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Lagrange polynomial

Lagrange polynomial is a 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 Lagrange polynomial rather than just read about it. In short: In numerical analysis, the Lagrange interpolating polynomial is the unique polynomial of lowest degree that interpolates a given set of data. Given a data set of coordinate pairs ⁠ ( x j , y j ) {\displaystyle \textstyle (x_{j},y_{j})} ⁠, the ⁠ x j {\displaystyle \textstyle x_{j}} ⁠ are called nodes and the ⁠ y j {\displaystyle \textstyle y_{j}} ⁠ are called values.

Lagrange polynomial — main illustration
Lagrange polynomial — illustration

Key takeaways

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

Reference excerpt

In numerical analysis, the Lagrange interpolating polynomial is the unique polynomial of lowest degree that interpolates a given set of data. Given a data set of coordinate pairs ⁠ ( x j , y j ) {\displaystyle \textstyle (x_{j},y_{j})} ⁠, the ⁠ x j {\displaystyle \textstyle x_{j}} ⁠ are called nodes and the ⁠ y j {\displaystyle \textstyle y_{j}} ⁠ are called values. The Lagrange polynomial ⁠ L ( x ) {\displaystyle L(x)} ⁠ which interpolates the data assumes each value at the corresponding node, ⁠ L ( x j ) = y j {\displaystyle \textstyle L(x_{j})=y_{j}} ⁠. If there are ⁠ k + 1 {\displaystyle k+1} ⁠ data pairs, the Lagrange polynomial has degree ⁠ ≤ k {\displaystyle \leq k} ⁠. Although named after Joseph-Louis Lagrange, who published it in 1795, the method was first discovered in 1779 by Edward Waring. It is also an easy consequence of a formula published in 1783 by Leonhard Euler. Uses of Lagrange polynomials include the Newton–Cotes method of numerical integration, Shamir's secret sharing scheme in cryptography, and Reed–Solomon error correction in coding theory. For equispaced nodes, Lagrange interpolation is susceptible to Runge's phenomenon of large oscillation.

Definition Given a set of ⁠ k + 1 {\displaystyle k+1} ⁠ nodes { x 0 , x 1 , … , x k } {\displaystyle \{x_{0},x_{1},\ldots ,x_{k}\}} , which must all be distinct, ⁠ x j ≠ x m {\displaystyle \textstyle x_{j}\neq x_{m}} ⁠ for indices ⁠ j ≠ m {\displaystyle j\neq m} ⁠, the Lagrange basis for polynomials of degree ⁠ ≤ k {\displaystyle \leq k} ⁠ for those nodes is the set of polynomials { ℓ 0 ( x ) , ℓ 1 ( x ) , … , ℓ k ( x ) } {\displaystyle \textstyle \{\ell _{0}(x),\ell _{1}(x),\ldots ,\ell _{k}(x)\}} each of degree ⁠ k {\displaystyle k} ⁠ which take values ⁠ ℓ j ( x m ) = 0 {\displaystyle \textstyle \ell _{j}(x_{m})=0} ⁠ if ⁠ m ≠ j {\displaystyle m\neq j} ⁠ and ⁠ ℓ j ( x j ) = 1 {\displaystyle \textstyle \ell _{j}(x_{j})=1} ⁠. Using the Kronecker delta this can be written ⁠ ℓ j ( x m ) = δ j m {\displaystyle \textstyle \ell _{j}(x_{m})=\delta _{jm}} ⁠. Each basis polynomial can be explicitly described by the product:

… excerpt ends here. Continue reading the full article.

Illustrations

Lagrange polynomial: This image shows, for four data points (@media screen{html.skin-theme-clientpref-night .mw-parser-output div:not(.notheme)>.tmp-color,html.skin-theme-clientpref-night .mw-parser-output p>.tmp-color,html.skin-theme-clientpref-night .mw-parser-output table:not(.notheme) .tmp-color{color:inherit!important}}@media screen and (prefers-color-scheme:dark){html.skin-theme-clientpref-os .mw-parser-output div:not(.notheme)>.tmp-color,html.skin-theme-clientpref-os .mw-parser-output p>.tmp-color,html.skin-theme-clientpref-os .mw-parser-output table:not(.notheme) .tmp-color{color:inherit!important}}(−9, 5), (−4, 2), (−1, −2), (7, 9)), the (cubic) interpolation polynomial L(x) (dashed, black), which is the sum of the scaled basis polynomials y0ℓ0(x), y1ℓ1(x), y2ℓ2(x) and y3ℓ3(x). The interpolation polynomial passes through all four control points, and each scaled basis polynomial passes through its respective control point and is 0 where x corresponds to the other three control points.
This image shows, for four data points (@media screen{html.skin-theme-clientpref-night .mw-parser-output div:not(.notheme)>.tmp-color,html.skin-theme-clientpref-night .mw-parser-output p>.tmp-color,html.skin-theme-clientpref-night .mw-parser-output table:not(.notheme) .tmp-color{color:inherit!important}}@media screen and (prefers-color-scheme:dark){html.skin-theme-clientpref-os .mw-parser-output div:not(.notheme)>.tmp-color,html.skin-theme-clientpref-os .mw-parser-output p>.tmp-color,html.skin-theme-clientpref-os .mw-parser-output table:not(.notheme) .tmp-color{color:inherit!important}}(−9, 5), (−4, 2), (−1, −2), (7, 9)), the (cubic) interpolation polynomial L(x) (dashed, black), which is the sum of the scaled basis polynomials y0ℓ0(x), y1ℓ1(x), y2ℓ2(x) and y3ℓ3(x). The interpolation polynomial passes through all four control points, and each scaled basis polynomial passes through its respective control point and is 0 where x corresponds to the other three control points.
Lagrange polynomial: Example of interpolation divergence for a set of Lagrange polynomials.
Example of interpolation divergence for a set of Lagrange polynomials.

Worked examples

Example 1 — a first encounter with Lagrange polynomial

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

In research
Lagrange polynomial appears in 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 Lagrange polynomial 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
Lagrange polynomial is common in secondary-school and first-year university syllabi. It links to neighbouring topics Interpolation, Polynomials, so understanding it makes those chapters shorter.
In everyday life
Look for Lagrange polynomial 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 Lagrange polynomial in 20 minutes

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

Frequently asked questions

What is Lagrange polynomial in simple terms?

In numerical analysis, the Lagrange interpolating polynomial is the unique polynomial of lowest degree that interpolates a given set of data. Given a data set of coordinate pairs ⁠ ( x j , y j ) {\displaystyle \textstyle (x_{j},y_{j})} ⁠, the ⁠ x j {\displaystyle \textstyle x_{j}} ⁠ are called node…

Why does Lagrange polynomial matter?

Because it connects several 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 Lagrange polynomial?

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 Lagrange polynomial.

Tags

  • Interpolation
  • Polynomials

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