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Theoretical Developments in Pure Mathematics

Sanjay Patel*

Department of Mathematics, University of Mumbai, India

*Corresponding Author:
Sanjay Patel
Department of Mathematics, University of Mumbai, India
E-mail: sanjaypatel@unimumbai.ac.in

Received: 01-Mar-2025, Manuscript No. JSMS-25-169988; Editor assigned: 4-Mar-2025, Pre-QC No. JSMS-25-169988 (PQ); Reviewed: 20-Mar-2025, QC No JSMS-25-169988; Revised: 26-Mar- 2025, Manuscript No. JSMS-25-169988 (R); Published: 30-Mar-2025, DOI: 10.4172/RRJ Stats Math Sci. 11.01.001

Citation: Sanjay Patel, Department of Mathematics, University of Mumbai, India. RRJ Mater Sci. 2025.11.001.

Copyright: © 2025 Sanjay Patel, this is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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Abstract

  

INTRODUCTION

Pure mathematics explores abstract structures and logical foundations, often laying groundwork for future applied innovations. Its domains include algebra, topology, geometry, number theory, and logic.

Core Areas of Pure Mathematics

Algebra: Modern algebra studies structures like groups, rings, and fields. Current research examines representation theory and its applications to physics [1].

Topology: Topology investigates properties preserved under deformation. Knot theory and algebraic topology find applications in DNA modeling and quantum theory [2].

Geometry: Geometry has evolved from Euclidean frameworks to modern differential and algebraic geometry, with implications in relativity and string theory [3].

Number Theory: Number theory continues to influence cryptography, modular forms, and Diophantine equations. Prime distribution remains an active research focus [4].

Mathematical Logic: Logic underpins proof theory, model theory, and computability. It ensures mathematical consistency and influences theoretical computer science [5].

REFERENCES

  1. Bottou L. Optimization methods for large-scale machine learning. SIAM Review, 2018; 60: 223–311.
  2. Goodfellow I, Bengio Y, Courville A. Deep Learning. MIT Press. 2016.
  3. Boyd S, Vandenberghe L. Convex Optimization. Cambridge University Press. 2004.
  4. Nesterov Y. Lectures on Convex Optimization. Springer. 2018.
  5. Wright SJ, Recht B. Optimization for Data Science. Annual Review of Statistics and Its Application, 2022; 9: 47–70.