A sufficient absolute constant is . The proof is a density increment argument for a cap set over the finite field .
First work in , write , and let be the indicator function of a cap set of subset density . All expectations below are uniform. In characteristic three, a solution of having two equal entries has all three equal. Consequently the normalized linear configuration count is
Set and use Fourier analysis on a finite abelian group with
The orthogonality of roots of unity and the Parseval identity on a finite group give
If , then for a cap set and
Thus some nonzero finite abelian Fourier coefficient has magnitude at least .
For this , let be the subset density of on the affine subspace , for . These three affine subspaces have equal cardinality, and
Among three directions separated by , one makes an angle at most with any given complex number. Hence . Restricting to that hyperplane gives the hyperplane density increment for cap sets
Translate the affine subspace to its underlying vector space. This preserves the cap set property: translating a triple by changes its sum by . The same argument can therefore be iterated.
For completeness, the density increment iteration gives an explicit uniform bound. If , the hypothesis is impossible. Suppose and a cap set has . For every integer , its remaining dimension is at least , and its current subset density is at least . Thus
The last inequality holds at and remains true as increases: the successive ratio of is for . At each step, as long as the subset density remains at most one,
After steps this would imply
a contradiction. The endpoint already contradicts the cap set property in positive dimension. This proves the claimed existence of three distinct points and the Meshulam bound for cap sets.
Use uniform expectations on the two nonempty parts, and equip their function spaces with the inner product . Let and
Since the bipartite graph is a biregular graph, both row and column averages of vanish. The normalized adjacency operator of a bipartite graph therefore splits into the map between constant functions, of singular value , and between their orthogonal complements.
There is no singular value larger than . Indeed, for , the Cauchy-Schwarz inequality gives
Averaging in uses the constant column degree and gives . For every operator is zero. Thus the second singular value is
If one part has a single vertex, missing singular values are interpreted as zero; the biregular graph then has .
The discrepancy in (i) is exactly . Its supremum over is the cut norm . To pass from this bound to arbitrary bounded real-valued functions, use the positive and negative parts and the identity for . Applying this separately to both factors gives
Here is a bound on , unrelated to any cardinality.
If , take real unit left singular vector and right singular vector with and . The Cauchy-Schwarz inequality and imply . Hence
This proves (i)(iii), with a constant independent of both part sizes. The case satisfies the same conclusion immediately.
Let be the singular values of the normalized adjacency operator of a bipartite graph, with the uniform inner products used above. Its ordinary matrix in an orthonormal basis on each side is . Expanding the trace of proves the box norm singular-value identity
Therefore (ii) immediately implies
which is (iii).
Conversely, the square of the Hilbert-Schmidt norm of this operator is
since a graph indicator function takes values zero and one. Under (iii), every with is at most , so
Thus (iii)(ii) with . This bound also covers the empty and complete bipartite graphs.
The biregular graph assumption allows both indicator functions in the discrepancy to be balanced:
The operator norm and the Cauchy-Schwarz inequality give
Consequently (iii)(i) with . Together with the previous sections, explicit constants for every direction are
All these constants tend to zero with the initial constant and are independent of . This is the required equivalence for a quasirandom bipartite graph.
Use the following normalization for Fourier analysis on a finite group. Choose one unitary irreducible representation from each equivalence class, including the trivial representation. For a scalar function , put
This convention uses , rather than , in the Fourier transform on a finite group; it makes the normalized convolution on a finite group preserve multiplication order.
The needed representation theory consists of Maschke's theorem and unitarization of a finite-group representation, together with the Schur orthogonality relations:
The regular representation contains copies of each , so . Thus the scaled matrix coefficients , and also their complex conjugates, form an orthonormal basis of all scalar functions on . These facts imply Fourier inversion on a finite group and the Parseval identity on a finite group in the forms
and hence
In particular, the transform is an isomorphism onto the direct sum of the matrix algebras , with the displayed weighted Hilbert-Schmidt inner product.
Define the normalized convolution on a finite group by
Substituting and using the group representation identity yields the convolution theorem on a finite group
Unlike normalized convolution on a finite group on an abelian group, this product need not commute. If , then . For left translation of a group function and right translation of a group function and ,
For an abelian group, every irreducible representation is one-dimensional; this reduces to Fourier analysis on a finite abelian group with characters relabelled by their inverses. These formulas establish the basic scalar theory, with all normalizations and multiplication orders fixed.
Now suppose every nontrivial irreducible representation has . If is a mean-zero function, its component at the trivial representation is zero. The Parseval identity on a finite group gives, for each other ,
Using the convolution theorem on a finite group, the Hilbert-Schmidt norm inequality , and the Parseval identity on a finite group once more gives the product mixing in a quasirandom group estimate
Write for the subset density values of , respectively, and let , be balanced subset indicators. Since both are mean-zero functions, . Their squared norms are and . Also , so the Cauchy-Schwarz inequality yields
If , the final bound is strictly smaller than . Thus the normalized number of pairs with is positive. Equivalently,
This is the desired conclusion for a quasirandom group; the strict inequality ensures positivity rather than merely a nonnegative lower bound.
Use , the necessary parameter range for the bounds involving . The construction comes from the singular value decomposition of matrix Fourier blocks. For each inequivalent unitary irreducible representation of dimension , consider the Hilbert space with the Hilbert-Schmidt inner product and the operator
Its operator norm is at most one, since is a unitary matrix and . Thus all its singular values lie in .
Two identities control their weighted moments:
Here the expectation in is uniform on the solutions of the constraint, and the trace on used to compute each moment is the ordinary operator trace. We justify the identities explicitly to fix their normalizations and the noncommutative order.
The adjoint operator is , so
For rectangular matrices, the operator has trace , as is seen on the matrix-unit basis. Squaring the previous operator therefore gives
The required representation theory consists of unitarization of a finite-group representation, the Schur orthogonality relations, and the regular representation decomposition. The last gives the character expansion of the identity delta
Summing the two operator traces with weights selects in the first, and in the second. Multiplication by converts each independent-variable expectation to its conditional expectation. This proves . In particular is real and nonnegative, despite the apparently complex summands in its original formula. Since , the nonvacuous hypothesis has .
Select every singular value satisfying . Index the selected pairs by , set , , and choose unit right singular vectors and unit left singular vectors with . Define
Both are matrices, proving (i). Repeated group representations in this list are literally the same chosen representative, rather than different equivalent realizations.
The singular values discarded by the threshold in (i) satisfy , so their contribution to the fourth moment is at most . The selected singular values consequently satisfy
Because , this implies . On the other hand, , and hence
Thus
The lower bound also shows that the selected list is nonempty. Both endpoints are inclusive, as required.
The selected singular value decomposition directly gives
Each is a real nonnegative singular value. The scale factor in both matrices cancels in this equation and is chosen to give the precise Hilbert-Schmidt inner product normalization in (iv).
Within each fixed unitary irreducible representation, choose the selected right singular vectors to be orthonormal. Their corresponding left singular vectors are also orthonormal, even when a singular value is repeated: choose an orthonormal basis in each eigenspace of and put . Therefore, when ,
This is (iv), with the unnormalized Hilbert-Schmidt inner product and the Kronecker delta. No orthogonality of differently shaped matrices is being asserted.
For the unheaded continuation, partition into blocks . Then
The Schur averaging of rectangular matrices formula is
for any matrix . The average is an intertwiner; the Schur lemma makes it zero for inequivalent irreducible representations, and a scalar multiple of the identity for the same representative. The trace determines that scalar in the latter case.
Apply this with . The Hilbert-Schmidt inner product normalization just proved gives
Expanding the squared Euclidean norm now yields
Thus the final identity follows from averaging, although itself need not be the identity. The whole construction is the spectral inverse theorem for the matrix-valued U2 quantity.

Articles by others on the same topic (0)

There are currently no matching articles.