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Evolutionary solving of the debts' clearing problem

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Paper breakdownThe authors' claims, conditions and measurements, as JSON Signed receiptProof this review has not been altered
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Csaba Patcas, Attila Bartha

Neural and Evolutionary Computing · Submitted 26 Feb 2014

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim An evolutionary algorithm for solving the debts' clearing problem is proposed.

how it is stated · asserted 1.00 read the record →

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arXiv ID1402.6556 (version 1)
Pith IDYMFT4F2L, Pith's permanent record of this paper and its review
CitationAttila Bartha, Csaba Patcas. "Evolutionary solving of the debts' clearing problem." arXiv:1402.6556 (2014). https://pith.science/paper/YMFT4F2L
Claim paper

The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

Open the compiled record

Compiled by classifier-1.13-20260917 via OpenRouter, question bank abstract-p1-20260923, 2026-09-23: 1 request, 3,482 input tokens, $0.0001 reported.

What the abstract itself states, as probabilities: claims first 0.04 · states limitation 0.09 · human participants 0.04 · controlled comparison 0.02 · randomized assignment 0.01 · states quantitative result 0.05 · compares with prior results 0.05 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

The debts' clearing problem is about clearing all the debts in a group of n entities (persons, companies etc.) using a minimal number of money transaction operations. The problem is known to be NP-hard in the strong sense. As for many intractable problems, techniques from the field of artificial intelligence are useful in finding solutions close to optimum for large inputs. An evolutionary algorithm for solving the debts' clearing problem is proposed.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 The debts' clearing problem is about clearing all the debts in a group of n entities (persons, companies etc.) using a minimal number of money transaction operations. background 1.00 0.05 0.04 asserted 1.00
s1 The problem is known to be NP-hard in the strong sense. background 1.00 0.05 0.03 asserted 1.00
s2 As for many intractable problems, techniques from the field of artificial intelligence are useful in finding solutions close to optimum for large inputs. background 0.99 0.04 0.04 asserted 0.77
s3 An evolutionary algorithm for solving the debts' clearing problem is proposed. contribution 0.96 0.93 0.52 asserted 1.00

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from argument 0.56.

What was examined, and what was not

sources read
  • arXiv listing abstract v1
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
levels run
  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

The debts' clearing problem is about clearing all the debts in a group of n entities (persons, companies etc.) using a minimal number of money transaction operations. The problem is known to be NP-hard in the strong sense. As for many intractable problems, techniques from the field of artificial intelligence are useful in finding solutions close to optimum for large inputs. An evolutionary algorithm for solving the debts' clearing problem is proposed.

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