In applied mathematics, the Johnson bound (named after Selmer Martin Johnson) is a limit on the size of error-correcting codes, as used in coding theory for data transmission or communications.
Let be a q-ary code of length , i.e. a subset of . Let be the minimum distance of , i.e.
where is the Hamming distance between and .
Let be the set of all q-ary codes with length and minimum distance and let denote the set of codes in such that every element has exactly nonzero entries.
Denote by the number of elements in . Then, we define to be the largest size of a code with length and minimum distance :
Similarly, we define to be the largest size of a code in :
Theorem 1 (Johnson bound for ):
If ,
If ,
Theorem 2 (Johnson bound for ):
(i) If
(ii) If , then define the variable as follows. If is even, then define through the relation ; if is odd, define through the relation . Let . Then,
where is the floor function.
Remark: Plugging the bound of Theorem 2 into the bound of Theorem 1 produces a numerical upper bound on .
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In the mathematics of coding theory, the Griesmer bound, named after James Hugo Griesmer, is a bound on the length of linear binary codes of dimension k and minimum distance d. There is also a very similar version for non-binary codes. For a binary linear code, the Griesmer bound is: Let denote the minimum length of a binary code of dimension k and distance d. Let C be such a code. We want to show that Let G be a generator matrix of C. We can always suppose that the first row of G is of the form r = (1, ...
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