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Generalized permutation matrix

Generalized permutation matrix is a biology 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 Generalized permutation matrix rather than just read about it. In short: In mathematics, a generalized permutation matrix (or monomial matrix) is a matrix with the same nonzero pattern as a permutation matrix, i.e. there is exactly one nonzero entry in each row and each column. Unlike a permutation matrix, where the nonzero entry must be 1, in a generalized permutation matrix the nonzero entry can be any nonzero value.

Key takeaways

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

Reference excerpt

In mathematics, a generalized permutation matrix (or monomial matrix) is a matrix with the same nonzero pattern as a permutation matrix, i.e. there is exactly one nonzero entry in each row and each column. Unlike a permutation matrix, where the nonzero entry must be 1, in a generalized permutation matrix the nonzero entry can be any nonzero value. An example of a generalized permutation matrix is

[ 0 0 3 0 0 − 7 0 0 1 0 0 0 0 0 0 2 ] . {\displaystyle {\begin{bmatrix}0&0&3&0\\0&-7&0&0\\1&0&0&0\\0&0&0&{\sqrt {2}}\end{bmatrix}}.}

Structure An invertible matrix A is a generalized permutation matrix if and only if it can be written as a product of an invertible diagonal matrix D and an (implicitly invertible) permutation matrix P: i.e.,

A = D P . {\displaystyle A=DP.}

Group structure The set of n × n generalized permutation matrices with entries in a field F forms a subgroup of the general linear group GL(n, F), in which the group of nonsingular diagonal matrices Δ(n, F) forms a normal subgroup. Indeed, over all fields except GF(2), the generalized permutation matrices are the normalizer of the diagonal matrices, meaning that the generalized permutation matrices are the largest subgroup of GL(n, F) in which diagonal matrices are normal. The abstract group of generalized permutation matrices is the wreath product of F× and Sn. Concretely, this means that it is the semidirect product of Δ(n, F) by the symmetric group Sn:

Sn ⋉ Δ(n, F), where Sn acts by permuting coordinates and the diagonal matrices Δ(n, F) are isomorphic to the n-fold product (F×)n. To be precise, the generalized permutation matrices are a (faithful) linear representation of this abstract wreath product: a realization of the abstract group as a subgroup of matrices.

Subgroups The subgroup where all entries are 1 is exactly the permutation matrices, which is isomorphic to the symmetric group. The subgroup where all entries are ±1 is the signed permutation matrices, which is the hyperoctahedral group. The subgroup where the entries are mth roots of unity μ m {\displaystyle \mu _{m}} is isomorphic to a generalized symmetric group. The subgroup of diagonal matrices is abelian, normal, and a maximal abelian subgroup. The quotient group is the symmetric group, and this construction is in fact the Weyl group of the general linear group: the diagonal matrices are a maximal torus in the general linear group (and are their own centralizer), the generalized permutation matrices are the normalizer of this torus, and the quotient, N ( T ) / Z ( T ) = N ( T ) / T ≅ S n {\displaystyle N(T)/Z(T)=N(T)/T\cong S_{n}} is the Weyl group.

Properties If a nonsingular matrix and its inverse are both nonnegative matrices (i.e. matrices with nonnegative entries), then the matrix is a generalized permutation matrix. The determinant of a generalized permutation matrix is given by det ( G ) = det ( P ) ⋅ det ( D ) = sgn ⁡ ( π ) ⋅ d 11 ⋅ … ⋅ d n n , {\displaystyle \det(G)=\det(P)\cdot \det(D)=\operatorname {sgn} (\pi )\cdot d_{11}\cdot \ldots \cdot d_{nn},} where sgn ⁡ ( π ) {\displaystyle \operatorname {sgn} (\pi )} is the sign of the permutation π {\displaystyle \pi } associated with P {\displaystyle P} and d 11 , … , d n n {\displaystyle d_{11},\ldots ,d_{nn}} are the diagonal elements of D {\displaystyle D} .

… excerpt ends here. Continue reading the full article.

Worked examples

Example 1 — a first encounter with Generalized permutation matrix

Start with the simplest possible case. Write down what Generalized permutation matrix claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In biology, 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 Generalized permutation matrix 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 Generalized permutation matrix 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 Generalized permutation matrix

In research
Generalized permutation matrix appears in biology 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 Generalized permutation matrix 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
Generalized permutation matrix is common in secondary-school and first-year university syllabi. It links to neighbouring topics Matrices (mathematics), Permutations, Sparse matrices, so understanding it makes those chapters shorter.
In everyday life
Look for Generalized permutation matrix 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 Generalized permutation matrix in 20 minutes

  1. Read the reference excerpt below once, without taking notes.
  2. Close the page and write down what Generalized permutation matrix 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 Generalized permutation matrix out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Generalized permutation matrix in simple terms?

In mathematics, a generalized permutation matrix (or monomial matrix) is a matrix with the same nonzero pattern as a permutation matrix, i.e. there is exactly one nonzero entry in each row and each column. Unlike a permutation matrix, where the nonzero entry must be 1, in a generalized permutation…

Why does Generalized permutation matrix matter?

Because it connects several biology 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 Generalized permutation matrix?

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 Generalized permutation matrix.

Tags

  • Matrices (mathematics)
  • Permutations
  • Sparse matrices

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