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Record W2398768505

From Interval Arithmetic to Interval Constraints.

2011· article· en· W2398768505 on OpenAlexaff
M. H. van Emden

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicConstraint Satisfaction and Optimization
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsInterval arithmeticInterval (graph theory)MathematicsAffine arithmeticArithmeticConstraint satisfaction problemDivision (mathematics)Function (biology)Algebra over a fieldConstraint (computer-aided design)Discrete mathematicsAlgorithmPure mathematicsCombinatorics
DOInot available

Abstract

fetched live from OpenAlex

Two definitions of division by an interval containing zero are compared: a functional one and a relational one. We show that the relational definition also provides interval inverses for other functions that do not have point inverses, such as max and the absolute-value function. Applying the same approach to the ≤ relation introduces the “companion functions ” of relations. By regarding the arithmetic operations +, −, ∗, and / as ternary relations, we obtain the interval versions of the operations. This opens the way for regarding arithmetic problems such as evaluating expressions and solving equations as Constraint Satisfaction Problems (csps). These have a useful computational theory, which is, however, influenced by their predominantly discrete applications. We generalize the conventional formulation to better accommodate real-valued variables, and state the main results. When these results are applied to numerical csps we relate the interval evaluation of an arithmetic expression to the family of solving algorithms of csps. The key to our method of bringing interval arithmetic and interval constraints under a common denominator are companion functions. These functions form an alternative characterization of n-ary relations and appear to be a new contribution to the mathematical theory of relations.

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.

How this classification was reachedexpand

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.870
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.035
GPT teacher head0.248
Teacher spread0.212 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2011
Admission routes1
Has abstractyes

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