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For any number a in this list, one can compute log 10 a. For example, log 10 10000 = 4, and log 10 0.001 = −3. These are instances of the discrete logarithm problem. Other base-10 logarithms in the real numbers are not instances of the discrete logarithm problem, because they involve non-integer exponents.
The identities of logarithms can be used to approximate large numbers. Note that log b (a) + log b (c) = log b (ac), where a, b, and c are arbitrary constants. Suppose that one wants to approximate the 44th Mersenne prime, 2 32,582,657 −1. To get the base-10 logarithm, we would multiply 32,582,657 by log 10 (2), getting 9,808,357.09543 ...
The discrete logarithm part of the computation took approximately 3100 core-years, using Intel Xeon Gold 6130 CPUs as a reference (2.1 GHz). The researchers estimate that improvements in the algorithms and software made this computation three times faster than would be expected from previous records after accounting for improvements in hardware.
The discrete logarithm is the integer n solving the equation =, where x is an element of the group. Carrying out the exponentiation can be done efficiently, but the discrete logarithm is believed to be very hard to calculate in some groups.
Log-likelihood; List of logarithmic identities; Logarithm of a matrix; Logarithm table; Logarithmic addition; Logarithmic convolution; Logarithmic decrement; Logarithmic differentiation; Logarithmic distribution; Logarithmic growth; Logarithmic number system; Logarithmic Sobolev inequalities; Logarithmus; Logarithmus binaris; Logarithmus ...
In computational number theory and computational algebra, Pollard's kangaroo algorithm (also Pollard's lambda algorithm, see Naming below) is an algorithm for solving the discrete logarithm problem. The algorithm was introduced in 1978 by the number theorist John M. Pollard , in the same paper as his better-known Pollard's rho algorithm for ...
The algorithm is performed in three stages. The first two stages depend only on the generator g and prime modulus q, and find the discrete logarithms of a factor base of r small primes. The third stage finds the discrete log of the desired number h in terms of the discrete logs of the factor base.
The hidden subgroup problem is especially important in the theory of quantum computing for the following reasons.. Shor's algorithm for factoring and for finding discrete logarithms (as well as several of its extensions) relies on the ability of quantum computers to solve the HSP for finite abelian groups.