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Schreier vector

Schreier vector is a computer science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Schreier vector rather than just read about it. In short: In mathematics, especially the field of computational group theory, a Schreier vector is a tool for reducing the time and space complexity required to calculate orbits of a permutation group. Overview Suppose G is a finite group with generating sequence X = { x 1 , x 2 , . . . , x r } {\displaystyle X=\{x_{1},x_{2},...,x_{r}\}} which acts on the finite set Ω = { 1 , 2 , . . . , n } {\displaystyle \Omega =\{1,2,...,n…

Key takeaways

  • Schreier vector belongs to computer science; place it in that map before memorising details.
  • Learn the definition first, then one example that makes the definition concrete.
  • Connect Schreier vector to a quantity you can measure, compute or draw — that is where exam questions come from.
  • Reproduce the core statement of Schreier vector from memory before moving on to harder problems.

Reference excerpt

In mathematics, especially the field of computational group theory, a Schreier vector is a tool for reducing the time and space complexity required to calculate orbits of a permutation group.

Overview Suppose G is a finite group with generating sequence X = { x 1 , x 2 , . . . , x r } {\displaystyle X=\{x_{1},x_{2},...,x_{r}\}} which acts on the finite set Ω = { 1 , 2 , . . . , n } {\displaystyle \Omega =\{1,2,...,n\}} . A common task in computational group theory is to compute the orbit of some element ω ∈ Ω {\displaystyle \omega \in \Omega } under G. At the same time, one can record a Schreier vector for ω {\displaystyle \omega } . This vector can then be used to find an element g ∈ G {\displaystyle g\in G} satisfying ω g = α {\displaystyle \omega ^{g}=\alpha } , for any α ∈ ω G {\displaystyle \alpha \in \omega ^{G}} . Use of Schreier vectors to perform this requires less storage space and time complexity than storing these g explicitly.

Formal definition All variables used here are defined in the overview. A Schreier vector for ω ∈ Ω {\displaystyle \omega \in \Omega } is a vector v = ( v [ 1 ] , v [ 2 ] , . . . , v [ n ] ) {\displaystyle \mathbf {v} =(v[1],v[2],...,v[n])} such that:

v [ ω ] = − 1 {\displaystyle v[\omega ]=-1}

For α ∈ ω G ∖ { ω } , v [ α ] ∈ { 1 , . . . , r } {\displaystyle \alpha \in \omega ^{G}\setminus \{{\omega }\},v[\alpha ]\in \{1,...,r\}} (the manner in which the v [ α ] {\displaystyle v[\alpha ]} are chosen will be made clear in the next section)

v [ α ] = 0 {\displaystyle v[\alpha ]=0} for α ∉ ω G {\displaystyle \alpha \notin \omega ^{G}}

Use in algorithms Here we illustrate, using pseudocode, the use of Schreier vectors in two algorithms

Algorithm to compute the orbit of ω under G and the corresponding Schreier vector Input: ω in Ω, X = { x 1 , x 2 , . . . , x r } {\displaystyle X=\{x_{1},x_{2},...,x_{r}\}}

for i in { 0, 1, …, n }: set v[i] = 0 set orbit = { ω }, v[ω] = −1 for α in orbit and i in { 1, 2, …, r }: if α x i {\displaystyle \alpha ^{x_{i}}} is not in orbit: append α x i {\displaystyle \alpha ^{x_{i}}} to orbit set v [ α x i ] = i {\displaystyle v[\alpha ^{x_{i}}]=i}

return orbit, v Algorithm to find a g in G such that ωg = α for some α in Ω, using the v from the first algorithm Input: v, α, X if v[α] = 0: return false set g = e, and k = v[α] (where e is the identity element of G) while k ≠ −1: set g = x k g , α = α x k − 1 , k = v [ α ] {\displaystyle g={x_{k}}g,\alpha =\alpha ^{x_{k}^{-1}},k=v[\alpha ]}

return g

References

Butler, G. (1991), Fundamental algorithms for permutation groups, Lecture Notes in Computer Science, vol. 559, Berlin, New York: Springer-Verlag, ISBN 978-3-540-54955-0, MR 1225579 Holt, Derek F. (2005), A Handbook of Computational Group Theory, London: CRC Press, ISBN 978-1-58488-372-2 Seress, Ákos (2003), Permutation group algorithms, Cambridge Tracts in Mathematics, vol. 152, Cambridge University Press, ISBN 978-0-521-66103-4, MR 1970241

Worked examples

Example 1 — a first encounter with Schreier vector

Start with the simplest possible case. Write down what Schreier vector claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In computer science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Schreier vector before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Schreier vector ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Schreier vector

In research
Schreier vector appears in computer science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry
Engineering practice reuses Schreier vector in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom
Schreier vector is common in secondary-school and first-year university syllabi. It links to neighbouring topics Computational group theory, Permutation groups, so understanding it makes those chapters shorter.
In everyday life
Look for Schreier vector outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.
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How to study Schreier vector in 20 minutes

  1. Read the reference excerpt below once, without taking notes.
  2. Close the page and write down what Schreier vector means in your own words.
  3. Compare your version with the excerpt and mark what you missed.
  4. Work through the three examples above with pen and paper.
  5. Explain Schreier vector out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Schreier vector in simple terms?

In mathematics, especially the field of computational group theory, a Schreier vector is a tool for reducing the time and space complexity required to calculate orbits of a permutation group. Overview Suppose G is a finite group with generating sequence X = { x 1 , x 2 , . . . , x r } {\displaystyl…

Why does Schreier vector matter?

Because it connects several computer science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Schreier vector?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Schreier vector.

Tags

  • Computational group theory
  • Permutation groups

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