cross Search via Submodular Optimization [58.15569071608769]
We study feature cross search as a fundamental primitive in feature engineering.
We show that there exists a simple greedy $(1-1/e)$-approximation algorithm for this problem.
arXiv Detail & Related papers (2021-07-05T16:58:31Z) - Global Convergence of Gradient Descent for Asymmetric Low-Rank Matrix
Factorization [49.090785356633695]
We study the asymmetric low-rank factorization problem: [mathbfU in mathbbRm min d, mathbfU$ and mathV$.
arXiv Detail & Related papers (2021-06-27T17:25:24Z) - Unique sparse decomposition of low rank matrices [17.037882881652617]
We find a unique decomposition of a low rank matrixYin mathbbRrtimes n$.
We prove that up to some $Yin mathRrtimes n$ is a sparse-wise decomposition of $Xin mathbbRrtimes n$.
arXiv Detail & Related papers (2021-06-14T20:05:59Z) - The Fine-Grained Hardness of Sparse Linear Regression [12.83354999540079]
We show that there are no better-than-brute-force algorithms for the problem.
We also show the impossibility of better-than-brute-force algorithms when the prediction error is measured.
arXiv Detail & Related papers (2021-06-06T14:19:43Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.