Quantum-Inspired Constraint Relaxation for Sparse Linear Systems

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

This paper introduces a new technique for relaxing constraints in sparse linear systems, leveraging quantum-inspired algorithms to enhance computational efficiency. Sparse linear systems are prevalent in various fields, presenting significant challenges due to their inherent sparsity and computational complexity. Traditional methods often struggle with these issues, necessitating innovative approaches. This work proposes a novel framework that employs quantum-like principles to identify and exploit sparse patterns within the system, ultimately reducing computational cost and improving solution quality. The technique is designed to be effective across a range of sparse linear systems, addressing key challenges associated with these data. The core mechanism involves a dynamic adjustment of constraint bounds, guided by an iterative process that mimics quantum phenomena. This approach offers a promising pathway to overcome limitations of current methods and facilitate more efficient solutions. The paper details the algorithm's implementation, discusses its performance evaluation, and concludes with insights into future research directions.

DOI: 10.5281/zenodo.22458222

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