Theorem · version 1

Optimization NP-hardness for three voters

Statement

Computing a linear order minimizing the sum of Kendall-tau distances to exactly three strict complete ballots is NP-hard.

Assumptions

  • This is a function/optimization task; NP-completeness is stated for the threshold decision language. Worst-case hardness does not prove P ≠ NP.
Input
Three strict complete ballots
Output
A consensus order of minimum total score

Evidence

Source reports

Connections

In this problem · publication v4 · Selected context

Kemeny rank aggregation for three voters →

Alternative answer · Proved

Related statements

A consensus optimizer would also answer score-threshold questions; the two tasks have different complexity classifications.

NP-completeness concerns the decision language; optimization is described as NP-hard.

Details & sources
Decision NP-completeness for three complete ballots →

Proved · version 1

Given exactly three strict complete linear orders π₁, π₂, π₃ on a finite set A and an integer B, deciding whether there exists an order π with ∑i=13KT⁡(πi,π)≤B\sum_{i=1}^3\operatorname{KT}(\pi_i,\pi)\le B is NP-complete.

  • KT counts unordered pairs whose relative orders disagree. The paper’s majority-margin threshold b is translated by the computable profile constant ∑{a,b} min(n_ab,n_ba).

Scope: KT counts unordered pairs whose relative orders disagree. The paper’s majority-margin threshold b is translated by the computable profile constant ∑{a,b} min(n_ab,n_ba).

Optimization NP-hardness for three voters →

Proved · version 1

Computing a linear order minimizing the sum of Kendall-tau distances to exactly three strict complete ballots is NP-hard.

  • This is a function/optimization task; NP-completeness is stated for the threshold decision language. Worst-case hardness does not prove P ≠ NP.

Scope: This is a function/optimization task; NP-completeness is stated for the threshold decision language. Worst-case hardness does not prove P ≠ NP.

Relationship not independently reviewed.

Connection in Kemeny rank aggregation for three voters →

Relationship not independently reviewed. This concerns its use in this problem.

In this problem · publication v5

Kemeny rank aggregation for three voters →

Alternative answer · Proved

Related statements

A consensus optimizer would also answer score-threshold questions; the two tasks have different complexity classifications.

NP-completeness concerns the decision language; optimization is described as NP-hard.

Details & sources
Decision NP-completeness for three complete ballots →

Proved · version 1

Given exactly three strict complete linear orders π₁, π₂, π₃ on a finite set A and an integer B, deciding whether there exists an order π with ∑i=13KT⁡(πi,π)≤B\sum_{i=1}^3\operatorname{KT}(\pi_i,\pi)\le B is NP-complete.

  • KT counts unordered pairs whose relative orders disagree. The paper’s majority-margin threshold b is translated by the computable profile constant ∑{a,b} min(n_ab,n_ba).

Scope: KT counts unordered pairs whose relative orders disagree. The paper’s majority-margin threshold b is translated by the computable profile constant ∑{a,b} min(n_ab,n_ba).

Optimization NP-hardness for three voters →

Proved · version 1

Computing a linear order minimizing the sum of Kendall-tau distances to exactly three strict complete ballots is NP-hard.

  • This is a function/optimization task; NP-completeness is stated for the threshold decision language. Worst-case hardness does not prove P ≠ NP.

Scope: This is a function/optimization task; NP-completeness is stated for the threshold decision language. Worst-case hardness does not prove P ≠ NP.

Relationship not independently reviewed.

Connection in Kemeny rank aggregation for three voters →

Relationship not independently reviewed. This concerns its use in this problem.

Sources

Kemeny Rank Aggregation is NP-Hard for Three Voters →

  • Theorem 1; Sections 2–5 → · Original passage ↗
    Recorded wording

    A polynomial reduction from SIMPLE MAX CUT, using a majority tournament realizable by exactly three complete ballots, proves decision NP-completeness.

    paraphrase · Linked proof prose is not republished. Any copied graph data is CC0, as specified by its archive.

  • Sections 1 and 6 → · Original passage ↗
    Recorded wording

    Computing an optimum is NP-hard for three voters. The paper also explains extensions to every fixed voter count at least three and recalls the polynomial-time two-voter case.

    paraphrase · Linked proof prose is not republished. Any copied graph data is CC0, as specified by its archive.

  • Section 1; Dwork et al. (2001) → · Original passage ↗
    Recorded wording

    The total Kendall-tau score differs from the majority-disagreement score by a ballot-dependent constant; they have the same minimizers. Three voters was the remaining boundary question.

    paraphrase · Linked proof prose is not republished. Any copied graph data is CC0, as specified by its archive.

Dominik Peters · Preprint · Publication date Jul 28, 2026

Source-reported results and external checks are separate from independent OpenMath assessment.

Revision & attribution

Source revision: v1. Observed Oct 14, 2026.

Peters credits GPT-5.6 Sol Ultra with finding the reduction, Claude Fable 5 with part of the simplification, and his own mathematical simplification and manuscript writing.

History

Statement versions

Statement versions and problem publication versions are separate. A new publication can update context or sources while retaining the same statement.

Assessment history · 1

Assessments retained in these publication records. Earlier statement versions retain their own source histories.

Record details
Claim ID
claim-kemeny-three-optimization
Statement version
1
SHA-256
0c3c8455d930d4afc1d5ccaca48279b0ddc6e13c0cb261c19210d3977838a67c

Publication records