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Super-recursive algorithm

Super-recursive 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 Super-recursive algorithm rather than just read about it. In short: In computability theory, super-recursive algorithms are posited as a generalization of hypercomputation: hypothetical algorithms that are more powerful, that is, compute more than Turing machines. The term was introduced by Mark Burgin, whose book Super-recursive algorithms develops their theory and presents several mathematical models.

Key takeaways

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

Reference excerpt

In computability theory, super-recursive algorithms are posited as a generalization of hypercomputation: hypothetical algorithms that are more powerful, that is, compute more than Turing machines. The term was introduced by Mark Burgin, whose book Super-recursive algorithms develops their theory and presents several mathematical models. Burgin argues that super-recursive algorithms can be used to disprove the Church–Turing thesis. This point of view has been criticized within the mathematical community and is not widely accepted.

Definition Burgin (2005: 13) uses the term recursive algorithms for algorithms that can be implemented on Turing machines, and uses the word algorithm in a more general sense. Then a super-recursive class of algorithms is "a class of algorithms in which it is possible to compute functions not computable by any Turing machine" (Burgin 2005: 107) Super-recursive algorithms are also related to algorithmic schemes, another novel concept from Burgin, which are more general than super-recursive algorithms. Burgin argues (2005: 115) that it is necessary to make a clear distinction between super-recursive algorithms and those algorithmic schemes that are not algorithms. Under this distinction, some types of hypercomputation are obtained by super-recursive algorithms.

Relation to the Church–Turing thesis The Church–Turing thesis in recursion theory relies on a particular definition of the term algorithm. Based on his personal definitions that are more general than the one commonly used in recursion theory, Burgin argues that super-recursive algorithms disprove the Church–Turing thesis. He furthermore claims to prove that super-recursive algorithms could hypothetically provide even greater efficiency gains than using quantum algorithms. Burgin's interpretation of super-recursive algorithms has encountered opposition in the mathematical community. One critic is logician Martin Davis, who argues that Burgin's claims have been well understood "for decades". Davis states,

"The present criticism is not about the mathematical discussion of these matters but only about the misleading claims regarding physical systems of the present and future."(Davis 2006: 128) Davis disputes Burgin's claims that sets at level Δ 2 0 {\displaystyle \Delta _{2}^{0}} of the arithmetical hierarchy can be called computable, saying

"It is generally understood that for a computational result to be useful one must be able to at least recognize that it is indeed the result sought." (Davis 2006: 128)

References Burgin, Mark (2005), Super-recursive algorithms, Monographs in computer science, Springer. ISBN 0-387-95569-0 Davis, Martin (2006), "The Church–Turing Thesis: Consensus and opposition". Proceedings, Computability in Europe 2006. Lecture notes in computer science, 3988 pp. 125–132 Peter Kugel, "It's time to think outside the computational box", Communications of the ACM, Volume 48, Issue 11, November 2005

External links A New Paradigm for Computation. Los Angeles ACM Chapter Meeting, December 1, 1999.

Worked examples

Example 1 — a first encounter with Super-recursive algorithm

Start with the simplest possible case. Write down what Super-recursive 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 Super-recursive 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 Super-recursive 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 Super-recursive algorithm

In research
Super-recursive 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 Super-recursive 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
Super-recursive algorithm is common in secondary-school and first-year university syllabi. It links to neighbouring topics Hypercomputation, so understanding it makes those chapters shorter.
In everyday life
Look for Super-recursive 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 Super-recursive algorithm in 20 minutes

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

Frequently asked questions

What is Super-recursive algorithm in simple terms?

In computability theory, super-recursive algorithms are posited as a generalization of hypercomputation: hypothetical algorithms that are more powerful, that is, compute more than Turing machines. The term was introduced by Mark Burgin, whose book Super-recursive algorithms develops their theory an…

Why does Super-recursive 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 Super-recursive 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 Super-recursive algorithm.

Tags

  • Hypercomputation

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