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Restricted isometry property

Restricted isometry property 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 Restricted isometry property rather than just read about it. In short: In linear algebra, the restricted isometry property (RIP) characterizes matrices which are nearly orthonormal, at least when operating on sparse vectors. The concept was introduced by Emmanuel Candès and Terence Tao and is used to prove many theorems in the field of compressed sensing.

Key takeaways

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

Reference excerpt

In linear algebra, the restricted isometry property (RIP) characterizes matrices which are nearly orthonormal, at least when operating on sparse vectors. The concept was introduced by Emmanuel Candès and Terence Tao and is used to prove many theorems in the field of compressed sensing. There are no known large matrices with bounded restricted isometry constants (computing these constants is strongly NP-hard, and is hard to approximate as well), but many random matrices have been shown to remain bounded. In particular, it has been shown that with exponentially high probability, random Gaussian, Bernoulli, and partial Fourier matrices satisfy the RIP with number of measurements nearly linear in the sparsity level. The current smallest upper bounds for any large rectangular matrices are for those of Gaussian matrices. Web forms to evaluate bounds for the Gaussian ensemble are available at the Edinburgh Compressed Sensing RIC page.

Definition Let A be an m × p matrix and let 1 ≤ s ≤ p be an integer. Suppose that there exists a constant δ s ∈ ( 0 , 1 ) {\displaystyle \delta _{s}\in (0,1)} such that, for every m × s submatrix As of A and for every s-dimensional vector y,

( 1 − δ s ) ‖ y ‖ 2 2 ≤ ‖ A s y ‖ 2 2 ≤ ( 1 + δ s ) ‖ y ‖ 2 2 . {\displaystyle (1-\delta _{s})\|y\|_{2}^{2}\leq \|A_{s}y\|_{2}^{2}\leq (1+\delta _{s})\|y\|_{2}^{2}.\,}

Then, the matrix A is said to satisfy the s-restricted isometry property with restricted isometry constant δ s {\displaystyle \delta _{s}} . This condition is equivalent to the statement that for every m × s submatrix As of A we have

‖ A s ∗ A s − I s × s ‖ 2 → 2 ≤ δ s , {\displaystyle \|A_{s}^{*}A_{s}-I_{s\times s}\|_{2\to 2}\leq \delta _{s},}

where I s × s {\displaystyle I_{s\times s}} is the s × s {\displaystyle s\times s} identity matrix and ‖ X ‖ 2 → 2 {\displaystyle \|X\|_{2\to 2}} is the operator norm. See for example for a proof. Finally this is equivalent to stating that all eigenvalues of A s ∗ A s {\displaystyle A_{s}^{*}A_{s}} are in the interval [ 1 − δ s , 1 + δ s ] {\displaystyle [1-\delta _{s},1+\delta _{s}]} .

Restricted Isometric Constant (RIC) The RIC Constant is defined as the infimum of all possible δ {\displaystyle \delta } for a given A ∈ R n × m {\displaystyle A\in \mathbb {R} ^{n\times m}} .

δ K = inf [ δ : ( 1 − δ ) ‖ y ‖ 2 2 ≤ ‖ A s y ‖ 2 2 ≤ ( 1 + δ ) ‖ y ‖ 2 2 ] , ∀ | s | ≤ K , ∀ y ∈ R | s | {\displaystyle \delta _{K}=\inf \left[\delta :(1-\delta )\|y\|_{2}^{2}\leq \|A_{s}y\|_{2}^{2}\leq (1+\delta )\|y\|_{2}^{2}\right],\ \forall |s|\leq K,\forall y\in R^{|s|}}

It is denoted as δ K {\displaystyle \delta _{K}} .

Eigenvalues For any matrix that satisfies the RIP property with a RIC of δ K {\displaystyle \delta _{K}} , the following condition holds:

… excerpt ends here. Continue reading the full article.

Worked examples

Example 1 — a first encounter with Restricted isometry property

Start with the simplest possible case. Write down what Restricted isometry property 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 Restricted isometry property 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 Restricted isometry property 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 Restricted isometry property

In research
Restricted isometry property 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 Restricted isometry property 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
Restricted isometry property is common in secondary-school and first-year university syllabi. It links to neighbouring topics Linear algebra, Signal processing, so understanding it makes those chapters shorter.
In everyday life
Look for Restricted isometry property 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 Restricted isometry property in 20 minutes

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

Frequently asked questions

What is Restricted isometry property in simple terms?

In linear algebra, the restricted isometry property (RIP) characterizes matrices which are nearly orthonormal, at least when operating on sparse vectors. The concept was introduced by Emmanuel Candès and Terence Tao and is used to prove many theorems in the field of compressed sensing.

Why does Restricted isometry property 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 Restricted isometry property?

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 Restricted isometry property.

Tags

  • Linear algebra
  • Signal processing

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