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For $1<a\le 2$, the radial function $f(x)=(1+|x|{)}^{-a}$ belongs to $L^2({\mathbb{R}}^2)$ and has finite distributional total variation seminorm on a domain, but is not in $L^1({\mathbb{R}}^2)$. Cutting it off outside radius $T$ gives bounded-variation space functions tending to $f$ in $L^2$, with variation tending to that of $f$: the added jump costs $2\pi T(1+T{)}^{-a}\to 0$. Thus the penalty equal to TV on $BV\cap L^2$ and infinity elsewhere is not sequentially lower semicontinuous in $L^2$. The closed homogeneous bounded-variation space extension avoids this domain issue.

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.

Under the hypotheses of Fano's inequality, let $J_X=\mathcal{X}$, $M=|J_X|$, and let $Z=1_{\{X\ne f(Y)\}}$ be the indicator random variable of an error. Its probability of being one is $p_e$. The $h$ in the displayed bound denotes binary entropy $h_2$ in bits. Since $Z$ is a deterministic function of $(X,Y)$, $H(Z\mid X,Y)=0$. Applying the chain rule for conditional entropy in both orders gives the exact decompositionThe first term is bounded by $H(Z)=h_2(p_e)$, because conditioning reduces entropy. For the second term, fix a report $Y=y$: if $Z=0$, then $X=f(y)$ is known; if $Z=1$, then $X$ lies among at most $M-1$ other symbols. Maximum entropy on a finite alphabet bounds the latter uncertainty by ${\log }_2(M-1)$. Averaging over the reports and the error flag givesThis proves Fano's inequality. Events of zero probability contribute zero to this average, so the proof also covers $p_e=0,1$ without assigning a conditional distribution to an impossible event.^[ref]

The decomposition also explains equality. The first bound is exact when the indicator random variable $Z$ and $Y$ are independent random variables: knowing the report does not change the error probability. The second is exact when, for every report with positive error probability, the wrong-symbol conditional distribution is uniform on all $M-1$ alternatives. Both conditions hold in the four-symbol toy channel of Fano's inequality. At $q=0$, the second condition is vacuous; at $q=1$, the flag is constant but three alternatives remain, giving $H(X\mid Y)={\log }_{2}3$. For $M=2$, there is just one alternative after an error, so the second term vanishes: all remaining uncertainty is whether the single alternative must replace the guess.

A fast-growing hierarchy indexes number-theoretic functions by an effective system of ordinal notations. Successor stages iterate the preceding function, while limit stages diagonalize along a chosen fundamental sequence of a limit ordinal. These choices are part of the definition. With the standard Cantor normal form sequences below epsilon zero, each fixed $F_{\alpha }$ for $\alpha <{\epsilon }_0$ is provably total in Peano arithmetic, but the uniform endpoint $F_{{\epsilon }_0}$ is not. Every function provably total in Peano arithmetic is eventually bounded by some lower level. These classification results are proved in epub.ub.uni-muenchen.de/3843/1/3843.pdf .

The set of all feasible points of an optimization problem. It depends on the optimization constraints and the decision domain; the objective function ranks its members.

Think of three successive questions. Which decisions can be represented in the model? Which satisfy every optimization constraint? Which of those receive the best objective function value? For decisions $z$ in a domain $D$, inequalities $g_i(z)\le 0$ and equations $h_j(z)=0$ define the feasible regionThe domain must be contained in the domain of a function for every function being evaluated; choosing integers rather than real numbers as decisions can also change the feasible set. Constraint satisfaction determines membership of $F$. Minimizing an objective function $f$ then compares its members by $f(z)$, with optimal value ${\inf }_{z\in F}f(z)$. Changing the scores while keeping $D$ and the optimization constraints fixed leaves $F$ unchanged, provided the new objective function is defined throughout $D$. These distinctions separate an allowed decision from a best decision.^[ref]

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 finite coloring of a set $X$ is a function $c:X\to C$ into a finite set $C$ of colors. A subset is monochromatic when the restriction of $c$ to it is constant.

An optimal transport plan of finite cost for a finite continuous cost function is c-cyclically monotone. A strict violation at finitely many support points persists in small product neighborhoods; subtracting small normalized pieces and inserting cyclically re-paired product measures preserves both marginals and strictly improves the finite cost. The finite-value hypothesis matters: if every competitor has infinite cost, a nonmonotone plan may still be an extended-value minimizer.

A finite difference compares the values of a function at finitely separated arguments. For example, the forward finite difference with step $h$ isIt is the discrete analogue of a derivative and lowers the degree of a nonconstant polynomial by one.

For any finite set of quadrature nodes $x_1,\dots ,x_m$ in $[a,b]$, $a<b$, consider the polynomialEvery sample is zero, so every quadrature rule at these quadrature nodes gives $Q(p)=0$. Yet $p$ is nonnegative and strictly positive away from its finitely many polynomial roots; its continuity makes the integral positive. Consequently the smooth functions $0$ and $g(x)=p(x)/A$ have identical samples but definite integrals $0$ and $1$. Multiplying $g$ by any positive constant makes the difference arbitrarily large. This is an information limit, even with exact arithmetic; merely assuming smoothness without quantitative bounds does not remove it. The related degree ceiling for quadrature exactness uses the same squared nodal polynomial to limit polynomial exactness.

For example, the endpoint quadrature nodes $0,1$ give $p(x)=x^2(1-x{)}^2$, $A=1/30$, and $g(x)=30x^2(1-x{)}^2$. Both the zero function and $g$ produce zero under the one-panel trapezoidal rule, although $I(g)=1$. More generally, any estimate based only on their shared samples has absolute error at least $1/2$ for one of these two integrands, by the triangle inequality.

The obstruction also applies to a deterministic adaptive procedure that terminates after finitely many evaluations on the zero function: form $p$ using every quadrature node it visited. On $g$, the same zero responses lead to the same choices and answer. Certified quadrature error control therefore needs additional information, such as a known bound on relevant derivatives, rather than agreement among sampled approximations alone.

In a primitive recursion, an existential representation of the step function may need a different witness tuple at every time. Code each coordinate of the finite witness array by a separate beta-function code pair. All row witnesses then have bounded remainder quantifiers under the bounded time quantifier, while the finitely many code pairs remain in the outer unrestricted existential block.

Foundations of mathematics asks what our objects mean, what we assume about them, and why an argument establishes its conclusion. Set theory gives a common language for collections and functions; mathematical logic makes the structure of statements and deductions precise. A mathematical definition fixes a meaning, an axiom supplies an assumption, and a mathematical proof explains what follows. Separating these roles makes results reusable: once the group axioms are enough for a conclusion, that conclusion applies to every group, regardless of how its members are represented.

The assumptions can change the answer to a familiar calculation. Over the real numbers, $x^2=x$ factors as $x(x-1)=0$, so $x$ is zero or one. The same reasoning holds in any integral domain, because a product can be zero only when a factor is zero. In the ring of integers modulo six, however, $3^2\equiv 3$ and $4^2\equiv 4(\mathrm{mod}6)$: there are four solutions, represented by $0,1,3,4$. The extra solutions are idempotents, and the failed step involves zero divisors. Writing the domain is part of explaining the answer.

This precision also reveals surprising possibilities. A two-dimensional grid of natural numbers is a countable set, while all infinite binary sequences form an uncountable set. The Cantor pairing function supplies a complete enumeration of the first; Cantor's diagonal argument defeats every proposed enumeration of the second. Both arguments are short, yet distinguish two sizes of infinity. The subject's appeal is this ability to turn a vague question into one whose assumptions, construction, and limits can all be inspected.

Fourier analysis describes a function by the oscillations that compose it. A graph records values at successive positions or times; a frequency spectrum records how much of each frequency is present, together with its wave phase. The same complicated shape can become a short list of simple components. For periodic functions, the components form a Fourier series; on the whole real line, the Fourier transform uses a continuous range of frequencies, and the Fourier inversion theorem puts them back together.

For a concrete example, consider the trigonometric polynomialWith $t$ measured in seconds, its three Fourier harmonics have frequencies $1$, $3$, and $5$ cycles per second and amplitudes $1$, $1/2$, and $1/4$. Each cosine has zero wave phase. Adding them creates the sharper peaks in the figure. The frequency spectrum makes the recipe immediately visible; changing an amplitude changes the shape without changing the component's frequency. Fourier orthogonality lets us recover each Fourier coefficient by averaging against the corresponding sine or cosine, because the other Fourier harmonics cancel over a full period of a function.

The deeper reason these components are so useful is that they simplify operations. By Euler's formula, the exponential function $e^{i\omega x}$ packages a cosine and a sine into one Fourier mode. A translation or a derivative changes only its multiplier:Thus Fourier modes are simultaneous eigenfunctions of these operations. In Fourier series, this gives a literal diagonalization in the Fourier basis; on the real line, the Fourier transform gives the continuous counterpart, with each frequency handled separately. The Translation property of the Fourier transform and the Fourier transform of a derivative turn shifting and differentiating a whole function into multiplication of its Fourier transform. A convolution likewise becomes multiplication by the convolution theorem. This is beautiful because one description simplifies several apparently different operations at once.

For example, the heat equation $u_t=\kappa u_{xx}$, with thermal diffusivity $\kappa >0$, evolves a spatial Fourier mode asFine ripples have larger angular frequencies and decay faster: the formula explains why heating smooths a jagged temperature profile. For a general initial profile with sufficient regularity, the Fourier transform gives $\hat{u}(\omega ,t)=e^{-\kappa {\omega }^{2}t}\hat{u}(\omega ,0)$, and Fourier inversion recovers the temperature. Similarly, multiplying selected parts of a frequency spectrum suppresses unwanted components. Claude E. Shannon's Communication in the Presence of Noise (1949), Section III, describes ideal filtering as a projection that retains some frequency coordinates and removes others.^[ref]

The physical motivation is already visible in Joseph Fourier's Théorie analytique de la chaleur (1822). Chapter III constructs heat profiles using sums of sines and cosines; Chapter IX, articles 355–359, passes from Fourier series to integrals as the spatial interval becomes unbounded. These heat problems explain the motivation for the discrete and continuous descriptions. Their modern formulations specify which functions admit reconstruction and in what sense the expansions converge.^[ref][ref]