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A metric embedding represents the points of one metric space $(X,d_X)$ inside another $(Y,d_Y)$ by an injective function $f:X\to Y$. The geometric question is how faithfully the representation preserves pairwise distances. For example, binary strings can be treated as vectors so that tools from linear algebra become available, but the chosen norm determines what their separation means. A drawing of the points alone does not specify a metric embedding: both metrics matter.

An isometric embedding preserves every distance exactly. A metric embedding that is a bi-Lipschitz map allows multiplicative error while keeping its bi-Lipschitz distortion bounded over all pairs. A coarse embedding instead controls separation through possibly nonlinear functions of distance. These are different requirements: the Hamming cube as an L1 and L2 metric below preserves Hamming distance exactly in one representation and takes its square root in another.

The usefulness is that a complicated distance comparison becomes a calculation with familiar vectors and norms. This also helps assess a visualization: nearby points should not acquire arbitrarily large separations, and far-apart points should not collapse together. Matoušek's 2013 lecture notes describe how motivations from computer science, especially approximation algorithms, accelerated the study of metric embeddings from the 1990s onward. ^[ref]

A coarse embedding $f:X\to Y$ has nondecreasing control functions ${\rho }_{-},{\rho }_{+}$ such thatA family is a uniform coarse embedding of a family of metric spaces when all its maps use the same controls.

For $H_n=\{0,1{\}}^n$ with the Hamming distance, the coordinate map into ${\ell }_1^n$ satisfiesso the cubes embed isometrically and uniformly into $L^1$. The same coordinate map into ${\ell }_2^n$ satisfiesThus they also uniformly coarsely embed into $L^2$, with ${\rho }_{-}(t)={\rho }_{+}(t)=\sqrt{t}$. This is the Hamming cube as an L1 and L2 metric.

For a finite graph $G=(V,E)$, defineAn expander family is a sequence of graphs with orders tending to infinity, uniformly bounded degrees, and $h(G)$ uniformly bounded below.

Now let $|V|=n$ and let $M$ be a median of $f:V\to \mathbb{R}$. PutBoth supports have at most $n/2$ vertices. The layer cake representation and the definition of $h$ giveand the same holds for $k$. Since their supports are disjoint,Adding givesThe triangle inequality also givesand henceRepresent an $L^1$-valued map as scalar functions on the underlying measure space and integrate this inequality. The result is the L1 Poincare inequality for an expander graph.

Suppose next that $G$ is $d$-regular and $F:V\to L^1$ is noncontracting with Lipschitz constant $D$. The Poincare inequality givesA ball of radius $r$ in a graph of maximum degree $d$ has at mostvertices. Therefore, forat least $n/2$ vertices lie outside the radius-$r$ ball about each vertex. Integrating the tail count of the distance givesConsequently every such $F$ haswhich proves the L1 distortion lower bound for an expander graph.

Finally, suppose an expander family admitted uniform coarse embeddings $F_n$ into $L^1$. Embedded edges have length at most ${\rho }_{+}(1)$, whereas the preceding Poincare argument and the fact that at least half the ordered pairs have distance comparable to $\log |V_n|$ implyThis contradicts ${\rho }_{-}(t)\to \infty$. Thus expanders do not uniformly coarsely embed into $L^1$. They do not uniformly coarsely embed into $L^2$ either: a Hilbert space embeds isometrically into an $L^1$ space byso an $L^2$ coarse embedding would produce an $L^1$ one. This is the expander graph obstruction to uniform coarse embedding.

A family $(X_n)$ uniformly coarsely embeds into $Y$ when maps $f_n:X_n\to Y$ admit the same two control functions ${\rho }_{-}$ and ${\rho }_{+}$ from the definition of a coarse embedding, independent of $n$. For a single finite metric space, the lower control can vanish up to its diameter and then grow without bound; this says little about its representation. For a family with unbounded diameters, common controls have real content: they cannot be chosen afresh after seeing each member's diameter. The Hamming cube as an L1 and L2 metric supplies explicit common controls, in contrast with the expander graph obstruction to uniform coarse embedding.