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Soft Heap

A meldable priority queue permits a bounded number of keys to rise, making the minimum current key cheap to find while the client tracks possible original-order error.

Version
v1 · 2026-10-17 · History
Domain-specific #
14303
Domain group
Applied Sciences & Engineering
Origin domain
Computer Science & Software Engineering
Subdomain
Data Structures and Algorithms → Computer Science & Software Engineering
Aliases
Soft heap

Core Idea

A soft heap is a meldable priority queue that may raise an item's current key above its original key; findmin returns an item with minimum current key, which may not minimize original key. After n cumulative insertions, at most εn items still held may be corrupted at one time, for 0 < ε ≤ ½; actual corruption is optional. In Chazelle's original construction, insert costs O(log(1/ε)) amortized while create, meld, delete, and findmin cost O(1) amortized.[^ref-5a03792a8a64]

Scope of Application

The sources support an operation-level MST role map from indexed passages and a full-text numeric selection procedure:

  • Minimum spanning trees: Border-edge queues supply minimum current-cost edges to Chazelle's algorithm.[^ref-46f656d7a76b]
  • Exact selection: ε = ⅓ extractions yield one pivot whose original rank is checked before recursion.[^ref-5a03792a8a64]

Clarity

Ask whether “minimum” refers to the original or current key: the heap returns an item minimal under current keys. The εn bound counts corrupted items still held against all insertions so far; it is neither a probability nor a fraction of the items currently present.[^ref-5a03792a8a64]

Manages Complexity

The client gets a stated count of possible misordered items and can decide how to handle them. Lower ε allows fewer corruptions but raises the original construction’s amortized insertion bound; after many deletions, the theorem does not guarantee that most remaining items are uncorrupted.[^ref-5a03792a8a64]

Abstract Reasoning

Identify the items, original keys, current keys, ε, and cumulative insertions. Check that key changes are upward and within the simultaneous cap, then ask whether the caller needs a current-key minimum or an exact original-key result; exactness needs a separate client step.[^ref-5a03792a8a64]

Knowledge Transfer

Graph edges and selection numbers fill the same queue roles, but their clients respond differently: MST retraction handles corrupted edges, while selection verifies an original rank. Data Structure is the broader arrangement-for-use pattern; ordinary exact heaps can be zero-corruption soft-heap special cases.[ref-5a03792a8a64][ref-46f656d7a76b]

Relationships to Other Abstractions

Local relationship map for Soft HeapParents appear above the current abstraction, mutual partners to the right, and children below. Node labels state whether each abstraction is prime or domain-specific; colors identify relation types.Soft HeapDOMAINPrime abstraction: Data Structure — is a kind ofData StructurePRIME

Current abstraction Soft Heap Domain-specific

Parents (1) — more general patterns this builds on

  • Soft Heap is a kind of Data Structure Prime

    A soft heap arranges keyed items for a defined operation profile under a controlled current-key corruption invariant.

Hierarchy path (1) — routes to 1 parentless root

Neighborhood in Abstraction Space

Soft Heap sits in a sparse region of the domain-specific corpus (95th percentile for distinctiveness): few abstractions share its structure, so a faithful description tends to retrieve it precisely.

Family — Unclustered & Miscellaneous (2721 abstractions)

Nearest neighbors

Computed from structural-signature embeddings · 2026-10-17

References

[^ref-5a03792a8a64]: Bernard Chazelle. “The Soft Heap: An Approximate Priority Queue with Optimal Error Rate.” Journal of the ACM 47, no. 6 (2000): 1012–1027. https://www.cs.cmu.edu/afs/cs/academic/class/15859-f05/www/documents/p1012-chazelle.pdf. Original published-paper full text inspected at a university mirror, especially Theorem 1.1, §2 applications and §3 construction. The author-hosted copy at https://www.cs.princeton.edu/~chazelle/pubs/sheap.pdf was indexed but did not open directly. [^ref-46f656d7a76b]: Bernard Chazelle. “A Minimum Spanning Tree Algorithm with Inverse-Ackermann Type Complexity.” Journal of the ACM 47, no. 6 (2000): 1028–1047. https://www.cs.princeton.edu/~chazelle/pubs/mst.pdf. Original author-hosted paper; indexed passages from §§1.2–1.3 and §2 RETRACTION/EXTENSION inspected. The full PDF did not open directly, so this citation supports only the bounded operation-level MST role map used here.