Bounds for parameters of codes¶
This module provided some upper and lower bounds for the parameters of codes.
AUTHORS:
David Joyner (2006-07): initial implementation.
William Stein (2006-07): minor editing of docs and code (fixed bug in elias_bound_asymp)
David Joyner (2006-07): fixed dimension_upper_bound to return an integer, added example to elias_bound_asymp.
” (2009-05): removed all calls to Guava but left it as an option.
Dima Pasechnik (2012-10): added LP bounds.
Let \(F\) be a finite set of size \(q\). A subset \(C\) of \(V=F^n\) is called a code of length \(n\). Often one considers the case where \(F\) is a finite field, denoted by \(\GF{q}\). Then \(V\) is an \(F\)-vector space. A subspace of \(V\) (with the standard basis) is called a linear code of length \(n\). If its dimension is denoted \(k\) then we typically store a basis of \(C\) as a \(k\times n\) matrix (the rows are the basis vectors). If \(F=\GF{2}\) then \(C\) is called a binary code. If \(F\) has \(q\) elements then \(C\) is called a \(q\)-ary code. The elements of a code \(C\) are called codewords. The information rate of \(C\) is
where \(\vert C\vert\) denotes the number of elements of \(C\). If \({\bf v}=(v_1,v_2,...,v_n)\), \({\bf w}=(w_1,w_2,...,w_n)\) are elements of \(V=F^n\) then we define
to be the Hamming distance between \({\bf v}\) and \({\bf w}\). The function \(d:V\times V\rightarrow \Bold{N}\) is called the Hamming metric. The weight of an element (in the Hamming metric) is \(d({\bf v},{\bf 0})\), where \(0\) is a distinguished element of \(F\); in particular it is \(0\) of the field if \(F\) is a field. The minimum distance of a linear code is the smallest non-zero weight of a codeword in \(C\). The relatively minimum distance is denoted
A linear code with length \(n\), dimension \(k\), and minimum distance \(d\) is called an \([n,k,d]_q\)-code and \(n,k,d\) are called its parameters. A (not necessarily linear) code \(C\) with length \(n\), size \(M=|C|\), and minimum distance \(d\) is called an \((n,M,d)_q\)-code (using parentheses instead of square brackets). Of course, \(k=\log_q(M)\) for linear codes.
What is the “best” code of a given length? Let \(A_q(n,d)\) denote the largest \(M\) such that there exists a \((n,M,d)\) code in \(F^n\). Let \(B_q(n,d)\) (also denoted \(A^{lin}_q(n,d)\)) denote the largest \(k\) such that there exists a \([n,k,d]\) code in \(F^n\). (Of course, \(A_q(n,d)\geq B_q(n,d)\).) Determining \(A_q(n,d)\) and \(B_q(n,d)\) is one of the main problems in the theory of error-correcting codes. For more details see [HP2003] and [Lin1999].
These quantities related to solving a generalization of the childhood game of “20 questions”.
GAME: Player 1 secretly chooses a number from \(1\) to \(M\) (\(M\) is large but fixed). Player 2 asks a series of “yes/no questions” in an attempt to determine that number. Player 1 may lie at most \(e\) times (\(e\geq 0\) is fixed). What is the minimum number of “yes/no questions” Player 2 must ask to (always) be able to correctly determine the number Player 1 chose?
If feedback is not allowed (the only situation considered here), call this minimum number \(g(M,e)\).
Lemma: For fixed \(e\) and \(M\), \(g(M,e)\) is the smallest \(n\) such that \(A_2(n,2e+1)\geq M\).
Thus, solving the solving a generalization of the game of “20 questions” is equivalent to determining \(A_2(n,d)\)! Using Sage, you can determine the best known estimates for this number in 2 ways:
- Indirectly, using best_known_linear_code_www(n, k, F),
which connects to the website http://www.codetables.de by Markus Grassl;
- codesize_upper_bound(n,d,q), dimension_upper_bound(n,d,q),
and best_known_linear_code(n, k, F).
The output of best_known_linear_code()
,
best_known_linear_code_www()
, or dimension_upper_bound()
would
give only special solutions to the GAME because the bounds are applicable
to only linear codes. The output of codesize_upper_bound()
would give
the best possible solution, that may belong to a linear or nonlinear code.
This module implements:
codesize_upper_bound(n,d,q), for the best known (as of May, 2006) upper bound A(n,d) for the size of a code of length n, minimum distance d over a field of size q.
dimension_upper_bound(n,d,q), an upper bound \(B(n,d)=B_q(n,d)\) for the dimension of a linear code of length n, minimum distance d over a field of size q.
gilbert_lower_bound(n,q,d), a lower bound for number of elements in the largest code of min distance d in \(\GF{q}^n\).
gv_info_rate(n,delta,q), \(log_q(GLB)/n\), where GLB is the Gilbert lower bound and delta = d/n.
gv_bound_asymp(delta,q), asymptotic analog of Gilbert lower bound.
plotkin_upper_bound(n,q,d)
plotkin_bound_asymp(delta,q), asymptotic analog of Plotkin bound.
griesmer_upper_bound(n,q,d)
elias_upper_bound(n,q,d)
elias_bound_asymp(delta,q), asymptotic analog of Elias bound.
hamming_upper_bound(n,q,d)
hamming_bound_asymp(delta,q), asymptotic analog of Hamming bound.
singleton_upper_bound(n,q,d)
singleton_bound_asymp(delta,q), asymptotic analog of Singleton bound.
mrrw1_bound_asymp(delta,q), “first” asymptotic McEliese-Rumsey-Rodemich-Welsh bound for the information rate.
Delsarte (a.k.a. Linear Programming (LP)) upper bounds.
PROBLEM: In this module we shall typically either (a) seek bounds on k, given n, d, q, (b) seek bounds on R, delta, q (assuming n is “infinity”).
Todo
Johnson bounds for binary codes.
mrrw2_bound_asymp(delta,q), “second” asymptotic McEliese-Rumsey-Rodemich-Welsh bound for the information rate.
-
sage.coding.code_bounds.
codesize_upper_bound
(n, d, q, algorithm=None)¶ Returns an upper bound on the number of codewords in a (possibly non-linear) code.
This function computes the minimum value of the upper bounds of Singleton, Hamming, Plotkin, and Elias.
If algorithm=”gap” then this returns the best known upper bound \(A(n,d)=A_q(n,d)\) for the size of a code of length n, minimum distance d over a field of size q. The function first checks for trivial cases (like d=1 or n=d), and if the value is in the built-in table. Then it calculates the minimum value of the upper bound using the algorithms of Singleton, Hamming, Johnson, Plotkin and Elias. If the code is binary, \(A(n, 2\ell-1) = A(n+1,2\ell)\), so the function takes the minimum of the values obtained from all algorithms for the parameters \((n, 2\ell-1)\) and \((n+1, 2\ell)\). This wraps GUAVA’s (i.e. GAP’s package Guava) UpperBound( n, d, q ).
If algorithm=”LP” then this returns the Delsarte (a.k.a. Linear Programming) upper bound.
EXAMPLES:
sage: codes.bounds.codesize_upper_bound(10,3,2) 93 sage: codes.bounds.codesize_upper_bound(24,8,2,algorithm="LP") 4096 sage: codes.bounds.codesize_upper_bound(10,3,2,algorithm="gap") # optional - gap_packages (Guava package) 85 sage: codes.bounds.codesize_upper_bound(11,3,4,algorithm=None) 123361 sage: codes.bounds.codesize_upper_bound(11,3,4,algorithm="gap") # optional - gap_packages (Guava package) 123361 sage: codes.bounds.codesize_upper_bound(11,3,4,algorithm="LP") 109226
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sage.coding.code_bounds.
dimension_upper_bound
(n, d, q, algorithm=None)¶ Return an upper bound for the dimension of a linear code.
Return an upper bound \(B(n,d) = B_q(n,d)\) for the dimension of a linear code of length n, minimum distance d over a field of size q.
Parameter “algorithm” has the same meaning as in
codesize_upper_bound()
EXAMPLES:
sage: codes.bounds.dimension_upper_bound(10,3,2) 6 sage: codes.bounds.dimension_upper_bound(30,15,4) 13 sage: codes.bounds.dimension_upper_bound(30,15,4,algorithm="LP") 12
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sage.coding.code_bounds.
elias_bound_asymp
(delta, q)¶ The asymptotic Elias bound for the information rate.
This only makes sense when \(0 < \delta < 1-1/q\).
EXAMPLES:
sage: codes.bounds.elias_bound_asymp(1/4,2) 0.39912396330...
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sage.coding.code_bounds.
elias_upper_bound
(n, q, d, algorithm=None)¶ Returns the Elias upper bound.
Returns the Elias upper bound for number of elements in the largest code of minimum distance \(d\) in \(\GF{q}^n\), cf. [HP2003]. If the method is “gap”, it wraps GAP’s
UpperBoundElias
.EXAMPLES:
sage: codes.bounds.elias_upper_bound(10,2,3) 232 sage: codes.bounds.elias_upper_bound(10,2,3,algorithm="gap") # optional - gap_packages (Guava package) 232
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sage.coding.code_bounds.
entropy
(x, q=2)¶ Computes the entropy at \(x\) on the \(q\)-ary symmetric channel.
INPUT:
x
- real number in the interval \([0, 1]\).q
- (default: 2) integer greater than 1. This is the base of the logarithm.
EXAMPLES:
sage: codes.bounds.entropy(0, 2) 0 sage: codes.bounds.entropy(1/5,4).factor() 1/10*(log(3) - 4*log(4/5) - log(1/5))/log(2) sage: codes.bounds.entropy(1, 3) log(2)/log(3)
Check that values not within the limits are properly handled:
sage: codes.bounds.entropy(1.1, 2) Traceback (most recent call last): ... ValueError: The entropy function is defined only for x in the interval [0, 1] sage: codes.bounds.entropy(1, 1) Traceback (most recent call last): ... ValueError: The value q must be an integer greater than 1
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sage.coding.code_bounds.
entropy_inverse
(x, q=2)¶ Find the inverse of the
q
-ary entropy function at the pointx
.INPUT:
x
– real number in the interval \([0, 1]\).q
- (default: 2) integer greater than 1. This is the base of the logarithm.
OUTPUT:
Real number in the interval \([0, 1-1/q]\). The function has multiple values if we include the entire interval \([0, 1]\); hence only the values in the above interval is returned.
EXAMPLES:
sage: from sage.coding.code_bounds import entropy_inverse sage: entropy_inverse(0.1) 0.012986862055... sage: entropy_inverse(1) 1/2 sage: entropy_inverse(0, 3) 0 sage: entropy_inverse(1, 3) 2/3
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sage.coding.code_bounds.
gilbert_lower_bound
(n, q, d)¶ Returns the Gilbert-Varshamov lower bound.
Returns the Gilbert-Varshamov lower bound for number of elements in a largest code of minimum distance d in \(\GF{q}^n\). See Wikipedia article Gilbert-Varshamov_bound
EXAMPLES:
sage: codes.bounds.gilbert_lower_bound(10,2,3) 128/7
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sage.coding.code_bounds.
griesmer_upper_bound
(n, q, d, algorithm=None)¶ Returns the Griesmer upper bound.
Returns the Griesmer upper bound for the number of elements in a largest linear code of minimum distance \(d\) in \(\GF{q}^n\), cf. [HP2003]. If the method is “gap”, it wraps GAP’s
UpperBoundGriesmer
.The bound states:
\[`n\geq \sum_{i=0}^{k-1} \lceil d/q^i \rceil.`\]EXAMPLES:
The bound is reached for the ternary Golay codes:
sage: codes.bounds.griesmer_upper_bound(12,3,6) 729 sage: codes.bounds.griesmer_upper_bound(11,3,5) 729
sage: codes.bounds.griesmer_upper_bound(10,2,3) 128 sage: codes.bounds.griesmer_upper_bound(10,2,3,algorithm="gap") # optional - gap_packages (Guava package) 128
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sage.coding.code_bounds.
gv_bound_asymp
(delta, q)¶ The asymptotic Gilbert-Varshamov bound for the information rate, R.
EXAMPLES:
sage: RDF(codes.bounds.gv_bound_asymp(1/4,2)) 0.18872187554086... sage: f = lambda x: codes.bounds.gv_bound_asymp(x,2) sage: plot(f,0,1) Graphics object consisting of 1 graphics primitive
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sage.coding.code_bounds.
gv_info_rate
(n, delta, q)¶ The Gilbert-Varshamov lower bound for information rate.
The Gilbert-Varshamov lower bound for information rate of a \(q\)-ary code of length \(n\) and minimum distance \(n\delta\).
EXAMPLES:
sage: RDF(codes.bounds.gv_info_rate(100,1/4,3)) # abs tol 1e-15 0.36704992608261894
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sage.coding.code_bounds.
hamming_bound_asymp
(delta, q)¶ The asymptotic Hamming bound for the information rate.
EXAMPLES:
sage: RDF(codes.bounds.hamming_bound_asymp(1/4,2)) 0.456435556800... sage: f = lambda x: codes.bounds.hamming_bound_asymp(x,2) sage: plot(f,0,1) Graphics object consisting of 1 graphics primitive
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sage.coding.code_bounds.
hamming_upper_bound
(n, q, d)¶ Returns the Hamming upper bound.
Returns the Hamming upper bound for number of elements in the largest code of length n and minimum distance d over alphabet of size q.
The Hamming bound (also known as the sphere packing bound) returns an upper bound on the size of a code of length \(n\), minimum distance \(d\), over an alphabet of size \(q\). The Hamming bound is obtained by dividing the contents of the entire Hamming space \(q^n\) by the contents of a ball with radius \(floor((d-1)/2)\). As all these balls are disjoint, they can never contain more than the whole vector space.
\[M \leq {q^n \over V(n,e)},\]where \(M\) is the maximum number of codewords and \(V(n,e)\) is equal to the contents of a ball of radius e. This bound is useful for small values of \(d\). Codes for which equality holds are called perfect. See e.g. [HP2003].
EXAMPLES:
sage: codes.bounds.hamming_upper_bound(10,2,3) 93
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sage.coding.code_bounds.
mrrw1_bound_asymp
(delta, q)¶ The first asymptotic McEliese-Rumsey-Rodemich-Welsh bound.
This only makes sense when \(0 < \delta < 1-1/q\).
EXAMPLES:
sage: codes.bounds.mrrw1_bound_asymp(1/4,2) # abs tol 4e-16 0.3545789026652697
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sage.coding.code_bounds.
plotkin_bound_asymp
(delta, q)¶ The asymptotic Plotkin bound for the information rate.
This only makes sense when \(0 < \delta < 1-1/q\).
EXAMPLES:
sage: codes.bounds.plotkin_bound_asymp(1/4,2) 1/2
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sage.coding.code_bounds.
plotkin_upper_bound
(n, q, d, algorithm=None)¶ Returns the Plotkin upper bound.
Returns the Plotkin upper bound for the number of elements in a largest code of minimum distance \(d\) in \(\GF{q}^n\). More precisely this is a generalization of Plotkin’s result for \(q=2\) to bigger \(q\) due to Berlekamp.
The
algorithm="gap"
option wraps Guava’sUpperBoundPlotkin
.EXAMPLES:
sage: codes.bounds.plotkin_upper_bound(10,2,3) 192 sage: codes.bounds.plotkin_upper_bound(10,2,3,algorithm="gap") # optional - gap_packages (Guava package) 192
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sage.coding.code_bounds.
singleton_bound_asymp
(delta, q)¶ The asymptotic Singleton bound for the information rate.
EXAMPLES:
sage: codes.bounds.singleton_bound_asymp(1/4,2) 3/4 sage: f = lambda x: codes.bounds.singleton_bound_asymp(x,2) sage: plot(f,0,1) Graphics object consisting of 1 graphics primitive
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sage.coding.code_bounds.
singleton_upper_bound
(n, q, d)¶ Returns the Singleton upper bound.
Returns the Singleton upper bound for number of elements in a largest code of minimum distance d in \(\GF{q}^n\).
This bound is based on the shortening of codes. By shortening an \((n, M, d)\) code \(d-1\) times, an \((n-d+1,M,1)\) code results, with \(M \leq q^n-d+1\). Thus
\[M \leq q^{n-d+1}.\]Codes that meet this bound are called maximum distance separable (MDS).
EXAMPLES:
sage: codes.bounds.singleton_upper_bound(10,2,3) 256
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sage.coding.code_bounds.
volume_hamming
(n, q, r)¶ Returns the number of elements in a Hamming ball.
Returns the number of elements in a Hamming ball of radius \(r\) in \(\GF{q}^n\).
EXAMPLES:
sage: codes.bounds.volume_hamming(10,2,3) 176