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L-notation

L-notation 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 L-notation rather than just read about it. In short: L-notation is an asymptotic notation analogous to big-O notation, denoted as L n [ α , c ] {\displaystyle L_{n}[\alpha ,c]} for a bound variable n {\displaystyle n} tending to infinity. Like big-O notation, it is usually used to roughly convey the rate of growth of a function, such as the computational complexity of a particular algorithm.

Key takeaways

  • L-notation 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 L-notation to a quantity you can measure, compute or draw — that is where exam questions come from.
  • Reproduce the core statement of L-notation from memory before moving on to harder problems.

Reference excerpt

L-notation is an asymptotic notation analogous to big-O notation, denoted as L n [ α , c ] {\displaystyle L_{n}[\alpha ,c]} for a bound variable n {\displaystyle n} tending to infinity. Like big-O notation, it is usually used to roughly convey the rate of growth of a function, such as the computational complexity of a particular algorithm.

Definition It is defined as

L n [ α , c ] = e ( c + o ( 1 ) ) ( ln ⁡ n ) α ( ln ⁡ ln ⁡ n ) 1 − α {\displaystyle L_{n}[\alpha ,c]=e^{(c+o(1))(\ln n)^{\alpha }(\ln \ln n)^{1-\alpha }}}

where c is a positive constant, and α {\displaystyle \alpha } is a constant 0 ≤ α ≤ 1 {\displaystyle 0\leq \alpha \leq 1} . L-notation is used mostly in computational number theory, to express the complexity of algorithms for difficult number theory problems, e.g. sieves for integer factorization and methods for solving discrete logarithms. The benefit of this notation is that it simplifies the analysis of these algorithms. The e c ( ln ⁡ n ) α ( ln ⁡ ln ⁡ n ) 1 − α {\displaystyle e^{c(\ln n)^{\alpha }(\ln \ln n)^{1-\alpha }}} expresses the dominant term, and the e o ( 1 ) ( ln ⁡ n ) α ( ln ⁡ ln ⁡ n ) 1 − α {\displaystyle e^{o(1)(\ln n)^{\alpha }(\ln \ln n)^{1-\alpha }}} takes care of everything smaller. When α {\displaystyle \alpha } is 0, then

L n [ α , c ] = L n [ 0 , c ] = e ( c + o ( 1 ) ) ln ⁡ ln ⁡ n = ( ln ⁡ n ) c + o ( 1 ) {\displaystyle L_{n}[\alpha ,c]=L_{n}[0,c]=e^{(c+o(1))\ln \ln n}=(\ln n)^{c+o(1)}\,}

is a polylogarithmic function (a polynomial function of ln n); When α {\displaystyle \alpha } is 1 then

L n [ α , c ] = L n [ 1 , c ] = e ( c + o ( 1 ) ) ln ⁡ n = n c + o ( 1 ) {\displaystyle L_{n}[\alpha ,c]=L_{n}[1,c]=e^{(c+o(1))\ln n}=n^{c+o(1)}\,}

is a fully exponential function of ln n (and thereby polynomial in n). If α {\displaystyle \alpha } is between 0 and 1, the function is subexponential of ln n (and superpolynomial).

Examples Many general-purpose integer factorization algorithms have subexponential time complexities. The best is the general number field sieve, which has an expected running time of

L n [ 1 / 3 , c ] = e ( c + o ( 1 ) ) ( ln ⁡ n ) 1 / 3 ( ln ⁡ ln ⁡ n ) 2 / 3 {\displaystyle L_{n}[1/3,c]=e^{(c+o(1))(\ln n)^{1/3}(\ln \ln n)^{2/3}}}

… excerpt ends here. Continue reading the full article.

Worked examples

Example 1 — a first encounter with L-notation

Start with the simplest possible case. Write down what L-notation 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 L-notation 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 L-notation 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 L-notation

In research
L-notation 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 L-notation 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
L-notation is common in secondary-school and first-year university syllabi. It links to neighbouring topics Asymptotic analysis, Computational complexity theory, so understanding it makes those chapters shorter.
In everyday life
Look for L-notation 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 L-notation in 20 minutes

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

Frequently asked questions

What is L-notation in simple terms?

L-notation is an asymptotic notation analogous to big-O notation, denoted as L n [ α , c ] {\displaystyle L_{n}[\alpha ,c]} for a bound variable n {\displaystyle n} tending to infinity. Like big-O notation, it is usually used to roughly convey the rate of growth of a function, such as the computati…

Why does L-notation 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 L-notation?

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 L-notation.

Tags

  • Asymptotic analysis
  • Computational complexity theory

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