Divide-and-conquer beam search
Algorithms AIMech:DivideConquerBeamSearch DRAFT
A memory-limited breadth-first heuristic search that discards least-promising open nodes when its chosen capacity is reached and recovers a solution through relay-based divide-and-conquer reconstruction instead of retaining every predecessor layer.
Identity Notes & Concept Scope
Divide-and-conquer beam search is the approximate search procedure introduced by Zhou and Hansen in 2004. It differs from divide-and-conquer beam-stack search, which adds systematic backtracking and replay under saved beam ranges. Relay reconstruction alone does not revisit every branch discarded by beam pruning. No normalized identity match was found across the 442 AIO and 135 prior native records; ignored-inclusive text hits in the main checkout were prior discovery leads, source citations, scope contrasts and DCBSS substring matches, plus seven public certificate-data substring matches, not existing concept records.
Claims & Grounded Evidence
3 Claims
Mechanism
#search-procedure
Use breadth-first heuristic search with layered duplicate handling and relay pointers. When memory is full, remove open nodes with the worst evaluation values and continue with the surviving beam. On reaching a goal, recover its path by recursively searching the start-to-relay and relay-to-goal subproblems. Those reconstruction searches can find shorter subpaths, but this does not restore a global completeness or optimality guarantee.
Scope
#capacity-pruning
The later journal account identifies the same approximate procedure as layered BFBnB plus deletion of high-f open nodes at the memory limit, and distinguishes it from a subsequent complete backtracking extension.
Limitation
#ranking-versus-bound
The planning experiments use the nonadmissible additive heuristic to guide approximate search while separately evaluating an admissible max-pair lower bound. A favorable experimental comparison does not convert the ranking function into an admissible cost bound.
⚠️ Curated Limitations & Negative Results
- Permanent width pruning can discard every solution path; ordinary DCBS is neither complete nor guaranteed optimal.
- The reported current, previous, next and relay layers count retained search-node layers, not all temporary successors, recursive reconstruction state or output storage.
- The original developed search setting has uniform edge costs. Its planning implementation uses an inadmissible additive heuristic for ranking and a separate admissible heuristic for lower bounds; ranking and safe cost pruning must not be conflated.
- Larger permitted beams and reconstruction improvements do not establish monotonic quality or a universal speed advantage.
Artificial Intelligence Ontology (AIO) Grounding
Explicit AIO Snapshot Assessment: No Match
Rationale: No suitable AIO counterpart for this graph/state-space search procedure was identified in the inspected snapshot. Using graphs, evaluation functions or a search subroutine in an AI system does not equate the algorithm with a learned model, a learning task or a mathematical function.
Search Scope: All 442 pinned AIO labels and scoped synonyms and 135 preceding native identities were compared. Both updated checkouts were searched including hidden and ignored files. Exact CLASS nodes and edges for GraphLanguageModel, ReasoningLLM, NeuroSymbolicLLM, MachineLearningTask, MathematicalFunction and ReinforcementLearning were inspected, alongside semantic search for search, planning, heuristic, path, traversal, symbolic, reasoning and graph terminology.
| Examined AIO Candidate | Rejection Reason |
|---|---|
AIO:GraphLanguageModel
|
A language model operating on structured graph representations is not a graph traversal or shortest-path algorithm. |
AIO:ReasoningLLM
|
Language-task deductive inference does not denote general state-space search. |
AIO:NeuroSymbolicLLM
|
A language-model architecture combining neural and symbolic components is not the search procedure itself. |
AIO:MachineLearningTask
|
Building methods that learn from data is not the identity of an algorithm traversing a specified graph. |
AIO:MathematicalFunction
|
The search procedure is distinct from the mathematical functions it may evaluate. |
AIO:ReinforcementLearning
|
A learning task balancing exploration and exploitation is distinct from deterministic graph/state-space traversal. |
Evidence Sources & Primary Literature
3 Sources| Source ID | Publication / Document | Type / Access | Reference & Checksum |
|---|---|---|---|
method-source
|
Rong Zhou, Eric A. Hansen
|
primary paper
full_text
|
sha256:3fd76738c171…
|
capacity-pruning
|
Rong Zhou, Eric A. Hansen
|
primary paper
full_text
|
ID: doi:10.1016/j.artint.2005.12.002
sha256:9f094b2d1e98…
|
ranking-versus-bound
|
Rong Zhou, Eric A. Hansen
|
primary paper
full_text
|
sha256:3fd76738c171…
|
Curation Provenance
Curator: Codex
Method: ai_assisted
Model: GPT-6
Created: 2026-10-11T06:16:04Z
Updated: 2026-10-11T06:16:04Z
Source File:
data/curated/DivideConquerBeamSearch.yaml