Solving Subset Sum Problems using Binary Optimization with Applications in Auditing and Financial Data Analysis
Many applications in automated auditing and the analysis and consistency check of financial documents can be formulated in part as the subset sum problem: Given a set of numbers and a target sum, find the subset of numbers that sums up to the target. The problem is NP-hard and classical solving algorithms are therefore not practical to use in real applications. We tackle the problem as a QUBO (quadratic unconstrained binary optimization) problem and show how gradient descent on Hopfield Networks reliably finds solutions for both artificial and real data. We give an outlook for the application of specialized hardware and quantum algorithms.
- Published in:
arXiv - Type:
Article - Authors:
Biesner, David; Gerlach, Thore; Bauckhage, Christian; Kliem, Bernd; Sifa, Rafet - Year:
2022
Citation information
Biesner, David; Gerlach, Thore; Bauckhage, Christian; Kliem, Bernd; Sifa, Rafet: Solving Subset Sum Problems using Binary Optimization with Applications in Auditing and Financial Data Analysis, arXiv, 2022, https://arxiv.org/abs/2211.02653, Biesner.etal.2022c,
@Article{Biesner.etal.2022c,
author={Biesner, David; Gerlach, Thore; Bauckhage, Christian; Kliem, Bernd; Sifa, Rafet},
title={Solving Subset Sum Problems using Binary Optimization with Applications in Auditing and Financial Data Analysis},
journal={arXiv},
url={https://arxiv.org/abs/2211.02653},
year={2022},
abstract={Many applications in automated auditing and the analysis and consistency check of financial documents can be formulated in part as the subset sum problem: Given a set of numbers and a target sum, find the subset of numbers that sums up to the target. The problem is NP-hard and classical solving algorithms are therefore not practical to use in real applications. We tackle the problem as a QUBO...}}
