Golomb-Dickman Constant Approximation
Bounded off line reference: Golomb-Dickman constant bounded definition/value inventory. Topics: definition, approximate-value. Not live/exhaustive reference, a proof of an open assertion, diagnosis or safe-use approval.
Description
The Golomb-Dickman constant describes a limiting normalized largest-cycle mean for uniform random permutations.
The Golomb-Dickman constant describes a limiting normalized largest-cycle mean for uniform random permutations.1
This tool returns a stored definition or rounded value without sampling permutations.
When to use Golomb-Dickman Constant Approximation
Read a bounded educational reference for one of the supported topics.
How to use Golomb-Dickman Constant Approximation and how it works
- Choose a supported exact topic from the table below.
- Enter that topic token and run the tool.
- Read result.text and its scope or precision limitations.
- Correct an unsupported token before rerunning; use an independently applicable reference when more detail or current evidence is required.
| Input field | Meaning | Value type | Requirement |
|---|---|---|---|
| topic | Topic. Use the units and matching context stated in the calculation below. | string | Required; other constraints below |
Choose one exact case-sensitive topic: definition, approximate-value. The implementation matches that token to a stored text record and returns result.text unchanged. Unknown tokens, blank strings and wrong input types are errors; no case folding or live lookup occurs. These are bounded educational records, not a calculation from observations, a proof search, a patient assessment or an arbitrary-precision value. The supported topic inventory below identifies the available records.
text=storedInventory[exactTopic] Apply the input ordering, units and branch conditions stated above.
| Exact topic token | Record selected |
|---|---|
| definition | Stored definition reference. |
| approximate-value | Stored approximate-value reference. |
| Output field | Meaning | Value type |
|---|---|---|
| result | Object containing the calculated or selected fields below. | object |
| result.text | Exact stored educational record selected by the supported topic token. | string |
{
"input": {
"topic": "definition"
},
"output": {
"result": {
"text": "Golomb-Dickman constant: lim mean normalized largest cycle of uniform random permutation."
}
}
}Limitations and assumptions
This is a stored text inventory. It does not calculate from measurements, obtain live data, prove an infinite assertion, classify a patient or certify arbitrary precision.
All fields require explicit values. Unexpected fields, wrong types, nonfinite numbers and sparse arrays are rejected. Alignment, bounds and supported-token rules are explained above; extremely large inputs or nonfinite results can also fail. Input errors differ from successful false comparisons, unavailable-domain flags and empty match lists.
Alternative or Complementary methods
Use Viswanath Constant Approximation for a complementary operation: The Viswanath constant describes a growth rate for a specified random Fibonacci recurrence.
Use Khinchin-Levy Constant Approximation for a complementary operation: The Levy constant describes almost-everywhere continued-fraction denominator growth.
References
-
Golomb-Dickman Constant Approximation — tool.io
Similar or alternative tools
- Viswanath Constant Approximation
The Viswanath constant describes a growth rate for a specified random Fibonacci recurrence.
- Khinchin-Levy Constant Approximation
The Levy constant describes almost-everywhere continued-fraction denominator growth.