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A natural interval extension evaluates a chosen expression by replacing its arithmetic operations with interval arithmetic operations, its constants with enclosing closed intervals, and any additional functions with valid interval enclosures. Each intermediate result must remain defined on its inputs. The resulting $F(X)$ encloses the actual image of a function $f(X)$, but its width depends on the expression used. Rump's INTLAB tutorial describes this inclusion principle and warns that repeated inputs can produce severe overestimation.^[ref]

False. Take $A=\{0\}$ and $B=C=\{0,1\}$, with $f(0)=0$ and $g={\mathrm{id}}_B$. Then $f$ is an injection and $g$ is a surjection, but the function composition $g\circ f$ has image of a function $\{0\}$, so it is not a surjection onto $C$.

Dominated convergence makes $\hat{f}$ continuous, and $\Vert \hat{f}{\Vert }_{\infty }\le \Vert f{\Vert }_1$. Smooth compactly supported functions are dense in $L^1$. Their Fourier transforms decay at infinity by integration by parts, so uniform approximation proves the Riemann-Lebesgue lemma: $\hat{f}\in C_0$.

For density, smooth compactly supported functions are also uniformly dense in $C_0$ by cutoff and mollification. Each such target $g$ is a Schwartz function; its inverse Fourier transform is another Schwartz function, hence belongs to $L^1$, and Fourier inversion recovers $g$. Therefore the image of a function of $L^1$ is dense in $C_0$.

The $L^1$ Fourier inversion theorem says that if $f,\hat{f}\in L^1$, then $f$ agrees almost everywhere with the inverse integral of $\hat{f}$. If $\hat{f}=0$, this applies and gives $f=0$ almost everywhere. Hence the Fourier transform is injective as a map of $L^1$ equivalence classes, with dense image in $C_0$.

Let $e=(1,0)$ and defineThen $\Vert g(x)-x\Vert \le 1$ everywhere, but $0$ is not in the image of a function. The map is discontinuous at $0$, giving a bounded-displacement map which is not surjective.

If $x\ne y$, one of $x<y$ and $y<x$ holds. Since $f$ is strictly increasing, the corresponding values are strictly ordered, so $f(x)\ne f(y)$. Thus $f$ is an injection. Every element of the image of a function $f([a,b])$ equals $f(x)$ for some $x\in [a,b]$, which is precisely surjectivity onto that image of a function. Hence $f:[a,b]\to f([a,b])$ is a bijection.

With the strict monotonicity assumed at the start of the question, parts (a) and (c) give a strictly increasing inverse functionIts image of a function is exactly the interval between its endpoint values. Apply part (c) to $g$, with domain $[f(a),f(b)]$, to conclude that $g$ is a continuous function, including at both endpoints.

If the word “increasing” in this subpart were interpreted as merely nondecreasing, an inverse function need not exist: a constant function is a counterexample. The preceding strict hypothesis supplies the required injectivity.

The shift $s:\mathbb{N}\to \mathbb{N}$, $s(n)=n+1$, is injective, so $s\in I(\mathbb{N})$. It is not surjective: the least natural number is missing from its image of a function, whether $\mathbb{N}$ starts at zero or one. For every $t:\mathbb{N}\to \mathbb{N}$, the image of a function of $s\circ t$ is contained in the image of a function of $s$, so $s\circ t\ne {\mathrm{id}}_{\mathbb{N}}$.

The identity function belongs to $I(\mathbb{N})$ and is its identity element under function composition; any group structure using that operation would have this same identity element. Since $s$ has no right inverse element, $I(\mathbb{N})$ is not a group under function composition. It is a monoid, since injectivity is preserved by function composition.

An injective function from a finite set to itself is surjective: its image of a function contains as many distinct elements as the entire set, so it cannot omit one. Thus every element of $I(X)$ is a bijection, and conversely every bijection is injective. HenceIt is a group under function composition by i.

Not injective; its image of a function is exactly $\{1,2,3,\dots \}$. For example, both $x=e^{-1}$ and $x=e^{-1/2}$ give the ceiling function value $1$. Since $0<x<1$, $(\ln x{)}^2>0$, so the value $0$ is never attained. For every positive integer $j$,Each such interval is nonempty and contained in $(0,1)$, with the upper endpoint $1$ excluded when $j=1$. Thus the answer concerning surjectivity depends on the convention for the natural numbers: onto if $\mathbb{N}=\{1,2,\dots \}$, and not onto if $0\in \mathbb{N}$. The source does not settle that convention.

For an odd prime $p$, a nonzero congruence class $a$ is a quadratic residue when $a\equiv x^2(\mathrm{mod}p)$ for some integer $x$. Here "quadratic residue" excludes zero: zero is a square of a number, but is neither a quadratic residue nor a quadratic nonresidue under this convention. Some treatments include zero among quadratic residues; the distinction matters when counting them or discussing their subgroup.^[ref]

Modulo seven, squaring the six nonzero congruence classes givesThus the set of quadratic residues is $Q_7=\{1,2,4\}$, and the quadratic nonresidues are $\{3,5,6\}$. In particular, $x^2\equiv 2(\mathrm{mod}7)$ has exactly the two solutions $x\equiv 3,4(\mathrm{mod}7)$: $3^2=9\equiv 2$ and $4^2=16\equiv 2$. Modulo eleven the quadratic residues are instead $Q_{11}=\{1,3,4,5,9\}$. Two is therefore a quadratic residue modulo seven and a quadratic nonresidue modulo eleven: being a quadratic residue depends on the modulus.

Why do the square roots come in pairs? If $x^2\equiv y^2(\mathrm{mod}p)$, the prime number $p$ divides $(x-y)(x+y)$, so it divides one factor: $y\equiv x$ or $y\equiv -x(\mathrm{mod}p)$. For nonzero $x$ these are distinct, because $p$ is an odd prime. Every nonempty nonzero fiber of a function of $s(x)=x^2$ therefore contains exactly two inputs. Pairing the $p-1$ nonzero congruence classes gives exactly $(p-1)/2$ quadratic residues and $(p-1)/2$ quadratic nonresidues. Including zero gives $(p+1)/2$ different squares, with one rather than two square roots for zero.

The quadratic residues form a subgroup of the multiplicative group of a finite field ${\mathbb{F}}_p^{\times }$, with index of a subgroup two. Indeed, one is a square, $(u^2)(v^2)=(uv{)}^2$, and $(u^2{)}^{-1}=(u^{-1}{)}^2$; the inverses exist because the inputs are nonzero in the prime field. Equivalently, the squaring group homomorphism has kernel of a group homomorphism $\{1,-1\}$ and image of a function $Q_p$. The apparent loss of a sign in squaring becomes a precise division of the nonzero arithmetic into two equal parts.

This structure turns a search for square roots into a yes-or-no test: Euler's criterion detects quadratic residues through modular exponentiation, and the Legendre symbol records the result as a sign. For a general integer modulus, the modular congruence $x^2\equiv a(\mathrm{mod}n)$ still makes sense, but the two-root count and the prime field argument need not survive. For example, $x^2\equiv 1(\mathrm{mod}15)$ has four solutions $1,4,11,14$; the Chinese remainder theorem separates the sign choices modulo three and five.

A real-valued function $f:D\to \mathbb{R}$ is a strictly decreasing function if every pair $x<y$ in its domain of a function satisfies $f(x)>f(y)$. It is a nonincreasing function and is injective, with a strictly decreasing inverse function on its image of a function. Negating a strictly increasing function gives a strictly decreasing function: $f(x)=-x^3$ is an example on the real line, even though its derivative vanishes at zero. Equal outputs are forbidden, unlike for a general nonincreasing function.

A real-valued function $f:D\to \mathbb{R}$ is a strictly increasing function if every pair $x<y$ in its domain of a function satisfies $f(x)<f(y)$. It is therefore injective, and each output has at most one preimage. Its inverse function on its image of a function is also strictly increasing. Every strictly increasing function is a nondecreasing function; the converse fails when outputs repeat.

The polynomial $f(x)=x^3$ is a strictly increasing function on the real line, although its derivative $f^{\prime }(x)=3x^2$ is zero at zero. Indeed, for $x<y$,because $x^2+xy+y^2=(x+y/2{)}^2+3y^2/4$ can vanish only when $x=y=0$. Thus a positive derivative everywhere is sufficient on a real interval, but is not necessary for a strictly increasing function.

Strict monotonicity does not ensure continuity: $g(x)=x$ for $x<0$ and $g(x)=x+1$ for $x\ge 0$ is a strictly increasing function with a jump discontinuity. Its image of a function omits $[0,1)$, so the inverse function exists only on the actual image of a function, rather than on all the real numbers.

A subsequential limit of a sequence $(a_n)$ is a value $L$ approached by some subsequence $(a_{n_k})$ with strictly increasing indices $n_k$. For $a_n=(-1{)}^n(1+1/n)$, the subsequences at even integers and odd integers give subsequential limits one and minus one. A finite value appearing among only finitely many initial terms does not become a subsequential limit merely by being a member of the image of a function represented by the sequence.

For a bounded sequence of real numbers, its subsequential limits form a nonempty bounded set that is also a closed set. The Bolzano-Weierstrass theorem guarantees nonemptiness, and its extreme values are the limit inferior and limit superior. The sequence in sparse peaks can determine a limit superior has the two subsequential limits zero and one, despite almost all indices in the natural density sense carrying zero.

A uniformly continuous function has one input tolerance valid throughout its domain of a function. For $f:D\to \mathbb{R}$, with $D\subseteq \mathbb{R}$, uniform continuity meansChoose the output tolerance $\epsilon$ first, then a positive $\delta$ valid for every pair of function inputs. It may depend on $\epsilon$, the function and its domain of a function, but not on the pair’s location. The absolute values measure input and output distance.^[ref]

Compare the quantifier order with the epsilon-delta definition of continuity:Both lines end with $|x-a|<\delta \Rightarrow |f(x)-f(a)|<\epsilon$. In the first, $\delta$ can depend on the point $a$; in the second, it is chosen before $a$. Thus uniform continuity implies continuity at every point. Each requested $\epsilon$ gets its own shared tolerance.

For an exact example, take the polynomial function $f(x)=x^2$ on the closed interval $D=[0,2]$. For every pair of function inputs in that interval, polynomial factorization givesConsequently $\delta =\epsilon /4$ works everywhere on this domain of a function. With $\epsilon =0.4$, the common tolerance is $\delta =0.1$. Near the right endpoint, for example, $x=1.92$ and $y=2$ have input distance $0.08$ and output distance $4-1.92^2=0.3136<0.4$. The bound is sufficient; it does not claim the largest admissible input window at each point.

On the whole real line, the same polynomial function is a continuous function but fails uniform continuity. Take the sequences $x_n=n$ and $y_n=n+1/n$, for positive integers $n$. ThenFor any proposed $\delta >0$, choose an integer $n>1/\delta$. The inputs are closer than $\delta$, yet their outputs violate the fixed tolerance $\epsilon =1$. The sequences need not themselves have a finite limit: their mutual distance is what tends to zero. This is the obstruction captured by the sequential criterion for uniform continuity. Restricting the domain of a function to $[0,2]$ removes these escaping pairs.

A uniformly continuous function need not be a bounded function. The identity function $f(x)=x$ on the real line has $|f(x)-f(y)|=|x-y|$, so $\delta =\epsilon$ always works, although its image of a function is the unbounded real line. Uniform continuity controls changes between nearby inputs, not the overall size of outputs. Likewise, an unbounded domain of a function does not by itself prevent uniform continuity.

The square root $g(x)=\sqrt{x}$ supplies a different common tolerance on the interval $[0,\infty )$. Assuming $x\ge y\ge 0$, put $u=\sqrt{x}$ and $v=\sqrt{y}$. Since $0\le u-v\le u+v$,Interchanging the inputs when necessary provesThus $\delta ={\epsilon }^2$ guarantees uniform continuity on the entire domain of a function, including zero. For $\epsilon =0.4$, any input distance below $0.16$ suffices. The pairs $(0,0.09)$ and $(4,4.09)$ both have input distance $0.09$; their output distances are respectively $0.3$ and $0.09/(\sqrt{4.09}+2)<0.0225$. The common guarantee accommodates quite different local sensitivities.

This Hölder continuity bound allows a curved tolerance rule. It is weaker than a Lipschitz bound: at zero the output change divided by the input change isNo finite Lipschitz constant works near zero, even though uniform continuity does. A Lipschitz function does have uniform continuity, choosing $\delta =\epsilon /L$ when its Lipschitz bound $L$ is positive; when $L=0$ it is a constant function.^[ref]

For general metric spaces, replace the absolute values by the respective distance functions. The Heine-Cantor theorem gives an existence guarantee: every continuous function on a compact metric space is uniformly continuous. In particular, continuity on a finite closed interval provides a common tolerance, even when no simple formula for it is known.^[ref]

For a short proof outline, fix $\epsilon >0$. Continuity gives each point $a$ an open ball of radius $r_a>0$ within which outputs differ from $f(a)$ by less than $\epsilon /2$. The open balls of half these radii still cover the domain of a function. Compactness supplies a finite subcover. Take $\delta$ to be the smallest half-radius in this finite set. Two inputs closer than $\delta$ lie together in one of the original open balls, so the triangle inequality bounds their output distance by $\epsilon$. The beautiful step is that the compact set turns local choices into finitely many choices, whose minimum stays positive.

A shared tolerance also makes uniformly continuous functions preserve Cauchy sequences: after choosing $\delta$ for $\epsilon$, all sufficiently late input pairs are closer than $\delta$, so all corresponding output pairs are closer than $\epsilon$. Unlike a convergent sequence argument, this does not require an input limit inside the domain of a function. It is why uniform continuity is useful for extending a function toward missing endpoints and for the decay argument below.