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Multiply-with-carry pseudorandom number generator

Multiply-with-carry pseudorandom number generator 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 Multiply-with-carry pseudorandom number generator rather than just read about it. In short: In computer science, multiply-with-carry (MWC) is a method invented by George Marsaglia for generating sequences of random integers based on an initial set from two to many thousands of randomly chosen seed values. It involves simple computational integer-arithmetic, and leads to high-speed generation of sequences of random numbers with immense periods (ranging from around 2 60 {\displaystyle 2^{60}} to 2 2 , 000…

Key takeaways

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

Reference excerpt

In computer science, multiply-with-carry (MWC) is a method invented by George Marsaglia for generating sequences of random integers based on an initial set from two to many thousands of randomly chosen seed values. It involves simple computational integer-arithmetic, and leads to high-speed generation of sequences of random numbers with immense periods (ranging from around 2 60 {\displaystyle 2^{60}} to 2 2 , 000 , 000 {\displaystyle 2^{2,000,000}} ); those are its main advantages. As with all pseudorandom number generators, the resulting sequences are functions of the supplied seed values.

General theory An MWC generator is a special form of Lehmer random number generator x n = b x n − 1 mod p {\displaystyle x_{n}=bx_{n-1}{\bmod {p}}} which allows efficient implementation of a prime modulus p {\displaystyle p} much larger than the machine word size. Normal Lehmer generator implementations choose a modulus close to the machine word size. An MWC generator instead maintains its state in base b {\displaystyle b} , so multiplying by b {\displaystyle b} is done implicitly by shifting one word. The base b {\displaystyle b} is typically chosen to equal the computer's word size, as this makes arithmetic modulo b {\displaystyle b} trivial. This may vary from b = 2 8 {\displaystyle b=2^{8}} for a microcontroller to b = 2 64 {\displaystyle b=2^{64}} . (This article uses b = 2 32 {\displaystyle b=2^{32}} for examples.) The initial state ("seed") values are arbitrary, except that they must not be all zero, nor all at the maximum permitted values ( x 0 = b − 1 {\displaystyle x_{0}=b-1} and c 0 = a − 1 {\displaystyle c_{0}=a-1} ). (This is commonly done by choosing c 0 {\displaystyle c_{0}} between 1 and a − 2 {\displaystyle a-2} .). The MWC sequence is then a sequence of pairs x n , c n {\displaystyle x_{n},c_{n}} determined by

x n = ( a x n − 1 + c n − 1 ) mod b , c n = ⌊ a x n − 1 + c n − 1 b ⌋ {\displaystyle x_{n}=(ax_{n-1}+c_{n-1})\,{\bmod {\,}}b,\ c_{n}=\left\lfloor {\frac {ax_{n-1}+c_{n-1}}{b}}\right\rfloor }

This is called a lag-1 MWC sequence. Sometimes an odd base is preferred, in which case b = 2 k − 1 {\displaystyle b=2^{k}-1} can be used, which is almost as simple to implement. A lag- r {\displaystyle r} sequence is a generalization of the lag-1 sequence allowing longer periods. The lag- r {\displaystyle r} MWC sequence is then a sequence of pairs

x n , c n {\displaystyle x_{n},c_{n}} (for n > r {\displaystyle n>r} ) determined by

… excerpt ends here. Continue reading the full article.

Worked examples

Example 1 — a first encounter with Multiply-with-carry pseudorandom number generator

Start with the simplest possible case. Write down what Multiply-with-carry pseudorandom number generator 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 Multiply-with-carry pseudorandom number generator 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 Multiply-with-carry pseudorandom number generator 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 Multiply-with-carry pseudorandom number generator

In research
Multiply-with-carry pseudorandom number generator 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 Multiply-with-carry pseudorandom number generator 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
Multiply-with-carry pseudorandom number generator 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 Multiply-with-carry pseudorandom number generator 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 Multiply-with-carry pseudorandom number generator in 20 minutes

  1. Read the reference excerpt below once, without taking notes.
  2. Close the page and write down what Multiply-with-carry pseudorandom number generator 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 Multiply-with-carry pseudorandom number generator out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Multiply-with-carry pseudorandom number generator in simple terms?

In computer science, multiply-with-carry (MWC) is a method invented by George Marsaglia for generating sequences of random integers based on an initial set from two to many thousands of randomly chosen seed values. It involves simple computational integer-arithmetic, and leads to high-speed generat…

Why does Multiply-with-carry pseudorandom number generator 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 Multiply-with-carry pseudorandom number generator?

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 Multiply-with-carry pseudorandom number generator.

Tags

  • Pseudorandom number generators

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