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ArticlePublished 7 Aug 20262 min readBy Kevin Joginconjugate vectorembeddingarchimedeanprecision
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Number Fields and Algebraic Numbers

The Conjugate Vector Representation

Representing a field element by its images under all embeddings, the analytic information this exposes, and the precision it demands.

Engineering / MathematicsNumber Fields and Algebraic Numbers2 min readKV-MATH-0577

The conjugate vector lists the images of an element under every embedding into the complex numbers. It is numerical rather than exact, and it exposes exactly the information the exact representations hide: size, sign, and analytic behaviour.

The representation

a -> ( s_1(a), s_2(a), ..., s_n(a) )One entry per embedding; r1 real entries and r2 conjugate pairs.

Key point

Field operations become componentwise operations. Addition, multiplication and inversion are all pointwise, which makes the representation extremely cheap to compute in — at the cost of being approximate.

What it exposes

Norm and trace
The product and sum of the entries. A useful cross-check against the exact values from the matrix representation.
Size
The largest absolute value among the entries — the natural measure of how big an algebraic number is, invisible in the standard representation.
Unit detection
An algebraic integer is a unit exactly when the product of the absolute values is one.
Logarithmic embedding
The logarithms of the absolute values, weighted by embedding type. See the logarithmic embedding.

Precision

Caution

The representation is approximate. Precision must be tracked and must be sufficient for the intended conclusion. Deciding that a quantity is zero from a numerical value alone is never valid — that decision must be made in an exact representation.

Precision requirements by application
UsePrecision requirement
Comparing sizesModest
Regulator computationHigh; the determinant is of logarithms
Recovering an exact elementVery high; see dependence detection
Deciding equality or vanishingNot valid at any precision; use an exact representation

Obtaining the vector

Compute the complex roots of the defining polynomial once, to the required precision, then evaluate the element's polynomial at each root. The root computation is the expensive part and is amortised across all elements of the field.

Note

Evaluation at the roots must use a numerically stable scheme. Horner's method is standard, but for elements with large coefficients the intermediate values can overflow the working precision, so scaling may be needed.

Where it is essential

The conjugate vector is required wherever archimedean information matters: computing regulators, performing ideal reduction against a size measure, and evaluating analytic class number formulas — see analytic formulas.

Source. Henri Cohen, A Course in Computational Algebraic Number Theory, Springer GTM 138 — 4.2.4. Structural reference unverified: the source file was not available during authoring; chapter and section numbers are taken from the published edition and have not been checked against a physical copy.

Related pages

  • Root Finding over the Reals and Complex Numbers
  • The Logarithmic Embedding and the Unit Lattice
  • The Matrix (Regular) Representation of Algebraic Numbers
  • Trace, Norm and the Characteristic Polynomial

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The Matrix (Regular) Representation of Algebraic NumbersArticle · Engineering MathematicsNEXT LESSON →Trace, Norm and the Characteristic PolynomialArticle · Engineering MathematicsThe Standard Representation of Algebraic NumbersArticle · Engineering MathematicsDiscriminants and Integral BasesArticle · Engineering Mathematics
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