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Nati Linial

Nati Linial 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 Nati Linial rather than just read about it. In short: Nathan (Nati) Linial (Hebrew: נתן (נתי) ליניאל; born 1953 in Haifa, Israel) is an Israeli mathematician and computer scientist, a professor in the Rachel and Selim Benin School of Computer Science and Engineering at the Hebrew University of Jerusalem, and an ISI highly cited researcher. Linial did his undergraduate studies at the Technion, and received his PhD in 1978 from the Hebrew University under the supervision…

Nati Linial — main illustration
Nati Linial — illustration

Key takeaways

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

Reference excerpt

Nathan (Nati) Linial (Hebrew: נתן (נתי) ליניאל; born 1953 in Haifa, Israel) is an Israeli mathematician and computer scientist, a professor in the Rachel and Selim Benin School of Computer Science and Engineering at the Hebrew University of Jerusalem, and an ISI highly cited researcher. Linial did his undergraduate studies at the Technion, and received his PhD in 1978 from the Hebrew University under the supervision of Micha Perles. He was a postgraduate researcher at the University of California, Los Angeles before returning to the Hebrew University as a faculty member. In 2012 he became a fellow of the American Mathematical Society. In 2019 he won the FOCS Test of Time Award for the paper "Constant Depth Circuits, Fourier Transform, and Learnability", co-authored with Yishay Mansour and Noam Nisan.

Selected publications Linial, Nati (1992), "Locality in Distributed Graph Algorithms", SIAM J. Comput., 21 (1): 193–201, CiteSeerX 10.1.1.471.6378, doi:10.1137/0221015 {{citation}}: Cite uses deprecated parameter |citeseerx= (help). The paper won the 2013 Dijkstra Prize. In the words of the prize committee: "This paper has had a major impact on distributed message-passing algorithms. It focused a spotlight on the notion of locality in distributed computation and raised interesting questions concerning the locality level of various distributed problems, in terms of their time complexity on different classes of networks. Towards that goal, in this paper, Linial developed a model particularly suitable for studying locality, which ignores message sizes, asynchrony and failures. This clean model allowed researchers to isolate the effects of locality and study the roles of distances and neighborhoods, as graph theoretic notions, and their interrelations with algorithmic and complexity-theoretic problems in distributed computing." Borodin, Allan; Linial, Nathan; Saks, Michael E. (1992), "An optimal on-line algorithm for metrical task system", J. ACM, 39 (4): 745–763, doi:10.1145/146585.146588, S2CID 18783826. This paper on competitive analysis of online algorithms studies metrical task systems, a very general model of tasks where decisions on how to service a sequence of requests must be made without knowledge of future requests. It introduces the metrical task system model, describes how to use it to model various scheduling problems, and develops an algorithm that in many situations can be shown to perform optimally. Linial, Nathan; Mansour, Yishay; Nisan, Noam (1993), "Constant depth circuits, Fourier transform, and learnability", J. ACM, 40 (3): 607–620, doi:10.1145/174130.174138, S2CID 16978276. By performing harmonic analysis on functions in the complexity class AC0 (a class representing highly parallelizable computational problems), Linial and his co-authors show that these functions behave poorly as pseudorandom number generators, can be approximated well by polynomials, and can be learned efficiently by machine learning systems. Linial, Nathan; London, Eran; Rabinovich, Yuri (1995), "The geometry of graphs and some of its algorithmic applications", Combinatorica, 15 (2): 215–245, doi:10.1007/BF01200757, S2CID 5071936. Linial's most-cited paper according to Google scholar, this paper explores connections between graph-theoretic problems such as the multi-commodity flow problem and low-distortion embeddings of metric spaces into low-dimensional spaces such as those given by the Johnson–Lindenstrauss lemma. Hoory, Shlomo; Linial, Nathan; Wigderson, Avi (2006), "Expander graphs and their applications", Bulletin of the American Mathematical Society, 43 (4): 439–561, doi:10.1090/S0273-0979-06-01126-8, MR 2247919. In 2008 Linial and his co-authors won the Levi L. Conant Prize of the American Mathematical Society for best mathematical exposition for this article, a survey on expander graphs.

References

Illustrations

Nati Linial illustration

Worked examples

Example 1 — a first encounter with Nati Linial

Start with the simplest possible case. Write down what Nati Linial 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 Nati Linial 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 Nati Linial 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 Nati Linial

In research
Nati Linial 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 Nati Linial 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
Nati Linial is common in secondary-school and first-year university syllabi. It links to neighbouring topics 1953 births, Academic staff of the Hebrew University of Jerusalem, Combinatorialists, so understanding it makes those chapters shorter.
In everyday life
Look for Nati Linial 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 Nati Linial in 20 minutes

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

Frequently asked questions

What is Nati Linial in simple terms?

Nathan (Nati) Linial (Hebrew: נתן (נתי) ליניאל; born 1953 in Haifa, Israel) is an Israeli mathematician and computer scientist, a professor in the Rachel and Selim Benin School of Computer Science and Engineering at the Hebrew University of Jerusalem, and an ISI highly cited researcher. Linial did…

Why does Nati Linial 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 Nati Linial?

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 Nati Linial.

Tags

  • 1953 births
  • Academic staff of the Hebrew University of Jerusalem
  • Combinatorialists
  • Dijkstra Prize laureates
  • Einstein Institute of Mathematics alumni
  • Fellows of the American Mathematical Society
  • Hebrew Reali School alumni
  • Israeli mathematicians
  • Israeli theoretical computer scientists
  • Living people
  • Technion – Israel Institute of Technology alumni

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