diff --git a/content/essays/ball-occupation-under-coarse-projections.md b/content/essays/ball-occupation-under-coarse-projections.md new file mode 100644 index 0000000..27a23de --- /dev/null +++ b/content/essays/ball-occupation-under-coarse-projections.md @@ -0,0 +1,436 @@ +--- +title: "Ball-Occupation Certificates under Coarse Graph Projections" +subtitle: "Degree Reduction, Square-Root Hard Families, and Toroidal Barriers" +date: 2026-07-27 +abstract: > + We isolate an abstract strategy-transfer principle for Cops and Robber: if a + surjection from H onto G has fibers of bounded order P and distances + between distinct fibers stretched by a scale factor lambda with bounded + additive slack, then a Hall-type assignment occupies a lifted macro-ball + before the robber can leave it, giving cop number O(P(d^3 + log(ePN)) + sqrt(N)) and capture time at most lambda R -- independent of the tower + depth lambda itself. Applied to the iterated degree-reduction construction + of Hosseini-Mohar-Gonzalez Hermosillo de la Maza, this shows the known + polylog-degree hard family for Meyniel's conjecture already meets the + square-root exponent up to polylogarithmic factors, sharper than the + M^{1/2+o(1)} notation suggests. A separate counting argument then proves a + sharp limitation of the one-shot occupation strategy class itself: for + every fixed k, Cartesian tori of k cycles have bounded metric doubling, + exact cop number k+1, and linear one-shot occupation cost at every radius, + so polynomially weak expansion alone cannot certify a universal robustness + theorem -- the obstruction in the universal problem is adaptive reuse + across many weak-growth layers, not degree reduction itself. +tags: + - research + - research/mathematics + - research/graph-theory +keywords: [cops and robber, meyniel's conjecture, graph products, degree reduction, coarse graph projections, expansion, occupation certificates] +authors: + - "Levi Neuwirth | /me.html" +affiliation: + - "Brown University | https://www.brown.edu" +bibliography: data/ball-occupation-paper.bib +preprint: /papers/ball-occupation-paper.pdf +no-collapse: true +status: "Durable" +confidence: proved +evidence: 5 +peer-status: unreviewed +result-shape: mixed +--- + +# Introduction and main conclusions + +The multi-cop version of Cops and Robber was developed by Aigner and Fromme, who proved that three cops suffice on every planar graph [@AignerFromme]. Meyniel's conjecture asks whether every connected $n$-vertex graph has cop number $O(\sqrt n)$. The current best universal upper bound remains $$\frac{n}{2^{(1-o(1))\sqrt{\log_2 n}}},$$ proved independently by Lu–Peng and Scott–Sudakov [@LuPeng; @ScottSudakov]. Bose–Esperet–Hodor–Joret–Micek–Rambaud recently extended the same scale of bound from graph order to vertex-cover number [@BoseEsperetHodorJoretMicekRambaud]. Expansion is one of the principal settings in which polynomial savings are known: Bradshaw–Hosseini–Mohar–Stacho obtain weak Meyniel bounds from bounded-degree expansion restricted to sublinear set scales [@BradshawHosseiniMoharStacho], while Clow's withdrawn preprint developed a closely related structural program connecting failure of weak Meyniel to high-cop expanding examples [@Clow]. + +The motivation here is the effect of bounded-degree replacement gadgets on pursuit. The degree-reduction construction of Hosseini–Mohar–Gonzalez Hermosillo de la Maza (HMGHM) preserves lower bounds on cop number and produces subcubic graphs with cop number $M^{1/2-o(1)}$ [@HMG]. A natural converse question is whether a useful upper strategy on the base graph survives the replacement tower. + +An arbitrary winning strategy does not lift transparently. Moving one step in the quotient may require a squad dispersed through a cloud to reorganize while the robber continues moving. The successful object is narrower and more stable: an *occupation certificate* that assigns distinct cops to all vertices of a region before the robber can leave it. Distance stretching slows both deployment and escape, and a bounded normalized additive error leaves a strict timing margin. + +The first result is therefore stated for an abstract projection, rather than for the HMGHM gadget. The gadget enters only later, through an exact metric calculation. The resulting upper bound is stronger quantitatively than the notation $M^{1/2+o(1)}$ suggests: it is $\sqrt M$ times a polylogarithmic factor. By contrast, the available lower bound approaches the square-root exponent at the triple-logarithmic rate displayed in the abstract. These two facts should not be conflated merely because both can be written $M^{1/2+o(1)}$. + +The final result marks the boundary of the mechanism. A one-shot occupation certificate needs polynomial ball amplification between radii $R$ and $2R$. Polynomially weak expansion alone does not supply this. For every fixed $k$, the Cartesian tori $C_L^{\square k}$ have bounded metric doubling, exact cop number $k+1$, and linear one-shot occupation cost at every radius. Taking $k>1/\delta$ puts these examples inside every window $h(G)\ge |G|^{-\delta}$. A separate cubic replacement retains the barrier at $\delta=1/2$. The obstacle in the universal problem is therefore not degree reduction itself; it is the need for adaptive reuse over many weak-growth layers. + +# Scale-adaptive cores and the robustness window + +For a connected graph $J$, write $$h(J)=\min_{\varnothing\ne A\subseteq V(J),\ |A|\le |J|/2} +\frac{|\partial_J A|}{|A|}.$$ The following elementary reduction explains why polynomially weak expansion is the relevant robustness window for the universal problem. + +::: {#prop-adaptive-core .exhibit .exhibit--proposition data-exhibit-type="proposition" data-exhibit-name="Proposition 1 (Scale-adaptive induced core)"} +**Proposition 1** (Scale-adaptive induced core). *Let $J$ be a connected graph of order $n$, and let $\eta(1)\ge\cdots\ge\eta(n)\ge0$. Then $J$ contains a connected induced subgraph $K$, of order $m$, such that $$\boxed{h(K)\ge\eta(m)} +\qquad\text{and}\qquad +\boxed{c(J)\le c(K)+\sum_{j=m+1}^{n}\eta(j).}$$* +::: + +::: proof +*Proof.* Maintain the connected induced region $J_i$ containing the robber. Whenever $|J_i|=m_i$ and $h(J_i)<\eta(m_i)$, choose $A_i\subseteq V(J_i)$ with $0<|A_i|\le m_i/2$ and $|\partial_{J_i}A_i|<\eta(m_i)|A_i|$, and occupy its boundary. The robber is then confined to one component $J_{i+1}$ of $J_i-\partial_{J_i}A_i$. Whether that component lies inside $A_i$ or outside it, one has $$|A_i|\le m_i-m_{i+1}.$$ Consequently the separator costs at most $$\eta(m_i)(m_i-m_{i+1}) +\le +\sum_{j=m_{i+1}+1}^{m_i}\eta(j).$$ These integer intervals are disjoint along the robber's nested component chain. The process terminates at a connected induced $K$ with $h(K)\ge\eta(|K|)$, and the separator costs telescope to the displayed sum. ◻ +::: + +Taking $\eta(j)=j^{-a}$ shows that a polynomial cop-number saving on subcubic graphs with $h(K)\ge |K|^{-a}$ would imply a weak form of Meyniel for arbitrary graphs after the bounded-degree transfer of Hosseini–Mohar–Gonzalez Hermosillo de la Maza [@HMG]. Bradshaw–Hosseini–Mohar–Stacho already treat constant expansion restricted to sublinear set scales [@BradshawHosseiniMoharStacho]; the unresolved axis in this reduction is expansion that itself shrinks polynomially. + +# Coarse occupation projections + +::: {#def-projection .exhibit .exhibit--definition data-exhibit-type="definition" data-exhibit-name="Definition 2 (Coarse occupation projection)"} +**Definition 2** (Coarse occupation projection). Let $G$ and $H$ be connected graphs. A surjection $\pi:V(H)\to V(G)$ is a $(\lambda,P)$-occupation projection if, writing $F_v=\pi^{-1}(v)$, + +1. $|F_v|\le P$ for every $v\in V(G)$; + +2. for distinct $u,v\in V(G)$ and arbitrary $x\in F_u$, $y\in F_v$, $$\lambda(\operatorname{dist}_G(u,v)-1)+1 + \le + \operatorname{dist}_H(x,y) + \le + \lambda(\operatorname{dist}_G(u,v)+2)-2;$$ + +3. $\operatorname{diam}_H(F_v)\le2(\lambda-1)$ for every $v\in V(G)$. +::: + +The particular constants in [Definition 2](#def-projection) are chosen because they are exact for the HMGHM tower. The proof below only needs a bounded additive slack after division by $\lambda$ and a strict gap between deployment and escape deadlines. + +For $U\subseteq V(G)$, let $B_G(U,r)$ be its closed radius-$r$ neighborhood. + +::: {#thm-abstract-transfer .exhibit .exhibit--theorem data-exhibit-type="theorem" data-exhibit-name="Theorem 3 (Abstract macro-ball occupation transfer)"} +**Theorem 3** (Abstract macro-ball occupation transfer). *Let $G$ have $N$ vertices. Fix $d\ge2$, $R\ge2$, and constants $a,A_0>0$. Assume $$\sqrt N\le d^{R-2}\frac{\sqrt N}{d}\ge N^{1/3},$$ so the polynomial prefactors are negligible. + +Hall's condition therefore holds simultaneously for all macro-balls with probability $1-o(1)$. The total number of sampled tokens is at most $2AP\Theta\sqrt N$ with probability $1-o(1)$, so a deterministic placement of the claimed size exists. + +It remains to compare deadlines. A target $y\in X_v$ lies over some $u\in B_G(v,R)$. Its assigned cop begins over $z$ with $\operatorname{dist}_G(z,u)\le R-2$. If $z\ne u$, the upper distortion bound gives travel time at most $$\lambda((R-2)+2)-2=\lambda R-2.$$ If $z=u$, the fiber-diameter bound gives at most $2(\lambda-1)\le\lambda R-2$, since $R\ge2$. + +To leave $X_v$, the robber must enter a fiber over a base vertex at distance at least $R+1$ from $v$. The lower distortion bound makes this require at least $$\lambda((R+1)-1)+1=\lambda R+1$$ steps. Every vertex of $X_v$ is occupied first, and the cop assigned to the robber's current vertex captures her. ◻ +::: + +::: {#rem-pw-uniform .exhibit .exhibit--remark data-exhibit-type="remark" data-exhibit-name="Remark 4 (The quantifier needed from the random base)"} +*Remark 4* (The quantifier needed from the random base). The use of the lower-growth hypothesis above is graph-uniform, not a per-source-set probability statement. In the dense theorem of Prałat and Wormald, condition (i) of their deterministic Theorem 3.1 is explicitly quantified over *every* source set and radius. Their Theorem 3.4 proves that a single $G(N,p)$ satisfies those hypotheses asymptotically almost surely; its proof unions over the bad source sets and concludes that the growth estimate holds simultaneously for all sets and radii [@PralatWormald]. Thus the external input has the quantifier order required by [Theorem 3](#thm-abstract-transfer). +::: + +# The HMGHM replacement tower + +For a vertex of degree $r$, the HMGHM replacement has one external port for every incident edge. The ports are partitioned into nearly equal classes, and for each pair of classes there is an internal vertex adjacent to every port in the two classes [@HMG]. + +::: {#lem-portgeometry .exhibit .exhibit--lemma data-exhibit-type="lemma" data-exhibit-name="Lemma 5 (One-round port geometry)"} +**Lemma 5** (One-round port geometry). *For every HMGHM replacement cloud of degree at least two:* + +1. *distinct ports are nonadjacent and have distance exactly two;* + +2. *every cloud vertex is within distance at most three of every specified port;* + +3. *the cloud diameter is at most four.* +::: + +::: proof +*Proof.* Two ports in different classes share the internal vertex associated with their class pair. Two ports in the same class share any internal vertex associated with that class and another nonempty class. Since ports are mutually nonadjacent, their distance is exactly two. + +An internal vertex is adjacent to every port in either of two classes. If a specified port lies in neither class, travel to a port in one of the two classes, then through the internal vertex corresponding to that class and the specified port's class, and finally to the specified port. This takes three steps. The diameter bound follows by routing arbitrary endpoints through a specified port. ◻ +::: + +Let $$G=G_0,G_1,\ldots,G_k=H$$ be an iterated HMGHM tower, and let $\pi:V(H)\to V(G)$ map every final vertex to its original ancestor. Put $$F_v=\pi^{-1}(v), +\qquad +P=\max_v|F_v|, +\qquad +\lambda=3^k.$$ + +::: {#thm-metric .exhibit .exhibit--theorem data-exhibit-type="theorem" data-exhibit-name="Theorem 6 (Exact normalized distortion)"} +**Theorem 6** (Exact normalized distortion). *The ancestry projection is a $(3^k,P)$-occupation projection. Explicitly, for distinct base vertices $u,v$, $r=\operatorname{dist}_G(u,v)$, and arbitrary $x\in F_u$, $y\in F_v$, $$\boxed{ +3^k(r-1)+1 +\le +\operatorname{dist}_H(x,y) +\le +3^k(r+2)-2, +}$$ and $$\boxed{\operatorname{diam}_H(F_v)\le2(3^k-1).}$$* +::: + +::: proof +*Proof.* For one round, a shortest path between distinct clouds uses $e\ge r$ external edges. Between consecutive external edges it enters and leaves an intermediate cloud through distinct ports: otherwise it immediately traverses one external edge back. By [Lemma 5](#lem-portgeometry), each intermediate port change costs at least two internal edges, and hence $$\operatorname{dist}_{G_1}(x,y)\ge e+2(e-1)\ge3r-2.$$ For the upper bound, follow a base geodesic. Reaching the first prescribed port costs at most three, each intermediate port change costs two, the external edges cost $r$, and reaching the final endpoint costs at most three. Thus $$\operatorname{dist}_{G_1}(x,y)\le3+r+2(r-1)+3=3r+4.$$ The one-round fiber diameter is at most four. + +The lower and upper affine recurrences are $$L_j(r)=3L_{j-1}(r)-2, +\qquad +U_j(r)=3U_{j-1}(r)+4,$$ with $L_0(r)=U_0(r)=r$. Solving gives $$L_k(r)=3^k(r-1)+1, +\qquad +U_k(r)=3^k(r+2)-2.$$ The diameter recurrence $D_j\le3D_{j-1}+4$, $D_0=0$, gives $D_k\le2(3^k-1)$. ◻ +::: + +The feature that matters is not the number of rounds but the normalized additive error: after division by $3^k$, it remains two quotient layers. By [Theorem 3](#thm-abstract-transfer), any other graph projection with the same three properties inherits the same occupation-certificate transfer. + +# Expansion retention under port-cloud replacement + +::: {#prop-port-exp .exhibit .exhibit--proposition data-exhibit-type="proposition" data-exhibit-name="Proposition 7 (Expansion under connected port replacement)"} +**Proposition 7** (Expansion under connected port replacement). *Let $H$ be obtained from a base graph $G$ by replacing every vertex by a connected cloud of order at most $L_0$, with distinct external ports for the incident base edges. If $\iota(G)$ is the edge-isoperimetric constant of $G$, then $$\boxed{ +\iota(H) +\ge +\frac{1}{2L_0} +\min\left\{1,\frac{\iota(G)}{L_0}\right\}. +}$$* +::: + +::: proof +*Proof.* Let $S\subseteq V(H)$ with $0<|S|\le|H|/2$. In each cloud, classify the majority side and let $\mathcal M$ be the total number of minority vertices. Since every partially cut cloud is connected and has at most $L_0$ vertices, its internal cut contributes at least one edge, so the total internal contribution is at least $\mathcal M/L_0$. + +Let $U$ be the set of base vertices whose clouds have majority in $S$, and put $E=e_G(U,V(G)\setminus U)$. A base cut edge can fail to cross the lifted cut only if one of its two ports is a minority vertex. Distinct base edges use distinct ports, so at most $\mathcal M$ of the $E$ external cut edges fail. Hence $$e_H(S,V(H)\setminus S) +\ge +\frac{\mathcal M}{L_0}+\max\{0,E-\mathcal M\} +\ge +\frac{E+\mathcal M}{2L_0}.$$ Apply the same majority accounting to $S$ or its complement, according as $|U|\le|G|/2$ or not, to obtain $$\min\{|U|,|V(G)\setminus U|\} +\ge +\frac{|S|-\mathcal M}{L_0}.$$ Thus $$E\ge\frac{\iota(G)}{L_0}(|S|-\mathcal M),$$ and consequently $$E+\mathcal M +\ge +\min\left\{1,\frac{\iota(G)}{L_0}\right\}|S|.$$ Combining the inequalities proves the proposition. ◻ +::: + +The HMGHM cloud-size recurrence turns the one-round estimate into a subpolynomial-loss statement in the polylogarithmic-degree regime. If $D$ is the initial maximum degree, $L_i$ is the largest cloud order in round $i$, and $k$ rounds are used, HMGHM prove $$\prod_{i=0}^{k-1}L_i +\le C D^2(\log D)^{\log_2(11/5)}, +\qquad +2^k=O(\log D).$$ Iterating [Proposition 7](#prop-port-exp) therefore gives the following. + +::: {#cor-exp-retention .exhibit .exhibit--corollary data-exhibit-type="corollary" data-exhibit-name="Corollary 8 (Expansion retained by HMGHM reduction)"} +**Corollary 8** (Expansion retained by HMGHM reduction). *Let $H$ be the final subcubic graph obtained from a connected graph $G$ of maximum degree $D\ge4$. Then $$\boxed{ +h(H) +\ge +\frac{\iota(G)}{C D^4(\log D)^{\kappa}}, +\qquad +\kappa=1+2\log_2(11/5)<3.28. +}$$ In particular, if $D=|G|^{o(1)}$ and $\iota(G)=|G|^{-o(1)}$, then $|H|=|G|^{1+o(1)}$ and $h(H)=|H|^{-o(1)}$.* +::: + +::: proof +*Proof.* At every round $\iota(G_i)\le D_i\le L_i$, so [Proposition 7](#prop-port-exp) gives $$\iota(G_{i+1})\ge\frac{\iota(G_i)}{2L_i^2}.$$ Thus $$\iota(H) +\ge +\frac{\iota(G)}{2^k(\prod_iL_i)^2} +\ge +\frac{\iota(G)}{C D^4(\log D)^\kappa}.$$ Since $H$ has maximum degree at most three, its vertex expansion is at least one third of its edge expansion. The order statement follows from the same cloud-product bound. ◻ +::: + +::: {#rem-replacement-products .exhibit .exhibit--remark data-exhibit-type="remark" data-exhibit-name="Remark 9 (Relation to replacement products)"} +*Remark 9* (Relation to replacement products). The regular replacement-product literature proves stronger spectral conclusions under much stronger hypotheses on the clouds; see, for example, Reingold–Vadhan–Wigderson [@ReingoldVadhanWigderson]. [Proposition 7](#prop-port-exp) allows arbitrary connected, nonuniform clouds and consequently gives only a crude isoperimetric estimate. No novelty claim is made here beyond this precise form without a fuller graph-substitution review. +::: + +# A polylogarithmically tight square-root family + +Take $$d=(\log N)^4, +\qquad +p=\frac{d}{N-1}, +\qquad +G\sim G(N,p).$$ Iterate the HMGHM replacement until the graph $H$ is subcubic, and write $M=|H|$. + +With high probability, $\Delta(G)\le2d$. By [Remark 4](#rem-pw-uniform), the dense Prałat–Wormald theorem supplies the uniform lower growth needed above; in the volume range used here it also supplies the matching upper growth [@PralatWormald]. Choose $R$ minimally so that $d^{R-2}\ge\sqrt N$. Since $d$ is polylogarithmic, the scale conditions of [Theorem 3](#thm-abstract-transfer) hold. + +HMGHM give, both globally and along one ancestry fiber, $$P\le C d^2(\log d)^{1.14}, +\qquad +N\le M\le PN.$$ Their shadow strategy gives $c(H)\ge c(G)$, and the random-graph lower bound of Bollobás–Kun–Leader used in their argument [@BollobasKunLeader] yields $$c(G) +\ge +d^{-2}N^{\frac12-\frac{9}{2\log\log d}}.$$ The abstract transfer theorem gives $$c(H) +\le +CP\bigl(d^3+\log(ePN)\bigr)\sqrt N +\le +\sqrt M\,(\log M)^{20+o(1)}.$$ Using $d=(\log N)^4$ and $M=N(\log N)^{O(1)}$ in the lower bound gives the following more informative formulation. + +::: {#thm-hardfamily .exhibit .exhibit--theorem data-exhibit-type="theorem" data-exhibit-name="Theorem 10 (Quantitative HMGHM hard family)"} +**Theorem 10** (Quantitative HMGHM hard family). *There is a sequence of connected subcubic graphs $H$, of order $M\to\infty$, for which $$\boxed{ +M^{\frac12-\frac{9+o(1)}{2\log\log\log M}} +\le +c(H) +\le +\sqrt M\,(\log M)^{20+o(1)}. +}$$ The upper bound is $\sqrt M$ times a polylogarithmic factor. The lower exponent tends to $1/2$ only at a triple-logarithmic rate.* +::: + +The base edge expansion is $\Omega(d)$ with high probability. Since $d=\operatorname{polylog}N$, [Corollary 8](#cor-exp-retention) gives $$h(H)\ge (\log M)^{-O(1)}=M^{-o(1)}.$$ This is exactly the degree regime in which the retention factor is informative; for polynomial initial degree the crude $D^4$ loss can be vacuous. + +::: {#cor-weakexpander .exhibit .exhibit--corollary data-exhibit-type="corollary" data-exhibit-name="Corollary 11 (Square-root weak-expander family)"} +**Corollary 11** (Square-root weak-expander family). *There are connected subcubic graphs satisfying $$h(H)\ge M^{-o(1)} +\qquad\text{and}\qquad +c(H)=M^{1/2+o(1)}.$$ More precisely, they obey the two-sided bounds of [Theorem 10](#thm-hardfamily).* +::: + +::: {#rem-robustness-endpoint .exhibit .exhibit--remark data-exhibit-type="remark" data-exhibit-name="Remark 12 (What is forced, and what is achieved)"} +*Remark 12* (What is forced, and what is achieved). By [Proposition 1](#prop-adaptive-core), polynomially weak expansion is a natural robustness window for weak Meyniel. The HMGHM lower bound alone already forces every proposed estimate $$c(J)\le C\phi^{-p}|J|^{1-\varepsilon+o(1)} +\qquad(h(J)\ge\phi=|J|^{-o(1)})$$ to have $\varepsilon\le1/2$. That restriction predates the upper transfer proved here. The new conclusion is that the known stressing family itself achieves the endpoint order $\sqrt M$ up to polylogarithmic factors, so this family is not an obstruction to a Meyniel-strength theorem on polynomially weak subcubic expanders. +::: + +# Why chase strategies need not transfer + +The abstract theorem deliberately transfers a strategy class, not arbitrary cop number. The smallest example explains the distinction. For a degree-two vertex, one HMGHM cloud is a three-vertex path. Replacing every vertex of $C_3$ therefore produces $C_9$. But $$c(C_3)=1, +\qquad +c(C_9)=2.$$ The one-cop win on $C_3$ is a direct chase/dismantling phenomenon. The subdivision-like stretching destroys it. By contrast, an occupation certificate is synchronized to a deadline: the replacement tower stretches the cops' travel and the robber's escape by the same factor, and the bounded normalized additive slack preserves a strict margin. The examples $K_4$ and the diamond graph exhibit the same one-round increase, so the issue is structural rather than peculiar to one cycle. + +# A one-shot occupation barrier + +::: {#def-occ .exhibit .exhibit--definition data-exhibit-type="definition" data-exhibit-name="Definition 13 (Universal one-shot occupation number)"} +**Definition 13** (Universal one-shot occupation number). For a connected graph $G$ and integer $R\ge0$, let $\operatorname{Occ}_R(G)$ be the minimum size of a finite set $X$ of distinct cop tokens, equipped with a position map $p:X\to V(G)$, such that for every $v\in V(G)$ there is an injection $$f_v:B_G(v,R)\longrightarrow X$$ with $$\operatorname{dist}_G(u,p(f_v(u)))\le R +\qquad\text{for every }u\in B_G(v,R).$$ Different tokens may have the same initial position. After learning the robber's starting vertex, the common prepositioned bank can occupy her entire radius-$R$ ball within $R$ moves. +::: + +::: {#rem-target-deadline .exhibit .exhibit--remark data-exhibit-type="remark" data-exhibit-name="Remark 14 (Why the target and deadline are natural)"} +*Remark 14* (Why the target and deadline are natural). If the robber starts at $v$, she needs at least $R+1$ robber moves to leave $B_G(v,R)$. Occupying that whole ball within $R$ cop moves is therefore the canonical one-shot certificate: every vertex she could still occupy is filled before her first possible escape. The parameter $\operatorname{Occ}_R$ measures this specific strategy class, not ordinary cop number. +::: + +::: {#thm-occ-lower .exhibit .exhibit--theorem data-exhibit-type="theorem" data-exhibit-name="Theorem 15 (Counting barrier)"} +**Theorem 15** (Counting barrier). *Every connected graph satisfies $$\boxed{ +\operatorname{Occ}_R(G) +\ge +\frac{\sum_{v\in V(G)}|B_G(v,R)|} +{\max_{x\in V(G)}|B_G(x,2R)|}. +}$$ In particular, if $G$ is vertex-transitive, then $$\boxed{ +\operatorname{Occ}_R(G) +\ge +|V(G)|\frac{|B_G(o,R)|}{|B_G(o,2R)|}. +}$$* +::: + +::: proof +*Proof.* Fix a feasible multiset $X$. For each possible robber start $v$, every cop token used by the injection $f_v$ lies in $B_G(v,2R)$, by the triangle inequality. Hence at least $|B_G(v,R)|$ tokens of $X$ lie in $B_G(v,2R)$. Summing over $v$, the number of incident pairs $(v,x)$ with $x\in X\cap B_G(v,2R)$ is at least $\sum_v|B_G(v,R)|$. + +A fixed token based at $x$ is counted only for starts $v\in B_G(x,2R)$, at most $\max_y|B_G(y,2R)|$ times. Therefore $$|X|\max_y|B_G(y,2R)| +\ge +\sum_v|B_G(v,R)|,$$ which proves the claim. ◻ +::: + +The theorem identifies the exact growth ratio demanded by one-shot occupation. A polynomial saving from the trivial $|V(G)|$ bound requires polynomial amplification from radius $R$ to radius $2R$. + +We now give subcubic witnesses showing that polynomially weak expansion does not imply such amplification. + +::: {#def-qt .exhibit .exhibit--definition data-exhibit-type="definition" data-exhibit-name="Definition 16 (The cubic truncated torus)"} +**Definition 16** (The cubic truncated torus). For $L\ge5$, let $Q_L$ have vertex set $$(\mathbb Z/L\mathbb Z)^2\times\mathbb Z/4\mathbb Z.$$ Inside each fiber $(x,y)\times\mathbb Z/4\mathbb Z$, join the four vertices in a cycle. Add the external edges $$(x,y,0)(x,y+1,2) +\qquad\text{and}\qquad +(x,y,1)(x+1,y,3)$$ for every $(x,y)$. Equivalently, $(x,y,2)$ receives its external edge from $(x,y-1,0)$, and $(x,y,3)$ receives its external edge from $(x-1,y,1)$. Thus every vertex has two internal cycle neighbors and one external neighbor, and $Q_L$ is the four-cycle port replacement of the square torus $C_L\square C_L$. +::: + +Every vertex of $Q_L$ has degree three, and the construction embeds on the torus by replacing each base vertex inside a small disk. Its order is $4L^2$. + +::: {#lem-doubling .exhibit .exhibit--lemma data-exhibit-type="lemma" data-exhibit-name="Lemma 17 (Vertex transitivity and explicit doubling of Q_L)"} +**Lemma 17** (Vertex transitivity and explicit doubling of $Q_L$). *The graph $Q_L$ is vertex-transitive and, for every vertex $x$ and radius $R\ge0$, $$|B_{Q_L}(x,2R)|\le 5500\,|B_{Q_L}(x,R)|.$$* +::: + +::: proof +*Proof.* Translations in the first two coordinates are automorphisms. The map $$\rho(x,y,i)=(y,-x,i+1)$$ (with coordinates interpreted cyclically) preserves internal cycle edges and interchanges the two external edge directions. Translations together with $\rho$ act transitively. + +Let $T_L=C_L\square C_L$ and project $(x,y,i)$ to $(x,y)$. Projection does not increase distance, so $$|B_{Q_L}(x,2R)|\le4|B_{T_L}(\pi x,2R)| +\le4\min\{L,4R+1\}^2.$$ Conversely, from an arbitrary cloud vertex one can enter the required port in at most two internal moves and then lift each base step using at most three moves. Hence, with $r=\lfloor(R-2)/3\rfloor$ for $R\ge2$, $$|B_{Q_L}(x,R)|\ge |B_{T_L}(\pi x,r)|.$$ The coordinate box of cyclic radius $\lfloor r/2\rfloor$ lies inside the $\ell_1$ ball, so $$|B_{T_L}(\pi x,r)| +\ge \min\{L,2\lfloor r/2\rfloor+1\}^2.$$ For $R<10$, the upper bound is at most $4\cdot37^2<5500$ and the denominator is at least one. For $R\ge10$, one has $r\ge R/6$ and $2\lfloor r/2\rfloor+1\ge r$, whence the ratio is at most $4\cdot30^2<5500$. This proves the displayed constant. ◻ +::: + +::: {#thm-cubic-barrier .exhibit .exhibit--theorem data-exhibit-type="theorem" data-exhibit-name="Theorem 18 (Cubic one-shot barrier)"} +**Theorem 18** (Cubic one-shot barrier). *For $L\ge5$, the connected cubic graphs $Q_L$, with $M=|Q_L|=4L^2$, satisfy $$\boxed{ +h(Q_L)=\Theta(M^{-1/2}), +\qquad +c(Q_L)\le3, +\qquad +\operatorname{Occ}_R(Q_L)\ge \frac{M}{5500} +\quad\text{for every }R\ge0. +}$$* +::: + +::: proof +*Proof.* The square torus has edge and vertex expansion $\Theta(1/L)$ by the discrete torus isoperimetric inequality [@BollobasLeader]. Applying [Proposition 7](#prop-port-exp) with cloud size four gives the matching lower bound for $Q_L$; lifting a coordinate slab of width $\lfloor L/2\rfloor$ gives the upper bound for every $L$. Since $Q_L$ is cubic, edge and vertex expansion differ by at most a constant factor. Thus $h(Q_L)=\Theta(1/L)=\Theta(M^{-1/2})$. + +The graph $Q_L$ is toroidal, and every toroidal graph has cop number at most three [@Lehner]. Vertex transitivity, [Theorem 15](#thm-occ-lower), and [Lemma 17](#lem-doubling) give $$\operatorname{Occ}_R(Q_L) +\ge +M\frac{|B_{Q_L}(x,R)|}{|B_{Q_L}(x,2R)|} +\ge \frac{M}{5500}.$$ The finite audit suggests that the optimal asymptotic constant is $1/4$, but that sharpening is not needed here. ◻ +::: + +## The barrier throughout every polynomial expansion window + +For integers $k\ge2$ and $L\ge4$, write $$T_{L,k}=\underbrace{C_L\square\cdots\square C_L}_{k\text{ factors}}.$$ Its order is $m=L^k$, its degree is $2k$, and its metric is the cyclic $\ell_1$ metric. + +::: {#lem-ktorus-doubling .exhibit .exhibit--lemma data-exhibit-type="lemma" data-exhibit-name="Lemma 19 (Uniform doubling of Cartesian tori)"} +**Lemma 19** (Uniform doubling of Cartesian tori). *For every fixed $k\ge2$, every $L\ge4$, every vertex $x$, and every radius $R\ge0$, $$|B_{T_{L,k}}(x,2R)|\le (5k)^k|B_{T_{L,k}}(x,R)|.$$* +::: + +::: proof +*Proof.* Every coordinate of a point in $B(x,2R)$ has cyclic distance at most $2R$, so $$|B(x,2R)|\le \min\{L,4R+1\}^k.$$ The coordinate box in which every coordinate has cyclic distance at most $\lfloor R/k\rfloor$ lies in $B(x,R)$, and therefore $$|B(x,R)|\ge\min\{L,2\lfloor R/k\rfloor+1\}^k.$$ If $R0$ there is a constant-degree graph family with $$h(G)\ge |G|^{-\delta}, +\qquad +c(G)=O_\delta(1), +\qquad +\operatorname{Occ}_R(G)=\Omega_\delta(|G|) +\quad\text{for every radius }R.$$* +::: + +::: proof +*Proof.* The discrete-torus edge-isoperimetric inequality gives order $1/L$ [@BollobasLeader]. Since $T_{L,k}$ has degree $2k$, edge boundary and external vertex boundary differ by at most the fixed factor $2k$; a coordinate slab of width $\lfloor L/2\rfloor$ supplies the matching upper bound. Hence $h(T_{L,k})=\Theta_k(1/L)=\Theta_k(m^{-1/k})$. Neufeld and Nowakowski proved that a Cartesian product of $k$ cycles, each of length at least four, has cop number exactly $k+1$ [@NeufeldNowakowski]. Since the torus is vertex-transitive, [Theorem 15](#thm-occ-lower) and [Lemma 19](#lem-ktorus-doubling) give the occupation lower bound. + +Given $\delta>0$, choose $k=\max\{2,\lfloor1/\delta\rfloor+1\}$. Then $1/k<\delta$, so for sufficiently large $m$ the expansion lower bound $h(T_{L,k})\ge m^{-\delta}$ holds after absorbing the fixed $k$-dependent constant. ◻ +::: + +::: {#rem-sharp-metric-constant .exhibit .exhibit--remark data-exhibit-type="remark" data-exhibit-name="Remark 21 (The sharp metric constant)"} +*Remark 21* (The sharp metric constant). For fixed $k$, choose radii $1\ll R\ll L$. Lattice-point asymptotics for the $\ell_1$ ball give $$\frac{|B_{T_{L,k}}(x,2R)|}{|B_{T_{L,k}}(x,R)|}=2^k+o(1).$$ Thus no uniform doubling constant below $2^k$ is possible, and the counting bound of [Theorem 15](#thm-occ-lower) approaches the natural fraction $2^{-k}m$ on these local radii. The explicit constant $(5k)^{-k}$ is chosen only for a short all-radii proof. +::: + +::: {#cor-no-exp-occ .exhibit .exhibit--corollary data-exhibit-type="corollary" data-exhibit-name="Corollary 22 (No expansion-only one-shot theorem)"} +**Corollary 22** (No expansion-only one-shot theorem). *For every $\delta>0$, there is no implication of the form $$h(G)\ge |G|^{-\delta} +\quad\Longrightarrow\quad +\operatorname{Occ}_R(G)\le |G|^{1-\varepsilon} +\text{ for some radius $R$}$$ with any fixed $\varepsilon>0$, even when the maximum degree is bounded by a constant depending only on $\delta$.* +::: + +::: proof +*Proof.* Take the family from [Theorem 20](#thm-full-window). It lies in the prescribed expansion window, while $\operatorname{Occ}_R(G)=\Omega_\delta(|G|)$ for every $R$. ◻ +::: + +::: {#rem-architectural-meaning .exhibit .exhibit--remark data-exhibit-type="remark" data-exhibit-name="Remark 23 (Architectural meaning)"} +*Remark 23* (Architectural meaning). The logical obstruction is not that tori are difficult pursuit instances; they are not. Rather, [Theorem 15](#thm-occ-lower) makes every vertex-transitive bounded-doubling graph expensive for one-shot occupation, and bounded doubling is compatible with every polynomial weak-expansion window by [Theorem 20](#thm-full-window). The tori certify that the hypothesis class contains such graphs while coordinate-wise shadowing still uses exactly $k+1$ cops. Therefore ball amplification is the wrong invariant for adaptive pursuit, not merely a poor description of one particular easy family. The cubic family $Q_L$ records that the same separation already occurs in maximum degree three at exponent $1/2$. +::: + +# Outlook + +For vertex expansion $h(G)\ge\phi$, iterating the elementary growth factor $1+\phi$ reaches global scale after $O(\phi^{-1}\log |G|)$ layers. The same iteration gives the standard diameter bound of that order; these are two forms of the same calculation, not independent evidence. The strategic consequence is that, when $\phi=|G|^{-a}$, an amplification-based pursuit scheme must operate over a full-traversal timescale $\Theta(|G|^a\log|G|)$. + +The present paper separates three phenomena: + +1. bounded normalized metric distortion preserves a strong occupation certificate through degree reduction; + +2. the HMGHM stressing family itself meets the square-root endpoint up to polylogarithmic factors; + +3. one-shot occupation is nevertheless incapable of proving a universal robustness theorem throughout any polynomial weak-expansion window, even on bounded-degree graphs with constant cop number; a cubic instance already appears at exponent $1/2$. + +The remaining universal question is therefore an adaptive one. On the tori, ball growth carries essentially no information about pursuit cost; product structure instead supports coordinate-wise shadowing. What geometric or combinatorial quantity replaces product coordinates on a general polynomially weak expander? Equivalently, can a capacitated, correlated, or deferred witness system reuse the same cop resources over polynomially many weak-growth layers, or must every such one-traversal certificate incur polynomial congestion? + +# Acknowledgments + +The author is grateful to Anthony Clow, Peter Bradshaw, Bojan Mohar, and Florian Lehner for work and perspectives that helped shape the questions addressed here. Additional acknowledgments will be added in a later version. The author welcomes corrections concerning priority, related graph-substitution inequalities, and the scope of the occupation framework. + +# Audit and reproducibility + +The metric inequalities were independently tested on HMGHM towers rebuilt from the published gadget description, including structured base graphs not used in the original audit. The Hall inequalities and timing margins were checked numerically, and exact small replacement games were solved by retrograde analysis. The toroidal barrier audit computes exact ball profiles of $C_L^{\square k}$ by convolving cyclic distance distributions, verifies the $(5k)^k$ doubling bound for $k=2,3,4,5$, constructs $Q_L$, checks cubicity, connectivity, and the displayed rotation automorphism, and evaluates the counting lower bound at every radius. No theorem depends on the computations. diff --git a/content/essays/branch-based-local-capture-in-tree-balls/index.md b/content/essays/branch-based-local-capture-in-tree-balls/index.md index ab39cc8..65f20df 100644 --- a/content/essays/branch-based-local-capture-in-tree-balls/index.md +++ b/content/essays/branch-based-local-capture-in-tree-balls/index.md @@ -64,7 +64,7 @@ Graphs are simple and undirected. The local results allow finite or infinite gra \qquad S_j(v)=\{x\in V(G):\operatorname{dist}(x,v)=j\}.$$ -::: {#def-tree-ball .definition} +::: {#def-tree-ball .exhibit .exhibit--definition data-exhibit-type="definition" data-exhibit-name="Definition 1 (Tree-ball)"} **Definition 1** (Tree-ball). The ball $B_R(v_0)$ is a *tree-ball* if the induced subgraph $G[B_R(v_0)]$ is a tree. ::: @@ -72,7 +72,7 @@ This is a local condition at the specified center. The global condition $\operat An induced tree-ball does not control shortest paths between arbitrary pairs of its vertices: a competing path may leave the ball and re-enter. The required radius depends on the endpoint depths and on the length of their path inside the tree-ball. -::: {#lem-buffer .lemma} +::: {#lem-buffer .exhibit .exhibit--lemma data-exhibit-type="lemma" data-exhibit-name="Lemma 2 (Endpoint-sensitive tree-ball geodesics)"} **Lemma 2** (Endpoint-sensitive tree-ball geodesics). *Suppose $B_q(v_0)$ is a tree-ball, let $x,y\in B_q(v_0)$, and let $L$ be the length of the $x$–$y$ path in $G[B_q(v_0)]$. If $$\operatorname{dist}(x,v_0)+\operatorname{dist}(y,v_0)+L\leq 2q,$$ then every ambient $x$–$y$ geodesic lies in $B_q(v_0)$. Consequently, that geodesic is unique and equals the $x$–$y$ path in $G[B_q(v_0)]$.* ::: @@ -83,7 +83,7 @@ An induced tree-ball does not control shortest paths between arbitrary pairs of \leq q.$$ Thus $P\subseteq B_q(v_0)$. Since the induced graph on this ball is a tree, $P$ is its unique $x$–$y$ path. □ ::: -::: remark +::: {#rem-uniform-buffers .exhibit .exhibit--remark data-exhibit-type="remark" data-exhibit-name="Remark 3 (Uniform buffers versus chase endpoints)"} **Remark 3** (Uniform buffers versus chase endpoints). For arbitrary $x\in B_R(v_0)$ and $y\in B_t(v_0)$, the walk from $x$ to $y$ through $v_0$ has length at most $R+t$. If $B_{R+t}(v_0)$ is a tree-ball, the path between $x$ and $y$ in the rooted tree $G[B_{R+t}(v_0)]$ is no longer than this walk, so Lemma [2](#lem-buffer) applies. For $R,t\geq1$, this uniform radius cannot in general be reduced. Put $L=R+t$, begin with the rooted $d$-regular tree truncated at depth $L-1$, and choose vertices $x$ and $y$ at depths $R$ and $t$ in distinct root branches. Choose descendants $a$ of $x$ and $b$ of $y$ in $S_{L-1}(v_0)$, allowing equality, add a new vertex $z$, and join $z$ to $a$ and $b$. Complete the graph explicitly: for each remaining degree deficit at a vertex of $S_{L-1}(v_0)\cup\{z\}$, attach by one edge a separate infinite rooted $(d-1)$-ary tree. Every vertex then has degree $d$, and the resulting graph is infinite, simple, and connected. The induced ball $B_{L-1}(v_0)$ is still the original tree. The path from $x$ to $y$ through $v_0$ has length $L$, while the path through $z$ has length $$\operatorname{dist}(x,a)+2+\operatorname{dist}(b,y)=(t-1)+2+(R-1)=L.$$ Each attached infinite tree meets the displayed core at only one vertex, so it creates no additional route between $x$ and $y$. Thus the two displayed paths are distinct geodesics. This proves sharpness at radius $R+t-1$ among possibly infinite $d$-regular graphs. @@ -93,11 +93,11 @@ The endpoint pairs used along a prescribed length-$t$ nonbacktracking path are m Whenever $B_q(v_0)$ is a tree-ball, we root $G[B_q(v_0)]$ at $v_0$. Every vertex other than $v_0$ has a unique parent, and every vertex of depth less than $q$ has $d-1$ children. -::: {#def-cone .definition} +::: {#def-cone .exhibit .exhibit--definition data-exhibit-type="definition" data-exhibit-name="Definition 4 (Geodesic cone)"} **Definition 4** (Geodesic cone). Suppose $B_r(v)$ is a tree-ball and $u\in N(v)$. The *geodesic cone* through $u$ at radius $r$ is $$C_u(v,r):=\{x\in B_r(v)\setminus\{v\}:\text{the path from $x$ to $v$ in $B_r(v)$ has penultimate vertex $u$}\}.$$ ::: -::: {#def-tube .definition} +::: {#def-tube .exhibit .exhibit--definition data-exhibit-type="definition" data-exhibit-name="Definition 5 (Path tube)"} **Definition 5** (Path tube). Let $R\geq t$, suppose $B_R(v_0)$ is a tree-ball, and let $$\sigma=(v_0,v_1,\ldots,v_t)$$ be a length-$t$ nonbacktracking path starting at $v_0$: consecutive vertices are adjacent and $v_{i+1}\neq v_{i-1}$ whenever the latter condition is defined. The *length-$t$ tube* associated with $\sigma$ is $$T_\sigma(R):=\{x\in B_R(v_0):\text{the rooted path from $x$ to $v_0$ contains $v_t,v_{t-1},\ldots,v_1$ in order}\}.$$ Equivalently, $T_\sigma(R)$ is the set of descendants of $v_t$ lying in $B_R(v_0)$. ::: @@ -127,7 +127,7 @@ Finally, Section [5](#sec-profile-loss) records information lost by finite-order # Cone and Tube Counts -::: {#lem-shell .lemma} +::: {#lem-shell .exhibit .exhibit--lemma data-exhibit-type="lemma" data-exhibit-name="Lemma 6 (Sharp shell and cone counts)"} **Lemma 6** (Sharp shell and cone counts). *Let $G$ be $d$-regular with $d\geq3$, and suppose $B_r(v)$ is a tree-ball. Then for every $u\in N(v)$ and $1\leq j\leq r$, $$|C_u(v,r)\cap S_j(v)|=(d-1)^{j-1}, \qquad |S_j(v)|=d(d-1)^{j-1}.$$ Hence $$|C_u(v,r)|=\sum_{j=1}^r(d-1)^{j-1} @@ -140,7 +140,7 @@ Finally, Section [5](#sec-profile-loss) records information lost by finite-order For $t=1$, geodesic cones are exactly the path tubes. We now count the general length-$t$ objects that drive the persistence argument. -::: {#lem-tube-partition .lemma} +::: {#lem-tube-partition .exhibit .exhibit--lemma data-exhibit-type="lemma" data-exhibit-name="Lemma 7 (Tube count and partition)"} **Lemma 7** (Tube count and partition). *Let $R\geq t$, and suppose $B_R(v_0)$ is a tree-ball. For every length-$t$ nonbacktracking path $\sigma$ starting at $v_0$, $$|T_\sigma(R)| =\sum_{j=t}^R(d-1)^{j-t} =\frac{(d-1)^{R-t+1}-1}{d-2}.$$ The length-$t$ tubes are pairwise disjoint and form a partition $$B_R(v_0)\setminus B_{t-1}(v_0) @@ -164,7 +164,7 @@ N_tq_{t,R} ## The interception schedule -::: {#lem-interception .lemma} +::: {#lem-interception .exhibit .exhibit--lemma data-exhibit-type="lemma" data-exhibit-name="Lemma 8 (Interception schedule along a tube)"} **Lemma 8** (Interception schedule along a tube). *Let $R\geq t$, suppose $B_R(v_0)$ is a tree-ball, and let $$\sigma=(v_0,v_1,\ldots,v_t)$$ be a length-$t$ nonbacktracking path starting at $v_0$. Let a cop start at $c\in T_\sigma(R)$ at depth $j=\operatorname{dist}(c,v_0)$. Suppose the robber follows $\sigma$ for as long as the path survives. For every $s\in\{1,\ldots,t\}$ such that the path has survived through round $s-1$, after the cop move in round $s$:* 1. *if $j=2s-1$, the cop reaches $v_{s-1}$ and captures the robber;* @@ -182,13 +182,13 @@ N_tq_{t,R} At $k=s_*$, if $j$ is odd then $j=2s_*-1$ and the cop reaches $v_{s_*-1}$; if $j$ is even then $j=2s_*$ and the cop reaches $v_{s_*}$. This proves the three cases. If $j\leq2s-2$, then $s_*\leq s-1$, so the scheduled capture or blockage prevents survival through round $s-1$. □ ::: -::: remark +::: {#rem-odd-even-depths .exhibit .exhibit--remark data-exhibit-type="remark" data-exhibit-name="Remark 9 (Odd and even initial depths as capture and blockage times)"} **Remark 9**. Odd and even initial depths encode potential capture and blockage times, respectively. If the corresponding time is at most $t$ and the prescribed play reaches it, the event occurs then. If $\lceil j/2\rceil>t$, that time lies beyond the horizon and the cop remains descendant pressure throughout the first $t$ reached rounds. ::: ## Persistence along nonbacktracking paths -::: {#thm-persistence .theorem} +::: {#thm-persistence .exhibit .exhibit--theorem data-exhibit-type="theorem" data-exhibit-name="Theorem 10 (Nonbacktracking path-tube persistence)"} **Theorem 10** (Nonbacktracking path-tube persistence). *Let $d\geq3$ and $1\leq t\leq R$, suppose $B_R(v_0)$ is a tree-ball, and let $X_0$ be any initial cop configuration satisfying $$X_0(T_\sigma(R))\geq1$$ for every length-$t$ tube. Cops outside $B_R(v_0)$ are permitted; the proof ignores them unless they cause capture, blockage, or additional branch support. If $X_0(\{v_0\})>0$, the robber is captured before any move. Otherwise, let $$\tau=(v_0,v_1,\ldots,v_t)$$ be any length-$t$ nonbacktracking path starting at $v_0$. At every round $s\in\{1,\ldots,t\}$ for which the robber has followed $\tau$ through round $s-1$, at least one of the following occurs during round $s$:* 1. *on the cops' move, a cop captures the robber at $v_{s-1}$;* @@ -213,7 +213,7 @@ The two witnesses contribute to distinct branches, proving (c). □ ## The one-round case -::: {#cor-one-round .corollary} +::: {#cor-one-round .exhibit .exhibit--corollary data-exhibit-type="corollary" data-exhibit-name="Corollary 11 (One-round persistence)"} **Corollary 11** (One-round persistence). *Let $R\geq1$, suppose $B_R(v_0)$ is a tree-ball, and let $X_0$ be a cop configuration satisfying $$X_0(C_u(v_0,R))\geq1 \qquad\text{for every }u\in N(v_0).$$ For every proposed neighboring first move $v_0\to w$, the robber is captured at $v_0$ (initially or on the first cop move), blocked at $w$, or, after a legal move to $w$, faces branch-load support of size at least two.* ::: @@ -226,7 +226,7 @@ The two witnesses contribute to distinct branches, proving (c). □ Theorem [10](#thm-persistence) uses a strong static hypothesis: every length-$t$ nonbacktracking path starting at $v_0$ indexes an occupied tube. We now quantify the exact deterministic cost and the conditional random-sampling threshold of this local certificate. These costs are not cop-number bounds and do not establish a robber-independent global placement. -::: {#prop-deterministic-cost .proposition} +::: {#prop-deterministic-cost .exhibit .exhibit--proposition data-exhibit-type="proposition" data-exhibit-name="Proposition 12 (Deterministic coverage cost)"} **Proposition 12** (Deterministic coverage cost). *Suppose $B_R(v_0)$ is a tree-ball with $R\geq t$, and let $X$ be any cop configuration. Then $X$ occupies every length-$t$ tube if and only if $$X(T_\sigma(R))\geq1$$ for every member of the family of $N_t=d(d-1)^{t-1}$ pairwise disjoint tubes. Consequently:* 1. *every such configuration satisfies $|X|\geq N_t$, counting cops with multiplicity;* @@ -238,7 +238,7 @@ Theorem [10](#thm-persistence) uses a strong static hypothesis: every length-$t$ *Proof.* This is immediate from Lemma [7](#lem-tube-partition). □ ::: -::: {#thm-sampling-threshold .theorem} +::: {#thm-sampling-threshold .exhibit .exhibit--theorem data-exhibit-type="theorem" data-exhibit-name="Theorem 13 (Conditional uniform-sampling threshold)"} **Theorem 13** (Conditional uniform-sampling threshold). *Fix $d\geq3$. For each positive integer $t$, let $G_t$ be a $d$-regular graph with distinguished vertex $v_{0,t}$, and let $R_t\geq t$ be such that $B_{R_t}^{G_t}(v_{0,t})$ is a tree-ball. Let $\mathcal P_t$ be the set of length-$t$ nonbacktracking paths from $v_{0,t}$, and let $T_{\sigma,t}$ be the tube of $\sigma\in\mathcal P_t$ in that ball. Set $$N_t:=|\mathcal P_t|=d(d-1)^{t-1}, \qquad q_t:=\frac{|T_{\sigma,t}|}{|B_{R_t}^{G_t}(v_{0,t})|}.$$ For each $t$, after the rooted ball has been fixed, sample $m_t$ positions independently and uniformly with replacement from $B_{R_t}^{G_t}(v_{0,t})$. Let $\mathcal C_t$ be the event that every $T_{\sigma,t}$ contains at least one sample. For every fixed $\varepsilon$ with $0<\varepsilon<1$:* @@ -274,7 +274,7 @@ I_{\sigma,t}:=\mathbf 1_{\{T_{\sigma,t}\text{ receives no sample}\}}.$$ Then $\m =\frac1{1-(d-1)^{-(h_t+1)}}+O((d-1)^{-t})$$ uniformly for $h_t\geq0$, proving the remaining assertions. □ ::: -::: {#cor-polylog-horizon .corollary} +::: {#cor-polylog-horizon .exhibit .exhibit--corollary data-exhibit-type="corollary" data-exhibit-name="Corollary 14 (Finite horizon from polylogarithmic conditional sampling)"} **Corollary 14** (Finite horizon from polylogarithmic conditional sampling). *Fix $d\geq3$. Let $(G_n)$ be a sequence of $n$-vertex $d$-regular graphs with distinguished vertices $v_{0,n}$ and integers $1\leq t_n\leq R_n$ such that $B_{R_n}^{G_n}(v_{0,n})$ is a tree-ball. After each rooted ball is fixed, sample $m_n$ positions independently and uniformly with replacement from it, and let $\mathcal C_n$ be the event that every length-$t_n$ tube rooted at $v_{0,n}$ is occupied.* *Call $(m_n)$ *polylogarithmic* if $m_n=O((\log n)^C)$ for some fixed $C>0$. If $(m_n)$ is polylogarithmic and $\Pr(\mathcal C_n)\to1$, then $t_n=O(\log\log n)$. If $t_n=\Theta(\log n)$ and $\Pr(\mathcal C_n)\to1$, then $$m_n=\Omega(N_{t_n}\log N_{t_n}), @@ -288,7 +288,7 @@ N_{t_n}=d(d-1)^{t_n-1};$$ in particular, $m_n\geq n^c$ eventually for some $c>0$ If $t_n=\Theta(\log n)$ and $m_n=o(N_{t_n}\log N_{t_n})$ along a subsequence, pass if necessary to a further subsequence with strictly increasing horizons and apply the same subsequence argument. This contradicts $\Pr(\mathcal C_n)\to1$. Thus $m_n=\Omega(N_{t_n}\log N_{t_n})$. Since $N_{t_n}=n^{\Omega(1)}$, this is at least $n^c$ eventually for some $c>0$. □ ::: -::: {#rem-partial-coverage .remark} +::: {#rem-partial-coverage .exhibit .exhibit--remark data-exhibit-type="remark" data-exhibit-name="Remark 15 (Partial coverage is cheaper)"} **Remark 15** (Partial coverage is cheaper). At one fixed, known root, the expected fraction of length-$t$ tubes occupied by $m$ independent uniform samples is $$1-(1-q_{t,R})^m.$$ Since $q_{t,R}=\Theta(N_t^{-1})$, $m=\Theta(N_t)$ samples occupy a fixed positive fraction of the tubes in expectation, while $m=\omega(N_t)$ occupies a $1-o(1)$ fraction in expectation. Under uniform sampling, reducing the expected number of empty tubes to $O(1)$ requires $\Theta(N_t\log N_t)$ samples; the same order is required to leave at most $O(1)$ tubes empty with probability tending to one. These are occupancy statements only: they give neither an adversarial guarantee over all paths from that root nor a root-independent placement, and they do not show that partial coverage supports an adaptive chase. ::: @@ -304,7 +304,7 @@ Fix integers $Q\geq r\geq1$ and $k\geq0$, and suppose $B_Q(v_0)$ is a tree-ball. \right).$$ It records the complete configuration through depth $r-1$ and the exact depth histogram in every length-$r$ tube; the $j=r$ coordinates also recover the multiplicities at depth $r$. It forgets only how cops at a fixed greater depth split among descendants below the terminal vertex of a length-$r$ prefix. For an admissible profile $P$, define its fixed-universe fiber by $$\mathcal F_{r;Q,k}(P) :=\{X\in\mathcal C_{Q,k}(v_0):\Pi_{r;Q,k}(X)=P\}.$$ -::: {#prop-profile-nondeterminacy .proposition} +::: {#prop-profile-nondeterminacy .exhibit .exhibit--proposition data-exhibit-type="proposition" data-exhibit-name="Proposition 16 (Augmented tube profiles do not determine later support)"} **Proposition 16** (Augmented tube profiles do not determine later support). *Fix $d\geq3$ and $r\geq1$, put $Q=2r+3$, and suppose $B_Q(v_0)$ is a tree-ball. There exist $X,Y\in\mathcal C_{Q,2}(v_0)$ and a length-$(r+1)$ nonbacktracking path $\tau=(v_0,\ldots,v_{r+1})$ such that $$\Pi_{r;Q,2}(X)=\Pi_{r;Q,2}(Y).$$ The path survives all $r+1$ rounds from both configurations, with all relevant cop moves unique, but immediately after the robber's legal move to $v_{r+1}$ the branch-load supports in $X$ and $Y$ have sizes $2$ and $1$, respectively. Hence, for this fixed path $\tau$, the predicate that the branch-load support after $r+1$ safe rounds has size at least $2$ does not factor through $\Pi_{r;Q,2}$.* ::: diff --git a/content/essays/near-critical-growing-radius-domination.md b/content/essays/near-critical-growing-radius-domination.md index 4a8a6b8..bc56953 100644 --- a/content/essays/near-critical-growing-radius-domination.md +++ b/content/essays/near-critical-growing-radius-domination.md @@ -197,7 +197,7 @@ with $0\log 0=0$; for a scalar $a\in[0,1]$, $H(a)$ denotes the binary entropy $H | $z=e^\theta$ | grand-canonical activity | | $b=d-1,\ D=d/(d-2)$ | recurring degree constants | -::: {#def-two-path .definition} +::: {#def-two-path .exhibit .exhibit--definition data-exhibit-type="definition" data-exhibit-name="Definition 1 (Internally two-path $(h,2)$ domination)"} **Definition 1** (Internally two-path $(h,2)$ domination). A set $S\subseteq V(G)$ is *internally two-path $(h,2)$ dominating* if every vertex $v\notin S$ has two $v$–$S$ paths of length at most $h$ whose only common vertex is $v$. In particular, the paths begin through distinct neighbors of $v$. Let $\gamma_{h,2}^{\mathrm{int}}(G)$ denote the minimum size of such a set. ::: @@ -225,7 +225,7 @@ $$ uniformly in $h$. Here $\Psi_{d,h}$ is the exact annealed variational exponent. -::: {#thm-bounded-critical-window .theorem} +::: {#thm-bounded-critical-window .exhibit .exhibit--theorem data-exhibit-type="theorem" data-exhibit-name="Theorem 2 (Bounded annealed critical window)"} **Theorem 2** (Bounded annealed critical window). *Put $L_h=\log B_h$. For every fixed $M<\infty$, uniformly for* $$ @@ -247,7 +247,7 @@ $$ *All $o(1)$ terms are uniform over the displayed window.* ::: -::: {#cor-critical-scaling-function .corollary} +::: {#cor-critical-scaling-function .exhibit .exhibit--corollary data-exhibit-type="corollary" data-exhibit-name="Corollary 3 (Critical scaling function)"} **Corollary 3** (Critical scaling function). *Uniformly for bounded $s$,* $$ @@ -257,7 +257,7 @@ $$ $$ ::: -::: {#cor-annealed-zero-expansion .corollary} +::: {#cor-annealed-zero-expansion .exhibit .exhibit--corollary data-exhibit-type="corollary" data-exhibit-name="Corollary 4 (Annealed zero and scalar expansion)"} **Corollary 4** (Annealed zero and scalar expansion). *Let* $$ @@ -283,7 +283,7 @@ C_h^{\mathrm{ann}}=L-2\ell+\frac{3\ell-1}{L}+\frac{5\ell^2-12\ell+3}{2L^2}+\frac $$ ::: -::: {#thm-fixed-slack-random .theorem} +::: {#thm-fixed-slack-random .exhibit .exhibit--theorem data-exhibit-type="theorem" data-exhibit-name="Theorem 5 (Fixed-slack random-regular lower bound)"} **Theorem 5** (Fixed-slack random-regular lower bound). *Fix $\omega>0$. If $h=h(n)\to\infty$ and* $$ @@ -301,7 +301,7 @@ $$ The following older-form consequences retain uniform information deeper in the subcritical region and will be useful for comparison. -::: {#thm-near-critical .theorem} +::: {#thm-near-critical .exhibit .exhibit--theorem data-exhibit-type="theorem" data-exhibit-name="Theorem 6 (Near-critical annealed negativity)"} **Theorem 6** (Near-critical annealed negativity). *Fix $\eta\in(0,1)$, put $L_h=\log B_h$, and let $W_h\to\infty$. Uniformly for real $C$ satisfying* $$ @@ -321,7 +321,7 @@ $$ **Remark 7** (The coefficient $1/2$). The coefficient $1/2$ is not predicted to be sharp. It comes from the symmetric choice of the upper integration point in [Section 16](#sec-integration). The activity law and the diagnostics in [Section 17](#sec-numerics) are consistent with the normalized ratio approaching $1-O(e^{-W_h})$ deeper in the near-critical window. ::: -::: {#cor-fixed-fraction-slack .corollary} +::: {#cor-fixed-fraction-slack .exhibit .exhibit--corollary data-exhibit-type="corollary" data-exhibit-name="Corollary 8 (Fixed-fraction slack)"} **Corollary 8** (Fixed-fraction slack). *For every fixed $00\}$, and its positive support is the prefix $\{0,\ldots,k\}$. -::: {#lem-boundary-repulsion .lemma} +::: {#lem-boundary-repulsion .exhibit .exhibit--lemma data-exhibit-type="lemma" data-exhibit-name="Lemma 17 (Relative boundary repulsion)"} **Lemma 17** (Relative boundary repulsion). *Fix a finite activity $z>0$. A maximizer of $\mathcal F_{d,h}^{(z)}$ cannot lie on any proper local face within its effective horizon. More precisely, if its effective horizon is $k$, then* $$ @@ -714,7 +714,7 @@ which is strictly positive. If several faces are active simultaneously, all lead The horizon itself is also repelling. -::: {#lem-horizon-extension .lemma} +::: {#lem-horizon-extension .exhibit .exhibit--lemma data-exhibit-type="lemma" data-exhibit-name="Lemma 18 (Horizon extension)"} **Lemma 18** (Horizon extension). *A maximizer of $\mathcal F_{d,h}^{(z)}$ cannot have effective horizon $k0$, every maximizer of the exact grand-canonical functional $\mathcal F_{d,h}^{(z)}$ has full effective horizon $h$ and satisfies* $$ @@ -814,7 +814,7 @@ $$ Then $y_i=B_i(B_i+A_{i+1})$. -::: {#lem-edge-kkt .lemma} +::: {#lem-edge-kkt .exhibit .exhibit--lemma data-exhibit-type="lemma" data-exhibit-name="Lemma 20 (Edge KKT identifies the local tilt)"} **Lemma 20** (Edge KKT identifies the local tilt). *For every $1\le i\le h$,* $$ @@ -832,7 +832,7 @@ $$ Thus $\lambda_iy_i=\sqrt{\ell_{i-1}\ell_i}$, which is the displayed identity. The root contribution at $i=1$ has the same algebra, with the activity term cancelling through the root diagonal equation. □ ::: -::: {#prop-kkt-stationarity .proposition} +::: {#prop-kkt-stationarity .exhibit .exhibit--proposition data-exhibit-type="proposition" data-exhibit-name="Proposition 21 (Compact KKT equals two-message stationarity)"} **Proposition 21** (Compact KKT equals two-message stationarity). *Every strict interior KKT point determines positive messages satisfying the stationary system above. Conversely, every positive message solution reconstructs a strict interior KKT point by* $$ @@ -892,7 +892,7 @@ $$ At the terminal level, $u_h=0$, so $\rho_h=v_h=:s\in(0,1)$. Let $\mathcal D=\{(\rho,v):0<\rho\le v\le1\}$. -::: {#prop-reverse-map .proposition} +::: {#prop-reverse-map .exhibit .exhibit--proposition data-exhibit-type="proposition" data-exhibit-name="Proposition 22 (Exact reverse map)"} **Proposition 22** (Exact reverse map). *Given a next-level state $(\rho',v')\in\mathcal D$, define* $$ @@ -926,7 +926,7 @@ $$ Substituting $\rho=Ru$ and solving yields $u=1/(R+M)$, which gives the displayed formulas for $\rho,v$. Uniqueness follows from the algebraic solution. Since $e+w/v'\ge e+w=1$, we have $M\ge1$, and therefore $0<\rho0$, the exact grand-canonical stationarity equations have exactly one positive solution up to common message scaling.* ::: @@ -1004,7 +1004,7 @@ For a positive stationary message profile whose reconstructed selected density i Although $\mathcal F_{d,h}$ is nonconcave, the preceding interiority and uniqueness results determine its global optimizer. -::: {#thm-grand-canonical-globality .theorem} +::: {#thm-grand-canonical-globality .exhibit .exhibit--theorem data-exhibit-type="theorem" data-exhibit-name="Theorem 27 (Grand-canonical globality)"} **Theorem 27** (Grand-canonical globality). *For every $d\ge3$, $h\ge1$, and $z>0$, the exact grand-canonical functional $\mathcal F_{d,h}^{(z)}$ has a unique global maximizer. It is the positive stationary profile corresponding to the unique terminal parameter $s$ satisfying $z_h(s)=z$.* ::: @@ -1014,7 +1014,7 @@ Although $\mathcal F_{d,h}$ is nonconcave, the preceding interiority and uniquen The microcanonical problem follows by duality. -::: {#thm-microcanonical-globality .theorem} +::: {#thm-microcanonical-globality .exhibit .exhibit--theorem data-exhibit-type="theorem" data-exhibit-name="Theorem 28 (Microcanonical globality)"} **Theorem 28** (Microcanonical globality). *For every density $1/B_h<\alpha<1$, the exact microcanonical functional has a unique global maximizer, and it is the positive stationary profile on the reverse-transfer branch with selected density $\alpha$. Equivalently,* $$ @@ -1040,7 +1040,7 @@ Hence every interior density is attained. Fix $\alpha$ and choose $\theta$ with $\alpha(\theta)=\alpha$. For every profile $P$ of density $\alpha$, $\mathcal F(P)+\theta\alpha\le \mathcal F(P_\theta)+\theta\alpha$, so $P_\theta$ is the microcanonical maximizer. Uniqueness follows from grand-canonical uniqueness. □ ::: -::: {#cor-envelope-identity .corollary} +::: {#cor-envelope-identity .exhibit .exhibit--corollary data-exhibit-type="corollary" data-exhibit-name="Corollary 29 (Concavity and the envelope identity)"} **Corollary 29** (Concavity and the envelope identity). *The exact value function $\Psi_{d,h}$ is strictly concave on $(1/B_h,1)$ and differentiable there. If $z(\alpha)$ is the activity exposing density $\alpha$, then* $$ @@ -1074,7 +1074,7 @@ $$ The stationary vertex and edge normalizers satisfy the following exact telescoping identity. -::: {#prop-telescoping .proposition} +::: {#prop-telescoping .exhibit .exhibit--proposition data-exhibit-type="proposition" data-exhibit-name="Proposition 30 (Corrected telescoping identity)"} **Proposition 30** (Corrected telescoping identity). *Every positive stationary solution satisfies* $$ @@ -1126,7 +1126,7 @@ $$ E_{\mathrm{root}}:=\log\kappa-b\log u,\qquad N_e:=\log Z_e-(D+1)\log u. $$ -::: {#prop-defect-identity .proposition} +::: {#prop-defect-identity .exhibit .exhibit--proposition data-exhibit-type="proposition" data-exhibit-name="Proposition 31 (Exact entropy-defect identity)"} **Proposition 31** (Exact entropy-defect identity). *Every positive stationary solution of selected density $\alpha$ satisfies* $$ @@ -1172,7 +1172,7 @@ $$ The window theorem requires terminal parameters linear in $h$ and uniformly below the critical coefficient, tracked with quantitative — not merely qualitative — error terms. -::: {#def-subcritical-window .definition} +::: {#def-subcritical-window .exhibit .exhibit--definition data-exhibit-type="definition" data-exhibit-name="Definition 32 (Subcritical terminal window)"} **Definition 32** (Subcritical terminal window). Fix constants $0<\tau_-\le\tau_+<\dfrac{4\log b}{3D}$. A sequence of terminal parameters is in the *subcritical linear window* if $$ @@ -1186,7 +1186,7 @@ All constants below may depend on $d,\tau_-,\tau_+$ but not on $h$ or $t$ in thi At one reverse step, keep $\rho'$ fixed and write $q'=1-v'$. The free predecessor with the same $\rho'$ has $w=(1-\rho')^{1/b}$, $\rho_0=1-w$, $u_0=w$, $q_0=0$. -::: {#lem-free-side-step .lemma} +::: {#lem-free-side-step .exhibit .exhibit--lemma data-exhibit-type="lemma" data-exhibit-name="Lemma 33 (Uniform free-side step)"} **Lemma 33** (Uniform free-side step). *There are $\delta_0,C>0$ such that, whenever $0\le q'\le\delta_0$, the actual predecessor satisfies* $$ @@ -1216,7 +1216,7 @@ Now $1-\rho=u+q=M/(R+M)$, $q=(M-1)/(R+M)$. Substitution gives the displayed expa For a reverse step near the stable manifold, write the next state as $v'=V$, $\rho'=V\delta$, $u'=V(1-\delta)$. The stable predecessor with the same $V$ is $(\rho_0,u_0)=(0,V^b)$. -::: {#lem-stable-side-step .lemma} +::: {#lem-stable-side-step .exhibit .exhibit--lemma data-exhibit-type="lemma" data-exhibit-name="Lemma 34 (Uniform stable-side step)"} **Lemma 34** (Uniform stable-side step). *There are $\delta_1,C>0$ such that, whenever $0\le\delta\le\delta_1$, the predecessor satisfies* $$ @@ -1266,7 +1266,7 @@ $$ It satisfies $\bar q_h=e^{-t}$ and $\bar q_i=b\bar u_i^2\bar q_{i+1}$. -::: {#prop-free-shadow .proposition} +::: {#prop-free-shadow .exhibit .exhibit--proposition data-exhibit-type="proposition" data-exhibit-name="Proposition 35 (Uniform free shadow)"} **Proposition 35** (Uniform free shadow). *Uniformly in every subcritical terminal window, for every $m\le i\le h$,* $$ @@ -1335,7 +1335,7 @@ $$ x=-\log u_1,\qquad \widetilde a=\widetilde a_1=\frac{\rho_1}{b u_1^D}. $$ -::: {#prop-stable-shadow .proposition} +::: {#prop-stable-shadow .exhibit .exhibit--proposition data-exhibit-type="proposition" data-exhibit-name="Proposition 36 (Uniform stable shadow)"} **Proposition 36** (Uniform stable shadow). *Under the terminal-window assumptions and the matching choice of $H,m$ from [Section 12](#sec-free-shadow), put* $$ @@ -1403,7 +1403,7 @@ which proves the last assertion. □ The free and stable shadows now match without an additional hypothesis. -::: {#thm-terminal-matching .theorem} +::: {#thm-terminal-matching .exhibit .exhibit--theorem data-exhibit-type="theorem" data-exhibit-name="Theorem 37 (Terminal-window matching)"} **Theorem 37** (Terminal-window matching). *Let $L=\log B_h$ and $c_t=Dt/L$. Uniformly for $t$ in every subcritical linear window,* $$ @@ -1442,7 +1442,7 @@ The exponent difference is $O(tb^{-H})=B_h^{-3c_t/4+o(1)}$, which is smaller tha To pass from $\widetilde a$ to the actual selected density, reconstruct the edge partition function $Z_e=\sum_{i=0}^hB_i^2+2\sum_{i=0}^{h-1}B_iA_{i+1}$. -::: {#lem-edge-normalizer .lemma} +::: {#lem-edge-normalizer .exhibit .exhibit--lemma data-exhibit-type="lemma" data-exhibit-name="Lemma 38 (Edge-normalizer localization)"} **Lemma 38** (Edge-normalizer localization). *With the matching index $m$,* $$ @@ -1489,7 +1489,7 @@ $$ B_0=v_1\frac{1-(1-\rho_1)^{1/b}}{(1-\rho_1)^{1/b}},\qquad \kappa=\left(\frac{v_1}{(1-\rho_1)^{1/b}}\right)^b,\qquad z=\frac{B_0\kappa}{(1+B_0)^b}. $$ -::: {#thm-activity-law .theorem} +::: {#thm-activity-law .exhibit .exhibit--theorem data-exhibit-type="theorem" data-exhibit-name="Theorem 39 (Quantitative uniform activity law)"} **Theorem 39** (Quantitative uniform activity law). *Fix constants $0