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Symmetry Handling in Mixed‐Integer Programming

2011· other· en· W1528193418 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueWiley Encyclopedia of Operations Research and Management Science · 2011
Typeother
Languageen
FieldEngineering
TopicVehicle Routing Optimization Methods
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsInteger programmingInteger (computer science)Symmetry (geometry)Branch and cutLinear programmingSet (abstract data type)Branch and boundMathematicsMathematical optimizationInteger points in convex polyhedraBranch and priceComputer scienceCombinatoricsDiscrete mathematicsGeometry

Abstract

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Abstract This article focuses on solving integer programs whose feasible regions are highly symmetric. Symmetry has long been considered a curse in integer programming, and auxiliary (often extended) formulations are sought to reduce the amount of symmetry in an integer linear programming (ILP) formulation. The approach taken in this article describes methods that seek to exploit the symmetry, not avoid it by reformulation. A standard method for solving integer programs is branch‐and‐bound . In branch‐and‐bound, the set of feasible solutions is partitioned, forming more easily solved subproblems. The presence of symmetry means that many of these subproblems are equivalent in a sense we describe later. Only one member of each collection of equivalent subproblems needs to be solved. Failure to recognize that many subproblems are equivalent results in a waste of computational effort that can render an instance unsolvable by branch‐and‐bound. In this article, we describe methods that use the symmetry of the problem formulation to reduce the size of the feasible region.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.654
Threshold uncertainty score0.658

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.034
GPT teacher head0.328
Teacher spread0.294 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it