Position-Candidate-Hypothesis
Structural-statistical approach to NP-complete problems
Structural-statistical approach to NP-complete problems
Structural-statistical approach to NP-complete problems
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.
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.
PCH uses n hypotheses, n positions, and n candidates per position for problems of size n.
Structural elements in solution space
Entities for position assignments
Independent research processes
Search → Structure
Sequential → Parallel
Deterministic → Statistical
Black‑box → Interpretable
High-performance implementations for Traveling Salesman Problem
Comparative analysis and benchmarking
n hypotheses · n positions · n candidates — complete problem coverage through structural decomposition.