computer science

Slowsort

Slowsort 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 Slowsort rather than just read about it. In short: Slowsort is a sorting algorithm. It is of humorous nature and not useful for practical applications.

Key takeaways

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

Reference excerpt

Slowsort is a sorting algorithm. It is of humorous nature and not useful for practical applications. It is a reluctant algorithm based on the principle of multiply and surrender (a parody formed by taking the opposites of divide and conquer). It was published in 1984 by Andrei Broder and Jorge Stolfi in their paper "Pessimal Algorithms and Simplexity Analysis" (a parody of optimal algorithms and complexity analysis).

Algorithm Slowsort is a recursive algorithm.

It sorts in-place. It is an unstable sort. (It might change the order of equal-valued keys.) A pseudocode implementation is given below:

Sort the first half, recursively. (1.1) Sort the second half, recursively. (1.2) Find the maximum of the whole array by comparing the results of 1.1 and 1.2, and place it at the end of the list. (1.3) Sort the entire list (except for the maximum now at the end), recursively. (2) An unoptimized implementation in Haskell (purely functional) may look as follows:

Complexity Analysis The time complexity of Slowsort is given by the function T ( n ) = 2 T ( n / 2 ) + T ( n − 1 ) + 1 {\displaystyle T(n)=2T(n/2)+T(n-1)+1} . It can be found by creating a recurrence relation of the initial recursive calls (1.1) and (1.2) respectively and summing the final recursive call (2) and modelling the other operations as a constant (+1) in this case. This gives a lower asymptotic bound for T ( n ) {\displaystyle T(n)} , which in Landau notation is given as Ω ( n log 2 ⁡ ( n ) / ( 2 + ϵ ) ) {\displaystyle \Omega \left(n^{\log _{2}(n)/(2+\epsilon )}\right)} for any ϵ > 0 {\displaystyle \epsilon >0} . Therefore Slowsort is not in polynomial time.

References

Worked examples

Example 1 — a first encounter with Slowsort

Start with the simplest possible case. Write down what Slowsort 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 Slowsort 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 Slowsort 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 Slowsort

In research: Slowsort 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 Slowsort 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: Slowsort is common in secondary-school and first-year university syllabi. It links to neighbouring topics Sorting algorithms, so understanding it makes those chapters shorter.
In everyday life: Look for Slowsort 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.

Affiliate

Preply — study more efficiently by working with a personal tutor. 50% off.

How to study Slowsort in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Slowsort means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Slowsort out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Slowsort in simple terms?

Slowsort is a sorting algorithm. It is of humorous nature and not useful for practical applications.

Why does Slowsort 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 Slowsort?

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 Slowsort.