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Lehmer–Schur algorithm

Lehmer–Schur algorithm 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 Lehmer–Schur algorithm rather than just read about it. In short: In mathematics, the Lehmer–Schur algorithm (named after Derrick Henry Lehmer and Issai Schur) is a root-finding algorithm for complex polynomials, extending the idea of enclosing roots like in the one-dimensional bisection method to the complex plane. It uses the Schur-Cohn test to test increasingly smaller disks for the presence or absence of roots.

Key takeaways

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

Reference excerpt

In mathematics, the Lehmer–Schur algorithm (named after Derrick Henry Lehmer and Issai Schur) is a root-finding algorithm for complex polynomials, extending the idea of enclosing roots like in the one-dimensional bisection method to the complex plane. It uses the Schur-Cohn test to test increasingly smaller disks for the presence or absence of roots.

Schur-Cohn algorithm This algorithm allows one to find the distribution of the roots of a complex polynomial with respect to the unit circle in the complex plane. It is based on two auxiliary polynomials, introduced by Schur. For a complex polynomial p {\displaystyle p} of degree n {\displaystyle n} its reciprocal adjoint polynomial p ∗ {\displaystyle p^{*}} is defined by p ∗ ( z ) = z n p ( z ¯ − 1 ) ¯ {\displaystyle p^{*}(z)=z^{n}{\overline {p({\bar {z}}^{-1})}}} and its Schur Transform T p {\displaystyle Tp} by

T p = p ( 0 ) ¯ p − p ∗ ( 0 ) ¯ p ∗ , {\displaystyle Tp={\overline {p(0)}}p-{\overline {p^{*}(0)}}p^{*},}

where a bar denotes complex conjugation. So, if p ( z ) = a n z n + ⋯ + a 1 z + a 0 {\displaystyle p(z)=a_{n}z^{n}+\cdots +a_{1}z+a_{0}} with a n ≠ 0 {\displaystyle a_{n}\neq 0} , then

p ∗ ( z ) = a ¯ 0 z n + a ¯ 1 z n − 1 + ⋯ + a ¯ n {\displaystyle p^{*}(z)={\bar {a}}_{0}z^{n}+{\bar {a}}_{1}z^{n-1}+\cdots +{\bar {a}}_{n}} , with leading zero-terms, if any, removed. The coefficients of T p {\displaystyle Tp} can therefore be directly expressed in those of p {\displaystyle p} and, since one or more leading coefficients cancel, T p {\displaystyle Tp} has lower degree than p {\displaystyle p} . The roots of p {\displaystyle p} , p ∗ {\displaystyle p^{*}} , and T p {\displaystyle Tp} are related as follows.

Lemma Let p {\displaystyle p} be a complex polynomial and δ = ( T p ) ( 0 ) {\displaystyle \delta =(Tp)(0)} .

… excerpt ends here. Continue reading the full article.

Worked examples

Example 1 — a first encounter with Lehmer–Schur algorithm

Start with the simplest possible case. Write down what Lehmer–Schur algorithm 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 Lehmer–Schur algorithm 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 Lehmer–Schur algorithm 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 Lehmer–Schur algorithm

In research
Lehmer–Schur algorithm 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 Lehmer–Schur algorithm 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
Lehmer–Schur algorithm is common in secondary-school and first-year university syllabi. It links to neighbouring topics Polynomial factorization algorithms, so understanding it makes those chapters shorter.
In everyday life
Look for Lehmer–Schur algorithm 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 Lehmer–Schur algorithm in 20 minutes

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

Frequently asked questions

What is Lehmer–Schur algorithm in simple terms?

In mathematics, the Lehmer–Schur algorithm (named after Derrick Henry Lehmer and Issai Schur) is a root-finding algorithm for complex polynomials, extending the idea of enclosing roots like in the one-dimensional bisection method to the complex plane. It uses the Schur-Cohn test to test increasingl…

Why does Lehmer–Schur algorithm 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 Lehmer–Schur algorithm?

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 Lehmer–Schur algorithm.

Tags

  • Polynomial factorization algorithms

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