Position-Candidate-Hypothesis

Structural-statistical approach to NP-complete problems

Paradigm Published 2025 Computational Complexity DOI: 10.5281/zenodo.17614888

Position-Candidate-Hypothesis Paradigm

Structural-statistical approach to NP-complete problems

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Novel structural-statistical approach that transforms NP-complete problem-solving from exhaustive search to systematic decomposition into positions, candidates, and hypotheses, followed by parallel investigation and statistical synthesis.

Abstract

This research paper introduces the Position-Candidate-Hypothesis (PCH) paradigm as a novel theoretical approach to NP-complete problems. This work proposes a fundamental shift from traditional combinatorial search to structuralstatistical analysis. The research explores the decomposition of problems into three interconnected components: positions, candidates, and hypotheses, followed by statistical integration. This work presents a new perspective on computational problem-solving that emphasizes structural analysis and pattern recognition over exhaustive search methods.

Key Resources

Full Paper

Position-Candidate-Hypothesis (PCH) Paradigm - Full Paper

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Research Proposition

PCH uses n hypotheses, n positions, and n candidates per position for problems of size n.

Fundamental Components

Positions
n positions

Structural elements in solution space

Candidates
n per position

Entities for position assignments

Hypotheses
n hypotheses

Independent research processes

DOI
10.5281/zenodo.17614888
Published
November 15, 2025
Language
English

Key Transformations

Search → Structure

Sequential → Parallel

Deterministic → Statistical

Black‑box → Interpretable

TSP Ecosystem

TSP Solvers

High-performance implementations for Traveling Salesman Problem


✓ Exact TSP Solver (Go) — Branch & Bound
✓ Smart TSP Oracle (Python) — Exact solver
✓ Smart TSP Solver (Python) — Heuristic (~25% better)
✓ Smart TSP Benchmark — Testing infrastructure

Performance

Comparative analysis and benchmarking


✓ ~25% improvement on clustered data
✓ Adaptive thresholding strategies
✓ Spatial intelligence heuristics

PCH Methodology

n hypotheses · n positions · n candidates — complete problem coverage through structural decomposition.

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