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Matrix similarity

Matrix similarity is a mathematics 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 Matrix similarity rather than just read about it. In short: In linear algebra, two n-by-n matrices A and B are called similar if there exists an invertible n-by-n matrix P such that B = P − 1 A P . {\displaystyle B=P^{-1}AP.} Two matrices are similar if and only if they represent the same linear map under two possibly different bases, with P being the change-of-basis matrix. A transformation A ↦ P−1AP is called a similarity transformation or conjugation of the matrix A.

Key takeaways

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

Reference excerpt

In linear algebra, two n-by-n matrices A and B are called similar if there exists an invertible n-by-n matrix P such that

B = P − 1 A P . {\displaystyle B=P^{-1}AP.}

Two matrices are similar if and only if they represent the same linear map under two possibly different bases, with P being the change-of-basis matrix. A transformation A ↦ P−1AP is called a similarity transformation or conjugation of the matrix A. In the general linear group, similarity is therefore the same as conjugacy, and similar matrices are also called conjugate; however, in a given subgroup H of the general linear group, the notion of conjugacy may be more restrictive than similarity, since it requires that P be chosen to lie in H.

Motivating example When defining a linear transformation, it can be the case that a change of basis can result in a simpler form of the same transformation. For example, the matrix representing a rotation in ℝ3 when the axis of rotation is not aligned with the coordinate axis can be complicated to compute. If the axis of rotation were aligned with the positive z-axis, then it would simply be

S = [ cos ⁡ θ − sin ⁡ θ 0 sin ⁡ θ cos ⁡ θ 0 0 0 1 ] , {\displaystyle S={\begin{bmatrix}\cos \theta &-\sin \theta &0\\\sin \theta &\cos \theta &0\\0&0&1\end{bmatrix}},}

where θ {\displaystyle \theta } is the angle of rotation. In the new coordinate system, the transformation would be written as

y ′ = S x ′ , {\displaystyle y'=Sx',}

where x' and y' are respectively the original and transformed vectors in a new basis containing a vector parallel to the axis of rotation. In the original basis, the transform would be written as

y = T x , {\displaystyle y=Tx,}

where vectors x and y and the unknown transform matrix T are in the original basis. To write T in terms of the simpler matrix, we use the change-of-basis matrix P that transforms x and y as x ′ = P x {\displaystyle x'=Px} and y ′ = P y {\displaystyle y'=Py} :

y ′ = S x ′ ⇒ P y = S P x ⇒ y = ( P − 1 S P ) x = T x {\displaystyle {\begin{aligned}&&y'&=Sx'\\[1.6ex]&\Rightarrow &Py&=SPx\\[1.6ex]&\Rightarrow &y&=\left(P^{-1}SP\right)x=Tx\end{aligned}}}

Thus, the matrix in the original basis, T {\displaystyle T} , is given by T = P − 1 S P {\displaystyle T=P^{-1}SP} . The transform in the original basis is found to be the product of three easy-to-derive matrices. In effect, the similarity transform operates in three steps: change to a new basis (P), perform the simple transformation (S), and change back to the old basis (P−1).

Properties Similarity is an equivalence relation on the space of square matrices. Because matrices are similar if and only if they represent the same linear operator with respect to (possibly) different bases, similar matrices share all properties of their shared underlying operator:

… excerpt ends here. Continue reading the full article.

Worked examples

Example 1 — a first encounter with Matrix similarity

Start with the simplest possible case. Write down what Matrix similarity claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In mathematics, 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 Matrix similarity 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 Matrix similarity 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 Matrix similarity

In research
Matrix similarity appears in mathematics 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 Matrix similarity 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
Matrix similarity is common in secondary-school and first-year university syllabi. It links to neighbouring topics Equivalence (mathematics), Matrices (mathematics), so understanding it makes those chapters shorter.
In everyday life
Look for Matrix similarity 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 Matrix similarity in 20 minutes

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

Frequently asked questions

What is Matrix similarity in simple terms?

In linear algebra, two n-by-n matrices A and B are called similar if there exists an invertible n-by-n matrix P such that B = P − 1 A P . {\displaystyle B=P^{-1}AP.} Two matrices are similar if and only if they represent the same linear map under two possibly different bases, with P being the chang…

Why does Matrix similarity matter?

Because it connects several mathematics 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 Matrix similarity?

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 Matrix similarity.

Tags

  • Equivalence (mathematics)
  • Matrices (mathematics)

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