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Euler's totient or phi function, φ(n) is an arithmetic function that counts the number of positive integers less than or equal to n that are relatively prime to n. That is, if n is a positive integer , then φ( n ) is the number of integers k in the range 1 ≤ k ≤ n which have no common factor with n other than 1.
In mathematics, the prime-counting function is the function counting the number of prime numbers less than or equal to some real number x. [1] [2] It is denoted by π(x) (unrelated to the number π). A symmetric variant seen sometimes is π 0 (x), which is equal to π(x) − 1 ⁄ 2 if x is exactly a prime number, and equal to π(x) otherwise.
The birthday problem in this more generic sense applies to hash functions: the expected number of N-bit hashes that can be generated before getting a collision is not 2 N, but rather only 2 N ⁄ 2. This is exploited by birthday attacks on cryptographic hash functions and is the reason why a small number of collisions in a hash table are, for ...
In contrast, no renaming of (x 1 ∨ ¬x 2 ∨ ¬x 3) ∧ (¬x 1 ∨ x 2 ∨ x 3) ∧ ¬x 1 leads to a Horn formula. Checking the existence of such a replacement can be done in linear time; therefore, the satisfiability of such formulae is in P as it can be solved by first performing this replacement and then checking the satisfiability of the ...
Use the special buttons ± and 1/x, that do not correspond to operations in the formula, for non-commutative operators. Mistakes can be hard to spot because: For the above reasons, the sequence of button presses may bear little resemblance to the original formula.
The first such distribution found is π(N) ~ N / log(N) , where π(N) is the prime-counting function (the number of primes less than or equal to N) and log(N) is the natural logarithm of N. This means that for large enough N, the probability that a random integer not greater than N is prime is very close to 1 / log(N).
For a value that is sampled with an unbiased normally distributed error, the above depicts the proportion of samples that would fall between 0, 1, 2, and 3 standard deviations above and below the actual value.
The optimal value depends on excess kurtosis, as discussed in mean squared error: variance; for the normal distribution this is optimized by dividing by n + 1 (instead of n − 1 or n). Thirdly, Bessel's correction is only necessary when the population mean is unknown, and one is estimating both population mean and population variance from a ...