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Remove the integer requirements from the previous formulation to obtain its linear programming relaxation. Give each edge constraint a nonnegative dual variable $y_e$, and each bound for $v\in Z$ a nonnegative dual variable $z_v$. Since all right-hand sides are one, the dual linear program isAn integer feasible point selects a multiset of edges touching every nonisolated vertex, together with at least one singleton for each isolated vertex. Repeated edges or singleton choices can be discarded without losing coverage, so an optimum has a genuine edge cover of the nonisolated part and one singleton per isolated vertex. If $G$ has no isolated vertices, this is exactly an edge cover, with the objective counting selected edges including any unnecessary multiplicities. The isolated-singleton extension is necessary because a graph with an isolated vertex admits no ordinary edge cover.

This interpretation concerns integer feasible dual solutions. The linear programming relaxation can have fractional optima, so its value need not equal the integer independent set optimum. For example, the triangle has relaxed independent-set value $3/2$, whereas its maximum independent set has size $1$ and its minimum edge cover has size $2$.

Worked solutions to all three questions of the Oxford examination held on 25 May 2023. Source release: released examination archive, member 2022/T/a18330w1.pdf; the cover identifies the examination as Trinity Term 2023. Numbering follows the source PDF, pages 2–4. The examination asks candidates to choose two questions; all three are solved here.

We use finite simple graphs throughout the graph arguments. In Question 1(c), $V$ must be nonempty for the stated minimum to exist. In Question 3(d), an edge cover exists only when there is no isolated vertex; for a nonempty connected graph this excludes the one-vertex graph. These minor domain qualifications are made explicit in the affected solutions.

Use minimum-weight edge cover by matching savings. Reject inputs with an isolated vertex. For every vertex $v$, choose a cheapest incident edge $e_v$ and write $c_v=w(e_v)$. Define a baseline and a saving for each edge $uv$ byCompute a maximum-weight matching $M$ using $p$ as its weights, allowing the empty matching in a graph and imposing no maximum-cardinality condition. Negative savings may be ignored. ReturnThis is an edge cover. Since original weights are nonnegative, removing duplicate appearances of an edge cannot increase the total, and henceTo prove optimality, take a minimum-weight edge cover and remove redundant edges until it is inclusion-minimal. Nonnegative weights ensure that its weight does not increase, so it remains optimal. As proved in (i), its components are star graphs. Choose one edge from each star graph, forming a matching in a graph $N$.

In a star graph with center $a$, selected leaf $b$, and other leaves $L$, this choice contributes $w(ab)+{\sum }_{v\in L}{c}_v$ to $B-p(N)$. Each other leaf $v$ has its edge $av$ available, so $c_v\le w(av)$. Thus this contribution is at most the weight of that star graph. Summing over components yieldsSince $M$ maximizes saving, $p(M)\ge p(N)$, and consequentlyAll inequalities are equalities. Therefore $F_M$ is a minimum-weight edge cover. This proof also handles zero weights and duplicate cheapest-edge choices.

Finding all $c_v$ and constructing $F_M$ takes $O(|V|+|E|)$ time. A general-graph maximum-weight matching polynomial-time algorithm supplies the only substantial step; the documented routine runs in $O(|V{|}^3)$ arithmetic time. Exact rational or integer weights avoid floating-point comparisons affecting optimality.^[ref]

First test for an isolated vertex, reporting infeasibility if one exists. Otherwise compute a maximum cardinality matching $M$ using a general-graph polynomial-time algorithm, then add one arbitrary incident edge for each unmatched vertex in a matching. The proof in (i) shows that the resulting edge cover has minimum cardinality $|V|-|M|$.

The final scan takes $O(|V|+|E|)$ time with adjacency lists; the maximum cardinality matching computation is polynomial. One may obtain it from a general-graph maximum-weight matching routine with every weight equal to one.^[ref]

Assume $G$ has no isolated vertex; otherwise an edge cover does not exist. In particular, a one-vertex connected graph is an excluded degenerate case of the statement.

Let $M$ be a maximum cardinality matching, of size $m(G)$, and let $U$ be its unmatched vertices in a matching. There is no edge joining two vertices of $U$, since adding it would enlarge $M$. Choose one incident edge for each $u\in U$. These chosen edges are distinct and outside $M$, so together they form an edge cover of sizeConsequently $e(G)\le |V|-m(G)$.

For the opposite bound, let $F$ be a minimum-cardinality edge cover. It is inclusion-minimal. Every selected edge has an endpoint of vertex degree one in $(V,F)$: otherwise deleting that edge would leave both endpoints covered. Each connected component of a graph of $(V,F)$ is therefore a star graph. To see this, a component can have at most one vertex of vertex degree greater than one; a shortest graph path between two such vertices would contain an edge whose endpoints both have vertex degree greater than one. A component with no such vertex is a single edge, also a star graph.

If there are $t$ such star graphs, each has one fewer edge than vertices, so $|F|=|V|-t$. Select one edge from each component to get a matching in a graph of size $t$. Hence $m(G)\ge t=|V|-e(G)$, proving the edge cover and matching identity

The joint law is the Edwards-Sokal coupling. For a fixed spin configuration, the sum over each edge state contributes $(1-p)+p{\delta }_e(\sigma )$. Since ${\delta }_e$ is either zero or one, setting $\beta =-\log (1-p)$ givesConsequently the spin marginal distribution is the Potts measureThis follows from a finite sum that factorizes over edges, without any independence assumption on the spin marginal.

For a fixed edge configuration, the joint weight vanishes unless the spins agree across every open edge, equivalently unless they are constant on each open percolation cluster. Each of the $k(\omega )$ clusters, including isolated vertices, may independently be assigned any of $q$ colors. Thus exactly $q^{k(\omega )}$ spin configurations have nonzero weight, and their common weight is the edge-product factor divided by $Z$. The edge marginal distribution is therefore the random-cluster measureIn this convention $Z^{\prime \prime }=Z$. Dividing the joint mass by this positive marginal gives the conditional distributionEquivalently the clusters receive independent uniformly chosen colors. Conversely, conditionally on the spins, edges are independent: an unequal-spin edge must be closed, while an equal-spin edge is open with probability $p$. These two conditional descriptions explain the Edwards-Sokal coupling directly.

If $x$ and $y$ lie in the same open cluster, their conditional spin-equality probability is one. If they lie in different clusters, their two independent uniform colors agree with probability $1/q$. Averaging over the random-cluster measure yieldsand hence the spin-connectivity identity for the Potts modelThe identity also includes $x=y$, when connectivity and spin equality are both certain.

Couple the uniform random graph process by giving every possible edge an independent uniform label in $[0,1]$ and revealing them in increasing order. The graph at label threshold $p$ is a binomial random graph. Define ${\tau }_0$ as the first step with no isolated vertices and ${\tau }_c$ as the first connected graph. Deterministically ${\tau }_c\ge {\tau }_0$. We prove the reverse inequality with high probability by tracking the same process through a short interval.

Put $w=\frac{1}{4}\log \log n$, $p_{\pm }=(\log n\pm w)/n$, and $L=n{p}_{-}$. At $p_{-}$ the isolated vertex count $Z$ hasThe variance estimate follows by comparing two isolated-vertex indicators, whose joint-to-product probability ratio is $(1-p_{-}{)}^{-1}$. Thus $Z>0$ with high probability. Also the Markov inequality gives $Z\le \log n$ with probability tending to one.

At this same threshold, exclude every graph component with order $2\le s\le n/2$. For $2\le s\le n/\log n$, any such component contains a spanning tree. The Cayley formula and a union bound therefore giveIn this range the bracket is at most $A=e^{2}L{e}^w/n=o(1)$. Summing over $s\ge 2$ gives $O(n{A}^2/L)=O(L{e}^{2w}/n)=o(1)$. For $n/\log n<s\le n/2$, even the cut-absence bound is enough:The bracket tends to zero, so this range also has total probability $o(1)$. Consequently the graph at $p_{-}$ consists of one component of order greater than $n/2$ and its isolated-vertex set $S$, where $1\le |S|\le \log n$ with high probability.

Conditional on the entire graph at $p_{-}$, every unrevealed edge label remains independently uniform on $(p_{-},1)$. In particular,There are no isolated vertices at $p_{+}$ with high probability, because their expected value is asymptotic to $e^{-w}\to 0$. On the intersection of these events, each vertex of $S$ loses isolation by joining the original large component, never by forming a separate small component with another vertex of $S$. At every step between these two thresholds there is therefore one nontrivial component and some isolated vertices. The final isolated vertex disappears precisely when the graph becomes connected. This proves the connectivity hitting time equals disappearance of isolated vertices result:

For the critical-window probability, let $p=(\log n+\alpha (n))/n$ with $|\alpha (n)|\le B$. For each fixed $j$, the falling factorial moment of the isolated-vertex count isuniformly for such shifts. Apply the Bonferroni inequalities to the zero-count event. Its odd and even truncations bracket $\mathrm{Pr}(Z=0)$ by partial sums of ${\sum }_{j\ge 0}(-1{)}^j{e}^{-j\alpha (n)}/j!$. Since $e^{-\alpha (n)}\le e^B$, their remainders tend uniformly to zero. Hence $\mathrm{Pr}(Z=0)=e^{-e^{-\alpha (n)}}+o(1)$. The process equality just proved changes this probability by at most its $o(1)$ failure probability. ThereforeThe ratio follows because the denominator is bounded below by $e^{-e^B}>0$; no convergence of $\alpha (n)$ is needed.

The printed second conclusion has the complementary denominator and is false as written. For $\alpha (n)=0$, the printed ratio tends to $e^{-1}/(1-e^{-1})=1/(e-1)\ne 1$. Its denominator $1-e^{-e^{-\alpha (n)}}$ is correct for the probability of being disconnected. Thus either remove the leading $1-$ in the denominator or replace “connected” by “disconnected”; the proof supplies both corrected versions.

Let $Y$ be the number of isolated vertices and set $\lambda =e^{-c}$. For fixed $k\ge 1$, the falling factorial $(Y{)}_k$ counts ordered $k$-tuples of distinct isolated vertices. A specified tuple is isolated exactly when all edges incident to its vertices are absent. There are $k(n-k)+\left(\genfrac{}{}{0ex}{}{k}{2}\right)$ such edges, soSince $np=\log n+c$ and $n{p}^2=o(1)$, taking logarithms givesThus every fixed factorial moment tends to ${\lambda }^k$. We use the standard factorial-moment criterion for Poisson convergence: nonnegative integer-valued random variables whose factorial moments converge, for every fixed order, to ${\lambda }^k$ converge in distribution to $\mathrm{Poisson}(\lambda )$. Consequently $Y$ has that limiting distribution, and its mass at each fixed integer converges. In particular,This establishes the limiting probability of at least one isolated vertex, rather than merely a bound based on its expectation.

A connected graph with $n>1$ has no isolated vertices. Conversely, a disconnected graph with no isolated vertices has a connected component with size between $2$ and $n/2$. We must show that this latter event has probability tending to zero.

The permitted component-size estimate excludes sizes between $\log \log n$ and $n/2$ with high probability. For the remaining small sizes, let $C_k$ count components with $k$ vertices. A connected graph on a fixed $k$-vertex set contains a spanning tree. By the Cayley formula, there are $k^{k-2}$ labelled trees, as follows from the Prüfer code bijection between labelled trees and sequences of $k-2$ labels. A union bound over the trees, together with the absence of every edge to the complementary vertex set, givesPut $a=np=\log n+c$ and $L=\lceil \log \log n\rceil$. Using $\left(\genfrac{}{}{0ex}{}{n}{k}\right)\le (en/k{)}^k$ and $1-p\le e^{-p}$, uniformly for $2\le k\le L$ we obtainHere $p{L}^2\to 0$. Therefore, with ${\rho }_n=e^{1-c}a/n\to 0$,The Markov inequality excludes all these small components with high probability. Together with the permitted estimate, this proves that the difference between the probability of connectivity and the probability of no isolated vertices tends to zero. Hence

For the unheaded degree-one continuation, write $X={\sum }_{v=1}^n{I}_v$, where $I_v$ indicates that vertex $v$ has degree one. Withwe haveFor distinct vertices $u,v$, separate the cases where their connecting edge is present or absent. If it is present, all their other incident edges must be absent. If it is absent, each vertex chooses its unique neighbour among the other $n-2$ vertices; those two neighbours may coincide. The respective edge constraints are independent even in that case. ThusDividing by $q^2$ gives the exact ratioThe first term tends to zero because $(n-1{)}^{2}p$ has order $n\log n$; the second tends to one. Consequently its variance satisfiesThe Chebyshev inequality now yields $\mathbb{P}(X=0)\le \mathrm{Var}X/(\mathbb{E}X{)}^2\to 0$. A vertex of degree one exists with high probability, even though the limiting probability of having an isolated vertex is strictly between zero and one.

It suffices to prove the assertion after replacing $\omega$ by $\min \{\omega ,\sqrt{\log n}\}$: that replacement gives a tighter interval. Thus assume $\omega \to \infty$ and $\omega =o(\log n)$, and putAt $p_{-}$, let $J$ count isolated vertices in $L$. The same indicator random variable calculation as for all isolated vertices givesBy the Chebyshev inequality, $J>0$ with high probability. Since $\ell \ge 2$ eventually, the marked vertices are then not all connected.

At $p_{+}$, the logarithmic-regime giant component result applies with $k=2$ and any fixed ${\gamma }_0\in (1/3,1/2)$. Its unique giant component misses $(1+o(1))n^{1/2}{e}^{-\omega /4}$ vertices. On the event that it misses at most twice that number, exchangeability makes the missed vertex set uniform conditional on its size. A union bound therefore givesThus all of $L$ is connected at $p_{+}$ with high probability.

For the uniform random graph process, assign uniform labels to the edges of the complete graph as independent random variables and reveal them in label order. The binomial random graph $G(n,p)$ is the prefix containing the $M(p)$ labels at most $p$, where $M(p)$ has a binomial distribution with parameters $N=\left(\genfrac{}{}{0ex}{}{n}{2}\right)$ and $p$. Its variance at $p_{\pm }$ is $O(n\log n)$, whereas the gap between $\mathbb{E}M(p_{\pm })$ and the relevant endpoint $m_{\pm }$ is $(1+o(1))\omega n/8$. The Chebyshev inequality gives $M(p_{-})\ge m_{-}$ and $M(p_{+})\le m_{+}$ with high probability, with harmless integer rounding. The monotone graph property that all marked vertices are connected now impliesThe marked-set connectivity threshold is lower than the threshold for connecting every vertex, because only about $\sqrt{n}$ specified vertices need to avoid the small graph components.

Put $d=np=\gamma \log n$ and $\mu =n^{1-\gamma }$. The hypothesis implies $\mu \ge e^{\omega (n)}\to \infty$. For the number $I$ of isolated vertices, the isolated vertices in the Erdős-Rényi model formulas giveThus the Chebyshev inequality gives $I=(1+o(1))\mu$ with high probability, uniformly over the specified $\gamma$ range.

The Cayley formula supplies a spanning tree on any connected vertex set. Consequently the expected value $C_j$ of the number of graph components of order $j\ge 2$ is bounded byChoose a fixed $\eta \in (0,1/2)$ with ${\gamma }_0(1-\eta )(k+1)>1$. For $k+1\le j\le \eta n$, with $j\ge 2$, the binomial coefficient bound $\left(\genfrac{}{}{0ex}{}{n}{j}\right)\le (en/j{)}^j$ givesHere $ed{e}^{-d(1-\eta )}\le e\log n{n}^{-{\gamma }_0(1-\eta )}\to 0$. The geometric series and a union bound therefore exclude this entire size range, since $n[e\log n{n}^{-{\gamma }_0(1-\eta )}{]}^{k+1}=o(1)$. For $\eta n\le j\le n/2$, an empty graph cut would be necessary. Its total probability is at mostThere are consequently no graph components of orders between $k+1$ and $n/2$, with high probability.

For each fixed $2\le j\le k$, a graph component containing a graph cycle has a spanning tree and at least one extra edge. Counting that extra edge gives an expected value $O_k(d^j{e}^{-dj})=o(1)$; hence every small graph component is a tree component. The total number $R_2$ of vertices in tree components of orders $2,\dots ,k$ satisfiesThe Markov inequality gives $R_2=o(\mu )$ with high probability. When $k=1$, this sum is empty and $R_2=0$. Since $\mu =o(n)$, the remaining vertices must lie in one graph component larger than $n/2$. It is unique, and its order is $n-(1+o(1))n^{1-\gamma }$. Every other graph component is a tree with at most $k$ vertices. This is the logarithmic-regime giant component mechanism: almost all missing vertices are isolated.

The empty family satisfies the bound, so assume $\mathcal{F}$ is nonempty and choose a graph according to the uniform distribution on a finite set $F\in \mathcal{F}$. Let $Y$ be its random vector of edge indicators, indexed by $E=\left(\genfrac{}{}{0ex}{}{[n]}{2}\right)$. Then its information entropy is $H(Y)={\log }_2|\mathcal{F}|$.

For each vertex $v$, put $S_v=\{e\in E:v\in e\}$, so $Y_{S_v}$ records the graph neighbourhood of $v$. The possible graph neighbourhoods form an intersecting family of subsets of $[n]\setminus \{v\}$: the graph intersection of any two members of $\mathcal{F}$ has no isolated vertex at $v$. A subset and its complement cannot both occur. Pairing the $2^{n-1}$ subsets into complementary pairs gives at most $2^{n-2}$ possible graph neighbourhoods. The maximum entropy on a finite alphabet therefore givesThis argument applies for $n\ge 2$. For $n=1$ the assumed nonempty family cannot exist, since its only possible graph has an isolated vertex.

Each edge belongs to exactly two of the sets $S_v$. To obtain the needed instance of Shearer inequality directly from the preceding compression argument, repeatedly apply a union-intersection compression to incomparable members of the multiset $(S_v{)}_{v=1}^n$. Each such step strictly increases ${\sum }_S|S{|}^2$, byThe potential is bounded and integer-valued, so the process terminates in a chain under inclusion. Coordinate multiplicities stay equal to two, so, for $E\ne \varnothing$, the terminal chain consists of two copies of $E$ and $n-2$ empty sets. The compression of an entropy sum now givesExponentiating proves the entropy bound for graphs with isolated-vertex-free intersections:

In the Erdős-Rényi model $G(n,p)$, the vertex set is $[n]$ and every one of the $\left(\genfrac{}{}{0ex}{}{n}{2}\right)$ possible edges is present independently with probability $p$.

Let $X$ count isolated vertices. If $p\ge (1+\epsilon )\log n/n$, the union bound givesThus $\mathbb{P}(E_n)\to 1$.

Chebyshev inequality states thatNow suppose $p\le (1-\epsilon )\log n/n$. Monotonicity lets us use the largest allowed $p$. Withthe supplied lower exponential estimate showsFor distinct vertices $i,j$,HenceandChebyshev with $t=\mathbb{E}X$ gives $\mathbb{P}(X=0)\to 0$, so $\mathbb{P}(E_n)\to 0$.

For connectivity, the lower-threshold result is immediate because a connected graph has no isolated vertex:For the upper threshold, if a graph is disconnected it has a component with vertex set $S$ of some size $1\le k\le n/2$. In particular, every one of the $k(n-k)$ edges from $S$ to its complement is absent. ThereforeChooseFor $1\le k\le \eta n$, the standard bound $\left(\genfrac{}{}{0ex}{}{n}{k}\right)\le (en/k{)}^k$ givesThe sum of these terms tends to zero. For $\eta n\le k\le n/2$, use $\left(\genfrac{}{}{0ex}{}{n}{k}\right)\le 2^n$ and $k(n-k)\ge \eta n^2/2$:Thus $\mathbb{P}(F_n)\to 1$ above the threshold. Together,This is the connectivity threshold in the Erdős-Rényi model.

A path-cycle matching in a finite simple graph is a collection of mutually vertex-disjoint paths and cycles in a graph. Its used edges form a subgraph with every degree of a vertex at most two. Conversely, every finite connected component of a graph with maximum degree at most two is a graph path, a cycle in a graph, or an isolated vertex; following successive edges proves this classification. Discarding unused isolated vertices therefore converts any such subgraph into a path-cycle matching without changing its edge count. On a bipartite graph, maximizing the used edges is exactly bipartite b-matching with every $b_v=2$.

A perfect matching is a matching in a graph that covers every vertex exactly once. Its edges have no shared endpoints, and every vertex is an endpoint of one selected edge. It is useful when a collection must be divided into disjoint pairs with no element left over.

In a bipartite graph with equally many vertices on its two sides, a perfect matching can assign every person one allowed job and every job one person. The example under permanent of a matrix has three people and three jobs, with exactly three valid assignments. The blue edges below show those three perfect matchings; a crossing in the drawing is not an extra vertex.

Every selected edge covers two vertices, so a graph with an odd integer number of vertices has no perfect matching. An even integer number is necessary but does not guarantee existence: an isolated vertex cannot be covered. A perfect matching also need not be unique. For example, the complete bipartite graph $K_{2,2}$ has two perfect matchings, corresponding to the two ways of assigning two people to two jobs.

Counting all perfect matchings is a different question from finding one. For a zero-one biadjacency matrix of a balanced bipartite graph, the count is its permanent of a matrix. The nonzero determinant criterion for a perfect matching supplies an existence test, but cancellation means that a zero determinant does not rule one out.

A forest whose connected components of a graph are star graphs. In an inclusion-minimal edge cover, every edge has at least one endpoint of vertex degree one, forcing this structure with no isolated vertex.

For $X_j$ the number of tree components of order $j$ in a binomial random graph, choose their vertices, choose one of $j^{j-2}$ labelled trees by the Cayley formula, require its $j-1$ edges, and exclude both the remaining internal edges and all crossing edges. For $j=1$ interpret $j^{j-2}=1$, recovering the isolated vertex count. If $p=\lambda /n$ for fixed positive $\lambda$ and $j=O(\log n)$, the Stirling formula gives $\mathbb{E}{X}_j\sim n(\lambda e^{1-\lambda }{)}^j/(\lambda \sqrt{2\pi }{j}^{5/2})$. Distinct overlapping vertex sets cannot both be graph components; for disjoint sets their joint occurrence gains the factor $(1-p{)}^{-j^2}$ relative to the product, because their between-set edges must be absent only once.

A vertex cover chooses vertices that collectively touch every edge of a graph. Think of placing monitors at junctions: if a monitor watches every connection incident to its junction, a vertex cover watches all connections. The minimum vertex cover asks how few monitors suffice. This is a model of covering connections through their endpoints, rather than of monitoring distances in the drawing.

For a finite simple graph $G=(V,E)$, a vertex cover is a subset $C\subseteq V$ satisfyingBoth endpoints may be selected; the condition requires at least one. An isolated vertex covers no edge, so a minimum vertex cover never needs it.^[ref]

Take the cycle graph $C_5$ with vertices $0,1,2,3,4$ and edges $01,12,23,34,40$. The set $C=\{1,3,4\}$ is a vertex cover: vertex $1$ covers $01$ and $12$, vertex $3$ covers $23$ and $34$, and vertex $4$ covers $40$ as well as $34$. In particular, the closing edge $40$ is covered even though vertex $0$ is unselected.

The unselected vertices $I=V\setminus C=\{0,2\}$ form an independent set. This is the key connection: an edge missed by $C$ would have both endpoints in $I$, precisely what an independent set forbids. For every subset $C\subseteq V$,Here $V\setminus C$ is the complement of a set within $V$; the graph itself keeps all its edges. The complement graph is a different construction.^[ref]

The endpoints of any maximal matching form a vertex cover: an edge avoiding all endpoints would enlarge that matching in a graph. Consequently a graph with no matching in a graph of size $s$ has a vertex cover of size at most $2(s-1)$. This supplies a convenient vertex cover, but it need not be a minimum vertex cover.