Delsarte s linear programming bound

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15-859 Coding Theory, Fall 14 December 5, 2014

Introduction For all n, q, and d, Delsarte s linear program establishes a series of linear constraints that every code in F n q with distance d must satisfy. We want to maximize the size of the code, subject to these linear constraints. Together, the constraints and the objective function form a linear program. Solving this linear program gives an upper bound on the size of a code in F n q with distance d.

Contents Preliminaries Association schemes, and the Hamming scheme Associate matrices, and the Bose-Mesner algebra Distribution vectors The linear programming bound Formulation Numerical results for small n Asymptotic lower and upper bounds Open problems

Association schemes Definition A symmetric association scheme A = {X, R} is a finite set X and a set of relations R = {R 0, R 1,..., R d } on X such that the R i satisfy: R 0 = {(x, x) : x X} If (x, y) R i, then (y, x) R i. (This condition is weaker in asymmetric association schemes.) R partitions X X. Fix values h, i, j [0, d], and consider the relations R h, R i, and R j. For each (x, y) R h, the number of elements z X such that (x, z) R i and (z, y) R j is always the same, regardless of (x, y).

Association schemes Graph intuition We can think of X as the vertices of a graph, and the values of (x, y) are the (undirected) edges of the graph. (Note that (x, x) is allowed, so the graph has self-loops.) We can think of the relations R 0,..., R d as d + 1 distinct colors. If an edge (x, y) is in R i, then we color the edge (x, y) by the color of R i. Since {R 0,..., R d } partitions X X, we know that each edge is colored exactly one color.

Association schemes Graph intuition Recall the following condition: Fix values i, j, k [0, d], and consider the relations R i, R j, and R k. For each (x, y) R i, the number of elements z X such that (x, z) R j and (z, y) R k is always the same, regardless of (x, y). Think of the edges (x, y), (x, z), and (z, y) as a triangle in the graph. Then, the condition becomes the following: If we consider all triangles (x, y, z) with (x, y) R h, (x, z) R i, and (z, y) R j, then every edge (x, y) R h takes part in the same number of triangles.

Hamming scheme Definition The association scheme that we are interested in is the Hamming scheme. Consider the vector space F n q. Our set of elements X will be all coordinates in F n q. Then, the Hamming scheme is defined as follows: There are n + 1 relations R 0,..., R n, which correspond to Hamming distances between pairs of points. For two coordinates x, y F n q, (x, y) belongs to the relation indexed by the Hamming distance of x and y. That is, (x, y) R Δ(x,y).

Hamming scheme... is an association scheme Let us check that the Hamming scheme satisfies the conditions for a symmetric association scheme. R 0 = {(x, x) : x X} Satisfied because Δ(x, y) = 0 x = y. If (x, y) R i, then (y, x) R i. Satisfied because Hamming distance is symmetric. R partitions X X. Satisfied by definition. Fix values h, i, j [0, d], and consider the relations R h, R i, and R j. For each (x, y) R h, the number of elements z X such that (x, z) R i and (z, y) R j is always the same, regardless of (x, y). Intuitively, this is true because the Hamming distance is unaffected by coordinate shifts.

Associate matrices Definition In an association scheme with set X and relations R 0,..., R d, we can define one associate matrix A i for each R i as follows: Each A i has rows and columns indexed by elements in X. (So each A i is an X -by- X matrix.) Entry (x, y) of A i is 1 if (x, y) R i, and 0 otherwise.

Associate matrices Example: Hamming scheme Consider the Hamming scheme on F 3 2, indexed by [000, 001, 010, 011, 100, 101, 110, 111]. We can easily check that A 0 = A 2 = 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 0 1 1 0 0 0 1 0 1 0 0 1 0 1 0 0 1 0 0 1 1 0 0 0 0 1 1 0 0 1 1 0 0 0 0 1 1 0 0 1 0 0 1 0 1 0 0 1 0 1 0 0 0 1 1 0 1 0 0 0, A 1 =, A 3 = 0 1 1 0 1 0 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 0 1 0 0 1 1 0 0 0 0 1 1 0 0 0 0 1 1 0 0 1 0 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 0 1 0 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0,.

Associate matrices Properties The associate matrices have several nice properties: A 0 = I, since R 0 only has elements of the form (x, x) d A i is the all-ones matrix, since R i partition X X i=0 If we multiply two matrices A i and A j, then we get a linear combination of A h for h [0, d]. d In particular, A j A i = pi,j h A h, where pi,j h is the number of h=0 triangles with one edge in (x, y) R h and other two edges in R i, R j. This is easily verified. From above, since p h i,j = p h j,i, we get that A ja i = A i A j, so the matrices are commutative. If we think of the matrices as a vector space, then the A i are linearly independent. because each of the X 2 entries is 1 in exactly one A i.

Bose-Mesner algebra Definition Recall the following property, which is perhaps the most important: If we multiply two matrices A i and A j, then we get a linear combination of A h for h [0, d]. An algebra is a vector space equipped with a bilinear product. The matrices A i are a basis for a vector space of matrices. Moreover, multiplying any two A i and A j results in a linear combination of the A h, which is again an element of the vector space. Therefore, the vector space spanned by A i forms an algebra over the matrices, called the Bose-Mesner algebra.

Bose-Mesner algebra Orthogonal basis It turns out that the Bose-Mesner algebra always has another basis of pairwise orthogonal matrices. Specifically, the vector space spanned by A 0,..., A d has another basis E 0,..., E d such that E i E j is the zero matrix if i j E 2 i = E i (such matrices are called idempotent.) This is analogous to the spectral theorem of linear algebra.

First and second eigenmatrices Definition Define the (d + 1)-by-(d + 1) matrices P and Q as follows: The entries of P satisfy A i = d P ji E j. j=0 The entries of Q satisfy E i = 1 X d Q ji A j. P is called the first eigenmatrix, and Q is the second eigenmatrix. They are essentially change-of-basis matrices from the basis A 0,..., A d to the basis E 0,..., E d and back. j=0

First and second eigenmatrices Properties If we instead think of the A i and E i as individual entries of a matrix (i.e. pretend that they are numbers), then the definitions can be more concisely written as [ ] [ A 0 A 1 A d ] = [ E 0 E 1 E d ] P,0 P,1 P,d [ E 0 E 1 E d ] = 1 X [ A 0 A 1 A d ] [ Q,0 Q,1 Q,d If we combine the two equations, then we can see that [ E 0 E 1 E d ] = 1 X ([ E 0 E 1 E d ] P) Q Since the E i are linearly independent, we must have P Q = X I (where I is the (d + 1)-by-(d + 1) identity matrix) ]

Eigenmatrices for the Hamming scheme Even for the Hamming scheme, computing the eigenmatrices P and Q is highly non-trivial. Delsarte [Del 73] showed that the eigenmatrix Q for the Hamming scheme on F n q can be represented in terms of the Krawtchouk polynomials, which are defined as: K k (x) = k i=1 ( x i )( n x k i ) ( 1) i (q 1) k i In particular, Q i,k = K k (i). (This is a highly non-trivial result.) As an example, for ] the Hamming scheme in F 3 2, Q = [ 1 3 3 1 1 1 1 1 1 1 1 1 1 3 3 1.

Distribution vectors Definition Let (X, R) be an association scheme and let Y be a subset of X. The distribution vector of Y is a vector a of length d + 1 such that a i = (Y Y ) R i. Y In graph notation, we can think of the subgraph induced by the vertices in Y. Then, a i is simply the average degree of a vertex in Y, where only edges in R i are considered. d We can easily see that a i = Y. i=0

Distribution vectors Relation to coding theory Let us consider the Hamming scheme again, where X = F n q. Consider a code in F n q. We can let Y be the set of codewords. Now consider the distribution vector a. Recall that a i is the average degree of a vertex in the subgraph induced by Y, where only edges in R i are considered. What can we deduce about a? d We know that a i 0 and that a i = Y. i=0 We can also easily see that a 0 = 1. Suppose, in addition, the code has distance r. Then, we also know that a 1 = a 2 = = a r 1 = 0. There is one more key property, which we prove next.

Distribution vectors Main theorem Here is the key theorem on distribution vectors that forms the basis for the linear programming bound. Theorem: If a is a distribution vector of a subset Y of an association scheme with second eigenmatrix Q, then aq 0. (That is, the vector aq has only non-negative entries.) Proof: Let y be the characteristic vector of Y. That is, y x = 1 if x Y, and 0 otherwise. Then, a i = ya iy T Y It follows that. 0 ye i 2 = (ye i )(ye i ) T = ye i Ei T y T = ye i y T, where the last step is true because E i is idempotent and symmetric.

Distribution vectors Main theorem Proof (continued): Recall that E i = 1 d Q ji A j and a i = ya iy T. X Y j=0 Therefore, 0 ye i y T = 1 d X y Q ji A j y T = 1 d Q ji ya j y T = X Y X d j=0 j=0 a j Q ji = Y X (aq) i. So for each i, (aq) i 0, as desired. j=0

The linear programming bound Formulation Let us collect all of the conditions that a must satisfy: a 0 = 1. a i = 0 for 1 i < r. a i 0 for r i n. aq 0. (This introduces d + 1 linear inequalities.) d At the end, we know that a i = Y. Therefore, to upper i=0 bound the set Y of codewords, our objective of the linear d program is to maximize a i. i=0 That s it for Delsarte s linear program.

The linear programming bound A nice property A nice property that merits its own slide: The linear programming bound works for all codes, not just linear codes. This is because we make no assumption on the set Y X.

The linear programming bound Comparison to Hamming bound For fixed n and q, we can numerically solve the linear program to find the upper bound for codes in F q n. Here is a table comparing the Hamming bound and the LP bound for codes in F n 2 with distance δ. Note that the tables suggest that the LP bound is always at most the Hamming bound. This is in fact true: Delsarte [Del 73] showed how to establish the Hamming bound using the LP bound, so the LP bound is always at least as strong. Also note the perfect code with n = 15 = 2 4 1 and δ = 3. As expected, both bounds achieve this perfect code.

Asymptotics for the linear programming bound What about for higher n? We would like to find asymptotics for the linear programming bound. For the rest of this talk, we will focus only on binary codes.

Asymptotics for the linear programming bound Let A(n, δn ) be the maximum size of a binary code with length n and distance δn. We can define the function log R(δ) = lim sup 2 A(n, δn ). Intuitively, this is an n n asymptotic measure of the best rate possible for a binary code. Similarly, let A LP (n, δn ) to be the maximum value of d a i for some a that satisfies Delsarte s linear program. i=0 Since the LP bound is an upper bound, we have A(n, δn ) A LP (n, δn ). log We can also define R LP (δ) = lim sup 2 A LP (n, δn ). We n n want bounds on R LP (δ), which is an upper bound for R(δ).

Asymptotics: upper bound for the linear programming bound We are most interested in an upper bound for R LP (δ), since this will also be an upper bound for R(δ). McEliece, Rodemich, Rumsey, and Welch [MRRW 77] showed that R LP (δ) H( 1 2 δ(1 δ). This is the best bound known for δ 0.273. Here is a plot of this upper bound with the Elias-Bassalygo bound R(δ) ( ) 1 1 2δ 1 H, 2 which we saw in class:

Asymptotics: upper bound for the linear programming bound Navon and Samorodnitsky [NS 05] established a simpler proof of the same bound, R LP (δ) H( 1 2 δ(1 δ). Their method was to construct feasible solutions to the dual of Delsarte s linear program, which can be formulated as: minimize (Qb) 0, given the constraints b 0. b 0 = 1. (Qb) i 0 for d i n. By linear programming duality, the minimum of the dual equals the maximum of the primal, so any feasible solution to the dual is an upper bound of the optimum A LP (n, d). Their construction uses Fourier analysis on Z n 2. Unfortunately, Fourier analysis is not as nice on Z n q for q > 2, so their construction does not generalize to arbitrary q.

Asymptotics: lower bound for the linear programming bound We might also be interested in a lower bound for R LP (δ). A lower bound for R LP (δ) gives a better measure of how powerful the LP bound actually is. It is essentially a cap on the strength of the bound. Navon and Samorodnitsky [NS 05] showed the lower bound R LP (δ) 1 2 H(1 2 δ(1 δ)), which is currently the best known. Here is a plot of the lower bound with the upper bound.

Open problems Improved lower bounds for binary codes Delsarte s linear program provides asymptotic upper bounds that are the best for δ 0.273. Therefore, any improvement to the upper bound with δ in this range improves upon the best known upper bound. While the lower and upper bounds of [NS 05] converge as δ 1, there is a large gap for smaller δ. This allows for 2 improvement of at least one of the bounds. Asymptotic bounds for q > 2: The linear programming bound has provided some of the best asymptotic bounds for q = 2. Unfortunately, these techniques do not generalize for arbitrary q. However, Delsarte s linear program generalizes to all q. Therefore, an open question remains to show good asymptotic bounds for R LP (δ) for q > 2.

The end References: Delsarte, P.: An algebraic approach to the association schemes of coding theory. Philips Res. Rep., Suppl. 10 (1973) McEliece, R.J., Rodemich, E.R., Rumsey, H. Jr., Welch, L.R.: New upper bounds on the rate of a code via the DelsarteMacWilliams inequalities. IEEE Trans. Inf. Theory IT-23, 157166 (1977) of FOCS 46 McKinley, S.: The Hamming Codes and Delsarte s Linear Programming Bound, available at http://www.mth.pdx.edu/ caughman/thesis.pdf. Navon, M., Samorodnitsky, A.: On Delsarte s linear programming bounds for binary codes. In: Proceedings Thank you for your attention. :)

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