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Absolute homogeneity is the norm axiom $\Vert av\Vert =|a|\Vert v\Vert$ for every scalar $a$ and vector $v$. It distinguishes a norm from a merely homogeneous expression of a different degree; for example the fourth root in the box norm converts a degree-four expression into a degree-one norm.

For four real-valued functions on a finite Cartesian product, let $\Lambda (f_{00},f_{01},f_{10},f_{11})={\mathbb{E}}_{x_0,x_1,y_0,y_1}{\prod }_{i,j=0}^1{f}_{ij}(x_i,y_j)$. Repeated Cauchy-Schwarz inequalities giveFirst separate the two $y$ averages and apply Cauchy-Schwarz inequality in $(x_0,x_1)$. Each resulting squared factor is ${\mathbb{E}}_{y,y^{\prime }}({\mathbb{E}}_{x}f(x,y)f(x,y^{\prime }))({\mathbb{E}}_{x}g(x,y)g(x,y^{\prime }))$, bounded by $\Vert f{\Vert }_{\square }^2\Vert g{\Vert }_{\square }^2$ by another Cauchy-Schwarz inequality. Expanding the four factors of $f+g$ and applying this inequality to each of the sixteen terms gives the triangle inequality for the box norm.

For a real-valued function $H$ on a nonempty finite Cartesian product, let $T_{H}v(x)={\mathbb{E}}_{y}H(x,y)v(y)$ with uniform inner products. The box norm obeys $\Vert H{\Vert }_{\square }^4=\mathrm{Tr}((T_H{T}_H^{\ast }{)}^2)={\sum }_j{\sigma }_j^4$, where ${\sigma }_j$ are its singular values. For a biregular graph this separates the constant component ${\gamma }^4$ from the other fourth powers.

For a real function $f$ bounded by one on a finite Cartesian product, the fourth power of its box norm is the mean of $f(x,y)f(x,y^{\prime })f(x^{\prime },y)f(x^{\prime },y^{\prime })$. Two applications of the Cauchy-Schwarz inequality bound every rectangular discrepancy by the box norm. Conversely, decompose each bounded real test function into positive and negative parts and use the layer cake representation. Its bilinear pairing with $f$ is at most four times the cut norm. Applying this to the row and column tests furnished by fixed $x^{\prime },y^{\prime }$ bounds the fourth moment by four times the cut norm. These dimension-independent estimates make the two notions of small discrepancy equivalent.

Definiteness says that a norm vanishes only at the zero vector. The box norm on a nonempty finite Cartesian product is definite because its defining sum of squares includes equal-column terms, each of which is the square of an average of squares. Omitting repeated coordinates would destroy this proof and can produce a different quantity.

Use uniform expectations on the nonempty sets $X,Y$. Define the rectangle fourth moment and the normalized cut norm byThe first quantity is nonnegative, sinceits fourth root is the box norm. To bound the cut norm, fix $A,B$ and put $T={\mathbb{E}}_{x,y}f(x,y)1_A(x)1_B(y)$. The Cauchy-Schwarz inequality in $y$ givesA second Cauchy-Schwarz inequality, now in $(x,x^{\prime })$, yieldsbecause the squared mean of $1_A(x)1_A(x^{\prime })$ is at most one. Taking the maximum over rectangles gives the first direction of cut norm and rectangle fourth-moment equivalence:The normalization divides the rectangle sum by $mn$ and the fourth-moment sum by $m^2{n}^2$, so the bound is independent of the two set sizes.

The expectations use normalized counting measure on a finite abelian group $G$. This is the finite-group convention implicit in the averaging notation; an arbitrary infinite group has no normalized counting probability measure on all its points. For $h\in G$, define ${\Delta }_{h}f(x)=f(x)\overline{f(x+h)}$, and let $\mathcal{C}$ denote complex conjugation. The Gowers U3 norm isAveraging first over $h_3$ gives the Derivative identity for the Gowers U3 normIndeed, if $u(x)={\Delta }_{h_1}{\Delta }_{h_2}f(x)$, the average over $(x,h_3)$ is ${\mathbb{E}}_{x,h_3}u(x)\overline{u(x+h_3)}=|{\mathbb{E}}_{x}u(x){|}^2$. The eighth root is consequently well-defined. Homogeneity follows because the cube has four unconjugated and four conjugated factors, so $\Vert af{\Vert }_{U^3}=|a|\Vert f{\Vert }_{U^3}$. If the norm is zero, every squared term in the finite average vanishes. Taking $h_1=h_2=0$ gives ${\mathbb{E}}_x|f(x){|}^4=0$, so $f=0$ pointwise. This proves positive definiteness.

For the triangle inequality, we prove the needed Gowers-Cauchy-Schwarz inequality. For eight functions $F_{\omega }$ on a product of finite sets $X_1\times X_2\times X_3$, form the box norm multilinear averageFix a coordinate $j$. After averaging its two variables separately, this is $\mathbb{E}A\overline{B}$, where $A$ uses all vertices with ${\omega }_j=0$ and $B$ those with ${\omega }_j=1$, with the remaining parity conjugations. The Cauchy-Schwarz inequality giveswhere $S_j^b$ replaces the function at each vertex by the original function with its $j$th index set to $b$. Each right-hand average is a squared absolute value averaged over the other coordinates, hence nonnegative. Apply this operation successively for $j=1,2,3$, to every resulting factor. The eight final families each use one original $F_{\omega }$ at all vertices. Taking roots gives the box Cauchy-Schwarz inequalityNow take $X_j=G$ and $F_{\omega }(a,b,c)=f_{\omega }(a+b+c)$. With $x=a_0+b_0+c_0$ and $h_j=x_j^1-x_j^0$, these independent variables induce uniform $(x,h_1,h_2,h_3)$: each such tuple has exactly $|G{|}^2$ preimages. Therefore each box norm is the corresponding $U^3$ norm, and the mixed cube obeysExpand the eight factors in the cube expression for $f+g$. Its $2^8$ mixed terms are bounded by this inequality, and their bounds sum to $(\Vert f{\Vert }_{U^3}+\Vert g{\Vert }_{U^3}{)}^8$. Thus $\boxed{\Vert f+g{\Vert }_{U^3}\le \Vert f{\Vert }_{U^3}+\Vert g{\Vert }_{U^3}}$, completing the norm proof.

Two consequences will be useful below. Multiplication by a character of a finite abelian group leaves the norm unchanged, because the alternating character product around a three-dimensional cube equals one. Alsowhere the last inequality uses the $h=0$ term and $\Vert u{\Vert }_{U^2}^4={\sum }_{\chi }|\hat{u}(\chi ){|}^4\ge |\mathbb{E}u{|}^4$. Hence $\Vert f{\Vert }_{U^3}\ge |G{|}^{-1/8}\Vert f{\Vert }_2$.

For this Cartesian product, the requested $U^2$ norm is the box norm, with complex conjugates:Equivalently it is ${\mathbb{E}}_{y,y^{\prime }}|K(y,y^{\prime }){|}^2$, where $K(y,y^{\prime })={\mathbb{E}}_{x}f(x,y)\overline{f(x,y^{\prime })}$. This makes its nonnegativity explicit.

Apply the Cauchy-Schwarz inequality first in $x$:The right-hand expectation is nonnegative, since it came from ${\mathbb{E}}_x|{\mathbb{E}}_{y}f(x,y)v(y){|}^2$. A second application in $(y,y^{\prime })$ givesTaking fourth roots proves the bilinear correlation bound for the box norm

A clean proof outline uses hypergraph removal rather than the quadratic density increment route. The structure is: establish removal for the three-uniform tetrahedron, encode four-term arithmetic progressions as its copies, and use the many edge-disjoint copies coming from constant progressions to contradict removal. We give the technical regularity stage in outline, as requested, and prove the counting inequality and the encoding that make the argument work.

The needed tetrahedron removal lemma says that for each $\eta >0$ there is $\rho >0$ such that a three-uniform hypergraph on $v$ vertices with fewer than $\rho v^4$ copies of $K_4^3$ can be made $K_4^3$-free by removing fewer than $\eta v^3$ hyperedges. Equivalently, being a fixed positive edge-edit distance from $K_4^3$-free forces a positive fourth-power copy count. Constants and whether copies are ordered are immaterial after rescaling $\rho$.

Here is the global proof of this hypergraph removal input. Strong regularity for three-uniform hypergraphs partitions both vertices and the bipartite pair sets between vertex classes. The resulting three-class cells, called triads in a hypergraph regularity partition, are supported on three pair cells. On all but a prescribed small total weight, the triple-edge function has approximately constant relative density and a small relative three-dimensional box norm. This two-level refinement is essential: a vertex partition alone does not capture correlations carried by pairs. The regularity construction uses the squared L2 norms of conditional expectations of edge indicators as bounded energies. Refinement increases this energy by the squared L2 norm of the difference between the new and old conditional expectations, by orthogonality. Thus a nonuniformity witness causing a definite discrepancy causes a definite energy increment. A witness to nonuniformity refines the relevant pair or triple partition and raises the appropriate energy. A hierarchy of tolerances, with sufficiently strong refinement at the pair level, makes these increases terminate after a bounded number of stages. Bounds may depend very badly on the requested tolerance, which is harmless for a qualitative theorem.

Delete hyperedges with two vertices in the same vertex class, hyperedges meeting exceptional classes or irregular cells, hyperedges supported on pair cells of very small density, and hyperedges in triads of very small triple density. Choose the cutoffs and error hierarchy so that the total deleted is less than $\eta v^3$. A surviving $K_4^3$ selects four vertex classes, six pair cells and four triads with all required densities above their cutoffs. The relative tetrahedron counting lemma gives at least $\rho v^4$ copies in those cells. This contradicts the assumed sparse copy count, and proves the tetrahedron removal lemma. The strong regularity and relative counting estimates are the technical portion outlined here; the dependence of their constants is not needed.

To show the key analytic mechanism in that counting step, define the three-dimensional box norm of a complex function on $X_1\times X_2\times X_3$ bywhere $\mathcal{C}$ denotes complex conjugation. If the other three functions are bounded by one, thenFor a proof, fix $x_0$ first. Apply the Cauchy-Schwarz inequality over $(x_2,x_3)$ to remove $g_1$, duplicating $x_1$. Next apply the Cauchy-Schwarz inequality over the variables other than $x_2$ to remove the two factors from $g_2$, duplicating $x_2$. A third application removes the four factors from $g_3$ and duplicates $x_3$. The resulting eighth power is exactly the cube average displayed above. Averaging over $x_0$ proves the inequality. This also shows nonnegativity of the cube average by its successive squared-sum form.

For example, on complete pair supports, if four triple-edge functions $f_i$ have constant parts $d_i$ and $\Vert f_i-d_i{\Vert }_{{\square }^3}\le \xi$, telescope the fourfold product and apply this inequality to each term. Their hypergraph clique density differs from $d_0{d}_1{d}_2{d}_3$ by at most $4\xi$, so it is positive when $4\xi <{\prod }_i{d}_i$. On general pair supports the same successive squaring argument is combined with sufficiently regular pair cells; choosing relative errors small compared with the pair-density product gives the relative tetrahedron counting lemma. This explains both the counting step and why its support must be regularized before the triple-edge densities. It supplies a proved important step without pretending that the strong regularity theorem is a one-line assertion.

We now prove the four-term progression hypergraph encoding completely. Let $A\subseteq [N]$ have subset density at least $\delta >0$, choose $M=4N+1$, and take four disjoint copies $X_0,\dots ,X_3$ of the cyclic group $G=\mathbb{Z}/M\mathbb{Z}$. Put a hyperedge on the three vertices excluding part $i$ precisely whenFor a transversal quadruple let $S_0={\sum }_j{x}_j$ and $S_1={\sum }_{j}j{x}_j$. Its four edge conditions are $S_1-i{S}_0\in A$, $i=0,1,2,3$. Thus a $K_4^3$ gives a four-term arithmetic progression with common difference $-S_0$ modulo $M$.

Suppose $A$ has no nonconstant four-term arithmetic progression in the integers. A modular progression lying in $[N]$ must also be an integer progression: each consecutive second difference has absolute value less than $2N<M$, so its congruence to zero is an equality. Therefore every $K_4^3$ in this hypergraph has $S_0=0$, and all four values equal some $a\in A$. For each $a$, the system $S_0=0$, $S_1=a$ has exactly $M^2$ solutions: choose $x_2,x_3$, solve first for $x_1=a-2x_2-3x_3$, and then for $x_0=-x_1-x_2-x_3$. There are exactly $|A|M^2=O(M^3)$ copies of $K_4^3$.

These copies are edge-disjoint. Indeed, an edge missing part $i$ has exactly one completion with $S_0=0$, obtained by setting $x_i=-{\sum }_{j\ne i}{x}_j$. Its common value is the already prescribed $L_i\in A$. Hence destroying all copies requires at least $|A|M^2$ edge deletions. Since $M\le 5N$, this is at least $\delta M^3/5$. Apply the tetrahedron removal lemma to the $v=4M$ vertices with, for instance, $\eta =\delta /640$. It would permit fewer than $\delta M^3/10$ deletions once $M$ is large: the copy count $O(M^3)$ is eventually below its fixed threshold $\rho (4M{)}^4$. This is impossible.

Every fixed positive density therefore forces a nonconstant four-term arithmetic progression in sufficiently long intervals. This proves the length-four case of the Szemerédi theorem. Applying it to sufficiently large initial intervals along a subsequence witnessing positive upper asymptotic density also gives the usual infinite-set formulation.

Uniform expectations require nonempty finite sets; assume this throughout the analytic argument. For real-valued functions on $X\times Y$, define the box norm byAll four variables are sampled as independent random variables with replacement, so repeated coordinates are included. The defining fourth power equalsIf it vanishes, every summand vanishes. In particular, the terms $y_0=y_1=y$ give ${\mathbb{E}}_{x}f(x,y{)}^2=0$ for each $y$, so $f=0$. Absolute homogeneity of a norm follows directly: $\Vert af{\Vert }_{\square }=|a|\Vert f{\Vert }_{\square }$.

For the triangle inequality, prove the mixed box Cauchy-Schwarz inequality. WriteSeparating the two $y$ averages and applying the Cauchy-Schwarz inequality in $(x_0,x_1)$ givesExpanding the square and reversing the order of finite sums,by a second Cauchy-Schwarz inequality. Thus $|\Lambda |\le {\prod }_{i,j}\Vert f_{ij}{\Vert }_{\square }$. Expand $\Lambda (f+g,f+g,f+g,f+g)$ into its sixteen multilinear terms and apply this bound to each term. Their upper bounds sum to $(\Vert f{\Vert }_{\square }+\Vert g{\Vert }_{\square }{)}^4$. Taking fourth roots yields the triangle inequality. Together with definiteness of a norm and absolute homogeneity of a norm, this proves the box norm is a norm. Complex functions require conjugates and are outside the printed real-valued convention.

For the bilinear correlation bound for the box norm, use the normalized L2 norm: let $B={\mathbb{E}}_{x,y}f(x,y)u(x)v(y)$ and $\Vert u{\Vert }_2^2={\mathbb{E}}_{x}u(x{)}^2$, $\Vert v{\Vert }_2^2={\mathbb{E}}_{y}v(y{)}^2$. The Cauchy-Schwarz inequality in $y$ givesThe remaining factor is ${\mathbb{E}}_{x,x^{\prime }}u(x)u(x^{\prime }){\mathbb{E}}_{y}f(x,y)f(x^{\prime },y)$. By the Cauchy-Schwarz inequality in $(x,x^{\prime })$, its magnitude is at mostTaking square roots proves

For the tripartite graph, assume its parts are nonempty and write $g_{XY},g_{YZ},g_{XZ}$ for its adjacency indicator functions. Let $f=g_{XY}-\alpha$, so $\Vert f{\Vert }_{\square }\le c$. The normalized triangle count isThe constant-degree hypothesis is exactly ${\mathbb{E}}_y{g}_{YZ}(y,z)=\beta$ for each $z$. Therefore the contribution of the constant $\alpha$ isFor fixed $z$, apply the bilinear correlation bound for the box norm with $u_z(x)=g_{XZ}(x,z)$ and $v_z(y)=g_{YZ}(y,z)$. Since these are indicator functions, $\Vert v_z{\Vert }_2^2=\beta$ and $\Vert u_z{\Vert }_2^2=d_X(z)$, the fraction of $X$ adjacent to $z$. Averaging and applying the Cauchy-Schwarz inequality in $z$ gives the sharper triangle counting with one box-uniform pair and constant opposite degree estimateMultiplying by $|X||Y||Z|$ yieldsThe middle bound also handles $\beta =0$ or $\gamma =0$, when the triangle count is zero.

To connect explicitly with the suggested expansion, put $f_2=g_{YZ}-\beta$, $f_3=g_{XZ}-\gamma$. Both have overall mean zero, and ${\mathbb{E}}_y{f}_2(y,z)=0$ for every $z$. Consequently ${\mathbb{E}}_{x,y,z}{f}_2(y,z)f_3(x,z)=0$; all terms with constant $\alpha$ reduce to $\alpha \beta \gamma$. The terms containing $f$ combine into the single error just estimated. This is where the exact degree assumption is used. If any part is empty the triangle count itself is zero, but the printed uniform expectations and edge density of a bipartite graph values would be undefined; the nonempty-parts convention must therefore be stated rather than dividing by zero.

For a biregular graph of edge density of a bipartite graph $\gamma$, small rectangular discrepancy, a box norm fourth power close to ${\gamma }^4$, and a small second singular value of the normalized adjacency operator of a bipartite graph are equivalent notions of quasirandomness, with quantitative bounds independent of the part sizes. Centering the adjacency function removes the constant singular component.

Give every entry of an $m$ by $n$ matrix an independent uniformly random sign. A four-entry rectangle product has expectation zero unless its two row indices coincide or its two column indices coincide; in those cases it is one. Counting the union of these diagonal cases gives $m{n}^2+m^{2}n-mn$ before normalization. Thus some sign matrix has box norm fourth power at most the displayed expectation, and its cut norm tends to zero when both dimensions tend to infinity.