computer science

KISS (algorithm)

KISS (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 KISS (algorithm) rather than just read about it. In short: KISS (Keep it Simple Stupid) is a family of pseudorandom number generators introduced by George Marsaglia. Starting from 1998 Marsaglia posted on various newsgroups including sci.math, comp.lang.c, comp.lang.fortran and sci.stat.math several versions of the generators.

Key takeaways

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

Reference excerpt

KISS (Keep it Simple Stupid) is a family of pseudorandom number generators introduced by George Marsaglia. Starting from 1998 Marsaglia posted on various newsgroups including sci.math, comp.lang.c, comp.lang.fortran and sci.stat.math several versions of the generators. All KISS generators combine three or four independent random number generators with a view to improving the quality of randomness. KISS generators produce 32-bit or 64-bit random integers, from which random floating-point numbers can be constructed if desired. The original 1993 generator is based on the combination of a linear congruential generator and of two linear feedback shift-register generators. It has a period 295, good speed and good statistical properties; however, it fails the LinearComplexity test in the Crush and BigCrush tests of the TestU01 suite. A newer version from 1999 is based on a linear congruential generator, a 3-shift linear feedback shift-register and two multiply-with-carry generators. It is 10–20% slower than the 1993 version but has a larger period 2123 and passes all tests in TestU01. In 2009 Marsaglia presented a version based on 64-bit integers (appropriate for 64-bit processors) which combines a multiply-with-carry generator, a Xorshift generator and a linear congruential generator. It has a period of around 2250 (around 1075).

References

Further reading Bucklew, James (2013). "1.1 Uniform Generators". Introduction to Rare Event Simulation. Springer. pp. 1–8. ISBN 978-1-4757-4078-3. Robert, Christian; George Casella (2013). "2.1.2 The Kiss Generator". Monte Carlo Statistical Methods. Springer. pp. 39–43. ISBN 978-1-4757-3071-5. Rose, Gregory G. (2017). "KISS: A bit too simple". Cryptography and Communications. 10: 123–137. doi:10.1007/s12095-017-0225-x. ISSN 1936-2447.

Worked examples

Example 1 — a first encounter with KISS (algorithm)

Start with the simplest possible case. Write down what KISS (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 KISS (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 KISS (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 KISS (algorithm)

In research: KISS (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 KISS (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: KISS (algorithm) is common in secondary-school and first-year university syllabi. It links to neighbouring topics Pseudorandom number generators, so understanding it makes those chapters shorter.
In everyday life: Look for KISS (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 KISS (algorithm) in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what KISS (algorithm) 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 KISS (algorithm) out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is KISS (algorithm) in simple terms?

Why does KISS (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 KISS (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 KISS (algorithm).