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Integer complexity

Integer complexity is a 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 Integer complexity rather than just read about it. In short: In number theory, the complexity of an integer is the smallest number of ones that can be used to represent it using ones and any number of additions, multiplications, and parentheses. It is always within a constant factor of the logarithm of the given integer.

Key takeaways

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

Reference excerpt

In number theory, the complexity of an integer is the smallest number of ones that can be used to represent it using ones and any number of additions, multiplications, and parentheses. It is always within a constant factor of the logarithm of the given integer.

Example For instance, the number 11 may be represented using eight ones:

11 = (1 + 1 + 1) × (1 + 1 + 1) + 1 + 1. However, it has no representation using seven or fewer ones. Therefore, its complexity is 8. The complexities of the numbers 1, 2, 3, ... are

1, 2, 3, 4, 5, 5, 6, 6, 6, 7, 8, 7, 8, 8, 8, 8, 9, 8, ... (sequence A005245 in the OEIS) The smallest numbers with complexity 1, 2, 3, ... are

1, 2, 3, 4, 5, 7, 10, 11, 17, 22, 23, 41, 47, ... (sequence A005520 in the OEIS)

Upper and lower bounds The question of expressing integers in this way was originally considered by Mahler & Popken (1953). They asked for the largest number with a given complexity k; later, Selfridge showed that this number is

2 x 3 ( k − 2 x ) / 3 where x = − k mod 3 . {\displaystyle 2^{x}3^{(k-2x)/3}{\text{ where }}x=-k{\bmod {3}}.}

For example, when k = 10, x = 2 and the largest integer that can be expressed using ten ones is 22 32 = 36. Its expression is

(1 + 1) × (1 + 1) × (1 + 1 + 1) × (1 + 1 + 1). Thus, the complexity of an integer n is at least 3 log3 n. The complexity of n is at most 3 log2 n (approximately 4.755 log3 n): an expression of this length for n can be found by applying Horner's method to the binary representation of n. Almost all integers have a representation whose length is bounded by a logarithm with a smaller constant factor, 3.529 log3 n.

Algorithms and counterexamples The complexity ‖ n ‖ {\displaystyle \|n\|} of every integer n up to some threshold N can be calculated in total time O ( N log O ( 1 ) ⁡ N ) {\displaystyle O(N\log ^{O(1)}N)} . The complexity of a single integer n {\displaystyle n} can be computed in sublinear time, O ( n 0.6514 ) {\displaystyle O(n^{0.6514})} . Algorithms for computing the integer complexity have been used to disprove several conjectures about the complexity. In particular, it is not necessarily the case that the optimal expression for a number n is obtained either by subtracting one from n or by expressing n as the product of two smaller factors. The smallest example of a number whose optimal expression is not of this form is 353942783. It is a prime number, and therefore also disproves a conjecture of Richard K. Guy that the complexity of every prime number p is one plus the complexity of p − 1. In fact,

‖ 353942783 ‖ = ‖ 353942782 ‖ = 63. {\displaystyle \|353942783\|=\|353942782\|=63.}

Moreover, Venecia Wang gave examples of other numbers whose integer complexity does not reflect the obvious formula for these numbers:

‖ 743 × 2 ‖ = ‖ 743 ‖ = 22 ‖ 166571 × 3 ‖ = ‖ 166571 ‖ = 39 ‖ 97103 × 5 ‖ = ‖ 97103 ‖ = 38 2 ‖ 23 ‖ > ‖ 23 2 ‖ = 20. {\displaystyle {\begin{aligned}\|743\times 2\|=\|743\|&=22\\\|166571\times 3\|=\|166571\|&=39\\\|97103\times 5\|=\|97103\|&=38\\2\|23\|>\|23^{2}\|&=20.\\\end{aligned}}}

References

External links Weisstein, Eric W., "Integer Complexity", MathWorld{{cite web}}: CS1 maint: overridden setting (link)

Worked examples

Example 1 — a first encounter with Integer complexity

Start with the simplest possible case. Write down what Integer complexity claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In 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 Integer complexity 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 Integer complexity 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 Integer complexity

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

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

Frequently asked questions

What is Integer complexity in simple terms?

In number theory, the complexity of an integer is the smallest number of ones that can be used to represent it using ones and any number of additions, multiplications, and parentheses. It is always within a constant factor of the logarithm of the given integer.

Why does Integer complexity matter?

Because it connects several 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 Integer complexity?

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 Integer complexity.

Tags

  • Integer sequences

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