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The canonical projection of a quotient set is the function $q(x)=[x]$. It is surjective: every set element of $X/\sim$ is $[x]$ for some $x\in X$. Two set elements have the same image exactly when they are related by the equivalence relation. Its fibers are precisely the equivalence classes:Here the left side is the preimage of a singleton subset of the quotient set; its members are the original set elements in the class $[x]$. In the diagram, the three arrows arriving at $[1]$ start at $1$, $4$ and $7$.

In a finite, time-homogeneous Discrete-time Markov chain with row-stochastic transition matrix $P$, write $i\to j$ when $j$ is accessible from $i$, meaning $(P^n{)}_{ij}>0$ for some $n\ge 0$. States communicate, written $i\leftrightarrow j$, when both $i\to j$ and $j\to i$. Communication is an equivalence relation, and a communicating class is an entire equivalence classThus communicating classes form a partition of the state space; a smaller mutually communicating subset is not itself a class. See Maria Cameron's Markov Chains notes, section 1.2.

The allowance of zero steps matters: $P^0=I$, the identity matrix, makes $i\to i$ true even for a state with no possible positive-time return. Symmetry is built into communication. For transitivity, concatenate a positive-probability path from $i$ to $j$ with one from $j$ to $k$, and do the same in reverse. These observations prove the equivalence relation directly. One-way accessibility alone is not symmetric and does not define the classes; see Accessible state of a Markov chain.

A closed communicating class satisfies $P_{ij}=0$ for every $i\in C$ and $j\notin C$. Once entered, it cannot be left. A singleton class is an absorbing state exactly when it is closed. An open communicating class has an outgoing transition; after a path leaves it, it cannot return, since that would make the visited outside states communicate with it. The internal motion of a closed class can nevertheless continue forever.

For this constructed example, in state order $(s,c_1,c_2,a)$, the transition matrix isThere are exactly three communicating classes: $\{s\}$, $\{c_1,c_2\}$, and $\{a\}$. Only the first is open. The second is closed but neither of its states is an absorbing state; they alternate deterministically. Although $s\to c_2$ via $c_1$, the reverse accessibility fails. A direct matrix power check gives $(P^2{)}_{s,c_2}=1/4$ and $(P^2{)}_{c_2,s}=0$.

Starting at $s$, let $\tau$ be the first hitting time of either closed class. Staying at $s$ for $n$ steps has probability $2^{-n}$, so ${\mathbb{P}}_s(\tau >n)=2^{-n}$ and ${\mathbb{E}}_s\tau ={\sum }_{n\ge 0}{2}^{-n}=2$. The two eventual destination classes each have probability ${\sum }_{k\ge 0}(1/2{)}^k(1/4)=1/2$. Hence the hitting probability of the absorbing state $a$ is only $1/2$, and its unconditional expected hitting time from $s$ is infinite: on half the paths it is never hit. In particular, entering a closed class with certainty does not imply entering an absorbing state with certainty.

Classifying the state space separates two questions: which closed region will eventually be entered, and how does the Markov chain move inside that region? In this example the restriction to $\{c_1,c_2\}$ is an irreducible Markov chain with period two; the restriction to $\{a\}$ is a one-state irreducible Markov chain with period one. The whole Markov chain is reducible. The absorbing state example illustrates a different possibility: every closed class is a singleton, so eventual class selection is also terminal outcome selection.

A connected component of a topological space $X$ is a nonempty connected subset that is maximal under subset inclusion. If $C$ is a connected component and $C\subseteq D\subseteq X$ with $D$ a connected subset, then $D=C$. Maximality means that no larger connected subset contains $C$; it does not mean largest cardinality among all connected subsets.

For example, the connected components of $[-2,2]\setminus \{0\}$ in the usual subspace topology of the real line are $[-2,0)$ and $(0,2]$. Each is a real interval, and no connected subset of the real line can meet both without containing the missing point $0$. For the finite set $\{-2,0,2\}$ in the same subspace topology, the three connected components are its singleton sets.

Every point $x\in X$ lies in exactly one connected component, constructed by taking the unionAll members contain $x$, so their union is connected by connected union with a connected hub. The union includes every connected subset through $x$, so it is maximal. Equivalently, defineThis is an equivalence relation: singleton sets give reflexivity, symmetry is immediate, and two connected subsets sharing the middle point have a connected union, giving transitivity. The equivalence classes are precisely the connected components. They form a partition of a set: they cover $X$ and any two are either equal or disjoint.^[ref]

Every nonempty connected subset lies within one connected component. Moreover, every connected component is closed in $X$: closure of a connected set makes its closure connected, and maximality forces that closure to equal the original connected component. A connected component need not be open. For example, in $S=\{0\}\cup \{1/n:n=1,2,\dots \}$ with its usual subspace topology from the real line, all connected components are singleton sets, but $\{0\}$ is not open: every neighbourhood of $0$ in $S$ contains some $1/n$.^[ref]

In the comparison model exposing only an equality test between otherwise opaque values, partitioning $n$ values into equivalence classes takes $\Omega (n^2)$ tests in the worst case. Run the algorithm on pairwise distinct values. If any pair remains untested, changing just those two values to equal is consistent with every answer observed so far but changes the required output partition. Every pair must therefore be tested. With a compatible total order, comparison sort can instead group equal values in $O(n\log n)$ time.

An equivalence class collects all the set elements indistinguishable from a specified set element under an equivalence relation. For an equivalence relation $R$ on a set $X$, the precise definition isThe ambient set matters: a finite class for integer congruence restricted to $\{0,\dots ,8\}$ differs from the infinite residue class for the same integer congruence on all integers. We write $[x]$ when the equivalence relation and ambient set are clear.

In the finite modulo-three example,There are nine names $[x]$ but only three distinct equivalence classes. In particular, $3\in [0]$, whereas $[3]=[0]$: the first statement is membership of a set element; the second is equality of two sets.

For any equivalence relation on $X$, the essential rule isReflexivity puts $x$ in $[x{]}_R$, so the equivalence classes are nonempty and cover $X$. If two equivalence classes share $z$, the symmetric relation property and transitivity relate their defining elements through $z$; transitivity then makes their members coincide. Thus distinct equivalence classes are disjoint, giving a partition of a set. Conversely, declare two set elements equivalent when they belong to the same block of a partition of a set. The three relation properties follow immediately and its equivalence classes recover the blocks. ^[ref]

An equivalence class representative is any set element belonging to that equivalence class. In the finite modulo-three example, either $0$, $3$ or $6$ represents $\{0,3,6\}$. Changing the equivalence class representative changes its name but preserves the equivalence class itself. Choosing $0,1,2$ gives one representative of an equivalence class from each class; the quotient set consists of the three whole sets.

An equivalence relation says which set elements should count as the same for a particular purpose. In modular arithmetic, for example, distinct integers with the same remainder are equivalent. The relation gathers interchangeable set elements into equivalence classes; the quotient set then treats each whole equivalence class as one object.

Formally, an equivalence relation on a set $X$ is a binary relation $R\subseteq X\times X$, written $x\sim y$, satisfying three conditions for all $x,y,z\in X$:These conditions make the equivalence classes a partition of a set: each set element belongs to exactly one class. ^[ref]

For a worked example, take the finite set $X=\{0,1,\dots ,8\}$ and restrict integer congruence modulo three to $X$:Reflexivity follows because $x-x=0$. The symmetric relation property follows by changing the sign of a difference, and transitivity follows by adding $x-y$ and $y-z$: divisibility by three is preserved in both steps. Thus $0\sim 3$ and $3\sim 6$ force $0\sim 6$. The three equivalence classes are $\{0,3,6\}$, $\{1,4,7\}$ and $\{2,5,8\}$. Their members alternate along the number line; membership in an equivalence class need not mean being close together.

The transitivity requirement rules out a tempting alternative. On $\{0,1,2\}$, define nearness by the absolute value condition $|x-y|\le 1$. This binary relation is reflexive and symmetric, but $0$ is near $1$ and $1$ is near $2$ while $0$ is not near $2$. Its neighborhoods $\{0,1\}$, $\{0,1,2\}$ and $\{1,2\}$ overlap without being equal. They are not equivalence classes or a partition of a set.

The essential infimum and essential supremum describe the lower and upper bounds of a measurable function after disregarding null sets for a specified measure. A pointwise infimum or supremum sees every input; an essential bound sees whether values below or above a proposed threshold occur on a set of positive measure. Thus "essential" means insensitive to changes on a null set, rather than insensitive to unusual values. This distinction makes essential bounds compatible with Lebesgue integrals and the equivalence classes used in Lp spaces. ^[ref].

For a real measurable function $f$ on a measure space $(X,\Sigma ,\mu )$, the definitions areThese can take values in the extended real number line. On a space of positive total measure, finite essential bounds obey ${\mathrm{essinf}}_{\mu }f\le f\le {\mathrm{esssup}}_{\mu }f$ almost everywhere. If $s={\mathrm{esssup}}_{\mu }f$ is finite, every threshold $b<s$ has $\mu (\{f>b\})>0$; the corresponding lower-edge statement is $\mu (\{f<a\})>0$ for every $a>{\mathrm{essinf}}_{\mu }f$. These are the measure-based counterparts of the approximation property of a supremum and approximation property of an infimum.

Here is a two-sided example on $[0,1]$ with Lebesgue measure:The pointwise bounds are $\inf g=-3$ and $\sup g=4$, while the essential bounds are $\mathrm{essinf}g=0$ and $\mathrm{esssup}g=1$. Both exceptional singletons are null sets. But for $0<a<1$, the inputs where $g<a$ still have measure $a$, and those where $g>a$ have measure $1-a$: deleting or adding two individual inputs cannot change those lengths. Hence neither essential bound can be moved inward.

The choice of measure is part of the answer. The same $g$ has essential bounds $-3$ and $4$ under counting measure, because every nonempty set has positive measure. Under the Dirac measure at $1/4$, both essential bounds equal $-3$. A null set for one measure need not be a null set for another.

Nor must the exceptional inputs be isolated. The Dirichlet function, equal to one at rational numbers and zero at irrational numbers, has pointwise supremum one on $[0,1]$, but essential supremum zero for Lebesgue measure: the rational numbers form a countable set that is a null set, even though they are dense. Conversely, a value ten on an interval of length $0.001$ affects the essential supremum just as decisively as it would on half the domain. Small positive measure is not zero measure.

A measurable function is essentially bounded if some finite nonnegative real number $M$ bounds its absolute value almost everywhere. The exceptional null set may contain arbitrarily large values. With Lebesgue measure on $[0,1]$, a function equal to zero outside $\{1/n:n\in {\mathbb{N}}_{>0}\}$ and equal to $n$ at $1/n$ is essentially bounded despite being unbounded pointwise: its exceptional countable set is a null set, so $M=0$ works. Its L-infinity norm is zero because it represents the zero equivalence class in Lp space.

Contrast this with $f(x)=1/x$ on $(0,1]$. For every $M\ge 1$, the values exceeding $M$ occupy $(0,1/M)$, a set of positive Lebesgue measure. Hence no finite almost everywhere bound works, and this function is not essentially bounded. Being finite at every input does not imply essential boundedness.

The essential range of a real or complex measurable function $m$ consists of the values $z$ whose every neighborhood receives inputs of positive measure:It depends on the measure as well as the function. This definition concerns values arbitrarily near $z$, not necessarily inputs at which $m=z$ exactly. It therefore retains meaningful limiting values while ignoring values occurring only on null sets. ^[ref].

For $m(x)=x$ on $(0,1)$ with Lebesgue measure, the ordinary image of a function is $(0,1)$, but the essential range is $[0,1]$. Neither endpoint is attained, yet inputs with $|m(x)|<\epsilon$ or $|m(x)-1|<\epsilon$ have positive measure for every $\epsilon >0$. Every interior value also belongs to the essential range, although its exact fiber is a singleton of zero measure. Positive measure of a fiber is sufficient, but is not necessary, for a value to belong to the essential range.

The two-sided example $g$ from essential infimum and essential supremum illustrates what disappears. It follows $g(x)=x$ on $[0,1]$, except $g(1/4)=-3$ and $g(3/4)=4$. Its ordinary image of a function is $([0,1]\setminus \{1/4,3/4\})\cup \{-3,4\}$, whereas its essential range remains $[0,1]$. The missing ordinary values $1/4$ and $3/4$ still have arbitrarily close neighbors with positive measure; the exceptional values $-3$ and $4$ can each be isolated by a sufficiently small neighborhood whose preimage is a null set.

For the three-point example in L-infinity norm, the values are $1,3,10$. With weights $(0.5,0.5,0)$ the essential range is $\{1,3\}$; with weights $(0.499,0.5,0.001)$ it is $\{1,3,10\}$. A small but positive probability retains ten, so an essential range does not discard a value merely because it is rare. For a real function on a space of positive total measure, the essential infimum and essential supremum are the lower and upper edges of its essential range, allowing infinite bounds. The L-infinity norm instead takes the upper edge of the essential range of $|m|$.

A useful operator connection is the spectrum of a real multiplication operator: on a nonzero L2 space over a sigma-finite measure, multiplication by a real measurable function $m$ has spectrum equal to its essential range, with the natural domain when $m$ is unbounded. Outside that range, $|m-z|$ has a positive lower bound almost everywhere, so multiplication by $1/(m-z)$ gives an inverse function that is a bounded linear operator. Near an essential value, this lower bound fails. Thus discarding null sets is precisely what makes the range match an operator acting on almost everywhere equivalence classes. ^[ref].

For $0<p<1$, write $Q_p(f)=(\int |f{|}^{p}d\mu {)}^{1/p}$ to emphasize that this is a quasi-norm, as in the Lq quasi-norm branch. On two coordinates with counting measure, $u=(1,0)$ and $v=(0,1)$ give $Q_p(u)=Q_p(v)=1$, but $Q_p(u+v)=2^{1/p}>2$. At $p=1/2$, this is the explicit failure $4>1+1$ in the right panel of the diagram. The midpoint $(1/2,1/2)$ has $Q_p=2^{1/p-1}>1$, so the corresponding unit sublevel set is not a convex set.

A useful replacement survives: for nonnegative real numbers $a,b$, $(a+b{)}^p\le a^p+b^p$. Together with the scalar triangle inequality, this yieldsConsequently $d_p(f,g)=Q_p(f-g{)}^p$ is a metric on the equivalence classes of measurable functions with finite $Q_p$, even though $Q_p(f-g)$ itself need not be a metric. Do not replace the failed Minkowski inequality by a universal reversed bound for signed or complex functions: $g=-f\ne 0$ would give a zero sum but two positive separate sizes.

For a function $f:X\to Y$ and $y\in Y$, its fiber of a function over $y$ is the set of all inputs producing that one output:Here $f^{-1}(y)$ is shorthand for a preimage of a singleton set, rather than a claim that an inverse function exists. A fiber can contain several inputs or be empty. It describes exactly which distinctions the function forgets.

For the finite function under image and preimage of a function, the fibers over $0,1,2,3$ are $\{a,b\}$, $\{c\}$, $\{d\}$ and the empty set, respectively. The nonempty fibers form a partition of a set: they are disjoint sets whose union is the domain of a function. Every input belongs to the fiber over its unique output, and two fibers with distinct output labels cannot share an input. A function is injective exactly when every fiber has at most one member; it is surjective exactly when every member of its specified codomain has a nonempty fiber.

More generally, $f^{-1}(U)$ is the union of the fibers over all $y\in U$. The relation $x\sim x^{\prime }$ defined by $f(x)=f(x^{\prime })$ is an equivalence relation, and its equivalence classes are precisely the nonempty fibers. The resulting quotient set is naturally in bijection with the image of a function. This links an input partition to the output values actually reached, without choosing one representative from each class.

Fibers are useful when an output is known but the input is not uniquely determined. A solution set $g^{-1}(\{0\})$ is one fiber, and factorization through a surjection explains when another function depends only on the retained output. Quantum hidden subgroup problem algorithms use functions whose fibers are cosets, giving the same idea a structured form in group theory.

A germ at a point is an equivalence class of functions defined on a neighbourhood of the point: two representatives are equivalent if they agree on some smaller neighbourhood. A germ of a holomorphic function requires its representatives to be holomorphic functions; smooth and continuous germs use their corresponding classes of functions. The germ retains local behavior while forgetting the representative's values farther from the point.

Changing the number of hash buckets and reassigning stored records using the new bucket-index hash function. Amortized analysis of rebuilding and expected value bounds for subsequent lookup address different costs.

Rebuild the balanced example from load factor of a hash table with $m^{\prime }=16$ while keeping exactly the same twelve records. Under $h_m(k)=k\mathrm{mod}m$, keys $0,\dots ,7$ retain their indices, whereas keys $8,9,10,11$ move from hash buckets $0,1,2,3$ to hash buckets $8,9,10,11$. The new chain lengths are twelve ones followed by four zeros; ${\alpha }^{\prime }=12/16=3/4$. Copying the old eight chain heads and adding eight empty heads would leave those four moved keys unreachable by the new lookup rule.

For a short algorithm correctness outline, allocate the new array, visit every old record once, compute its new bucket index, and link it into that hash bucket. At every step, each transferred record is stored exactly once at the index prescribed by the new hash function. Finishing the visit therefore preserves every key-value association and makes the new lookup rule valid. Direct transfer can skip duplicate searches because the old one-value-per-key map already established distinct equivalence classes; rebuilding is not twelve new user insertions.

Under constant-cost hashing, linking and slot initialization, separate chaining rebuilding takes $O(n+m+m^{\prime })$ time complexity: initialize $m^{\prime }$ new heads, examine $m$ old heads, and transfer $n$ records. For doubling, $m^{\prime }=2m$, this is $O(n+m)$ in Big O notation. It is $O(n)$ when the resize policy makes $m$ proportional to $n$, but an enormous mostly empty old array can make scanning its heads dominate. Growing capacity can lower $\alpha$ without shortening a particular chain: the concentrated keys $0,16,\dots ,176$ all remain in hash bucket $0$ after growth from eight to sixteen.

For an insertion-only sequence starting empty, choose initial capacity $m_0=8$ and double before a genuinely new key would make $n>m$. The first three growths occur before inserting records $9,17,33$ and transfer $8,16,32$ existing records. If $N=33$ distinct records are inserted, the transfer count is $8+16+32=56$. More generally, the transferred old capacities form a geometric series; if the last is $M<N$, their sum is $2M-m_0<2N$. Initializing and scanning these geometrically growing arrays also costs $O(N)$ in total, giving $O(1)$ amortized rebuilding overhead per insertion. A triggering insertion still pays for its whole rebuild.^[ref]

The illustrative twelve-record resize above is an explicitly requested rebuild, not a growth triggered by this threshold policy. Checking for an equal key before growing also prevents a value replacement from triggering unnecessary growth. To combine resizing overhead with expected constant insertion time complexity, separately impose the collision and constant-cost assumptions in load factor of a hash table. Amortized analysis distributes rebuilding costs over operations; it does not randomize a poor hash function or bound a single operation's latency. With deletions, separate the grow and shrink thresholds; for example, growing when full and halving at one-quarter live occupancy leaves room for many operations between opposite rebuilds.^[ref]

// Target: computer-science.bigb

For a positive modulus $m$, integers $a,b$ are congruent when their difference is divisible by $m$. This is an equivalence relation: zero is divisible by $m$, changing sign preserves divisibility, and adding two divisible differences proves transitivity. Addition and multiplication respect it, because $(a+c)-(b+d)=(a-b)+(c-d)$ and $ac-bd=c(a-b)+b(c-d)$. Its equivalence classes are the residue classes used in modular arithmetic.

The intersection of any family of equivalence relations on one set is an equivalence relation. Reflexivity and symmetry hold in every component. A pair of consecutive related points belongs to every component, so each component's transitivity supplies the required final pair. Its equivalence classes refine the component partitions. The empty intersection is the universal relation.

A kernel records the information lost by a homomorphism. For a group homomorphism it is the preimage of the identity element, giving the kernel of a group homomorphism; for a ring homomorphism or linear map it is the preimage of zero, giving the kernel of a ring homomorphism or kernel of a linear map. More generally, the relation $a\sim b⟺f(a)=f(b)$ records which inputs the homomorphism identifies. In the group case, its equivalence classes are cosets of the group kernel, as explained by fibres of a group homomorphism are cosets of its kernel.

The equivalence relation used to decide whether two keys name the same record. A hash function must respect key equality; a hash collision does not establish key equality.

The choice of key equality is part of the map's meaning. In the one-value-per-key policy of hash table, one record represents each stored equivalence class of keys: put(k,v) updates an existing equal key and adds a record only if no equal key is present. A collection that deliberately retains repeated equal keys has a different policy; its load count must count those extra records too. Repeated calls and distinct stored records are different quantities.^[ref]

For an original example, restrict keys to two-letter strings using letters A through Z or a through z. Declare two keys equal when replacing every uppercase letter by its lowercase counterpart gives the same string. Assign each letter its position $1,\dots ,26$ in the alphabet, ignoring case, and define the bucket-index hash function as the sum of the two positions modulo $8$, using modular arithmetic. Then AB and ab are equal keys and both select hash bucket $3$. Key ba also selects hash bucket $3$ because $2+1=3$, but is unequal to ab: order still matters. Starting empty, put(AB,7), put(ab,9), put(ba,4) leave two records, with values $9$ for the ab equivalence class and $4$ for ba; the hash table load factor is $2/8=1/4$, not $3/8$.

The compatibility rule is one-way: $x\sim y\Rightarrow h_m(x)=h_m(y)$. Otherwise a lookup using an equal spelling could search a different hash bucket and incorrectly report absence. Equal indices merely allow a key equality check. Both the equivalence relation and the hash function must stay consistent while records are stored; changing equality-relevant key contents can invalidate the lookup rule. The Java contract imposes these consistency and equality requirements on equals and hashCode.^[ref]

Take $\mathbb{R}\times \{0,1\}$ with its Euclidean topology and identify $(t,0)$ with $(t,1)$ for every $t\ne 0$, retaining two distinct origins. This quotient space is locally homeomorphic to the real line but is not Hausdorff. Any open neighbourhoods of its origins have preimages containing $(-\epsilon ,\epsilon )\times \{0\}$ and $(-\delta ,\delta )\times \{1\}$, so a nonzero $|t|<\min (\epsilon ,\delta )$ gives a shared image point. All equivalence classes in the original metric space are finite and closed. This shows that closed equivalence classes do not suffice for a Hausdorff quotient space.

On a measure space of positive total measure, the L-infinity norm of an essentially bounded measurable function is $\Vert f{\Vert }_{\infty }={\mathrm{esssup}}_{\mu }|f|$. The absolute value matters: a constant function $f=-3$ has L-infinity norm three, even though its essential supremum is $-3$. The norm belongs to the equivalence class in Lp space; different representatives equal almost everywhere give the same answer. ^[ref].

For the function $g$ in essential infimum and essential supremum, $\Vert g{\Vert }_{\infty }=1$ under Lebesgue measure, whereas its supremum norm is four. Replacing the exceptional values by the ordinary values $x$ leaves every finite Lp norm unchanged as well: $\Vert g{\Vert }_p=({\int }_0^1{x}^{p}dx{)}^{1/p}=(p+1{)}^{-1/p}$. This tends to one as $p\to \infty$. That agreement explains why the endpoint with exponent $p=\infty$ uses an essential bound rather than a pointwise bound.

A finite probability space makes the distinction between zero and small measure explicit. Let $f(a)=1$, $f(b)=3$, and $f(c)=10$. Assign weights $\mu (a)=1/2-\epsilon$, $\mu (b)=1/2$, $\mu (c)=\epsilon$, where $0\le \epsilon <1/2$. For $1\le p<\infty$,When $\epsilon =0$, the singleton $\{c\}$ is a null set and $\Vert f{\Vert }_{\infty }=3$. For every $\epsilon >0$, however small, $\Vert f{\Vert }_{\infty }=10$. For example, $\epsilon =0.001$ gives $\Vert f{\Vert }_1=2.009$ and $\Vert f{\Vert }_2=\sqrt{5.099}$, yet the large-$p$ Lp norms approach ten. The ordinary pointwise supremum norm is ten in both cases.

Why can a rare value dominate? For a bounded function on a probability space, an almost everywhere bound $|f|\le M$ gives $\Vert f{\Vert }_p\le M$. If $0<t<\Vert f{\Vert }_{\infty }$, the set $A_t=\{|f|>t\}$ has positive probability, so $\Vert f{\Vert }_p\ge t\mu (A_t{)}^{1/p}$. As $p\to \infty$, the factor $\mu (A_t{)}^{1/p}$ tends to one. These two bounds squeeze the limit to $\Vert f{\Vert }_{\infty }$. The probability measure assumption keeps total measure finite; this explanation does not assert the same finite-$p$ integrability on arbitrary infinite-measure spaces.