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The analytic Wiener algebra consists of the power series $f(z)={\sum }_{n\ge 0}{a}_n{z}^n$ with ${\sum }_{n\ge 0}|a_n|<\infty$, identified with their values on the unit circle. The norm is $\Vert f\Vert =\sum |a_n|$. Discrete convolution gives multiplication and a submultiplicative norm; completeness follows from the absolutely summable sequence space. Each series converges uniformly on the closed unit disk and is holomorphic in its interior.

Circular convolution of two length-$N$ sequences uses modular arithmetic for their indices. Extending both inputs periodically gives the displayed formula. Equivalently, fold a finite discrete convolution into residue classes modulo $N$ by adding entries whose indices are congruent. The discrete Fourier transform of the circular convolution is the product of the two discrete Fourier transforms, with the usual unnormalized forward transform and $1/N$ inverse convention.

Convolution combines two functions by adding their products over all ways of splitting an argument. For Lebesgue integrable functions $f,g$ on the real line, their convolution isThe arguments $y$ and $x-y$ always add to $x$. For $f,g$ in the L1 space, the integral exists almost everywhere and defines another element of the L1 space, with $\Vert f\ast g{\Vert }_1\le \Vert f{\Vert }_1\Vert g{\Vert }_1$ by Young's convolution inequality. Particular examples below have a value at every $x$.

To see what the integral counts, take the indicator function $q=1_{[0,1]}$. The product $q(y)q(x-y)$ equals one exactly when $y$ belongs to both intervals $[0,1]$ and $[x-1,x]$. Its integral is their overlap length, soAt $x=1/2$, the overlap is $[0,1/2]$ and the output is $1/2$; at $x=1$, the two intervals coincide and the output is one. The result is the shifted triangular function $\mathrm{tri}(x-1)$. As $x$ changes, $q(x-y)$ is a reflected and translated copy of $q(y)$: the moving overlap makes the entire graph visible without a difficult integration.

The same calculation gives the probability distribution of a sum of two independent random variables. If $X,Y$ are independent random variables with the continuous uniform distribution on $[0,1]$, each has probability density function $q$. Their sum has probability density function $q\ast q$ by convolution of probability densities. The probability density function at one is larger than near zero or two because there are more combinations of the two inputs giving that sum. Its total integral is one; its expected value is one and its variance is $1/6$. These follow equally from integrating the triangular function or from linearity of expectation and variance additivity for independent random variables.

Convolution also gives a useful averaging operation. For $\epsilon >0$, let $k_{\epsilon }(y)=1_{[-\epsilon ,\epsilon ]}(y)/(2\epsilon )$. Thenthe mean of $f$ in a neighborhood of $x$. A smaller $\epsilon$ examines a smaller neighborhood. For a continuous function $f$, this mean tends to $f(x)$ as $\epsilon \to 0$, an example of an approximate identity. Integrating a sharp input against a distributed convolution kernel can produce a smoother output; the triangular example turns two discontinuous inputs into a continuous output.

In signal processing, a linear time-invariant system combines shifted copies of its impulse response by the same rule. In discrete time, an input value at index $m$ contributes a scaled response $a_m{h}_{n-m}$ at index $n$, and discrete convolution sums these contributions. This is the superposition interpretation developed in Oppenheim, MIT Signals and Systems, Lecture 4, convolution sums and integrals. The convolution theorem then gives another description: transform both inputs with a Fourier transform and multiply their transforms. Combining shifts, combining independent quantities and multiplying frequency descriptions are three faces of the same operation.

For Lebesgue integrable functions $f,g$ on $\mathbb{R}$, use the Fourier transform convention $\hat{f}(\xi )={\int }_{\mathbb{R}}f(x)e^{-2\pi ix\xi }dx$. ThenThus the integral combining all translated products becomes an ordinary multiplication at each frequency. Different Fourier transform normalizations can place a constant in this identity; fixing the convention prevents that ambiguity. Its use in filtering is illustrated by ^[ref].

For the indicator function $q=1_{[0,1]}$,with $\mathrm{sinc}(0)=1$. Therefore the triangular function $q\ast q$ has Fourier transform $e^{-2\pi i\xi }{\mathrm{sinc}}^2(\xi )$. The extra factor of the sinc function improves the large-frequency bound from a constant times $1/|\xi |$ to one times $1/|\xi {|}^2$, matching the smoother output in the overlap picture. Fourier decay of repeated interval convolutions extends this calculation to further repetitions.

For a proof outline, Fubini's theorem permits exchanging the two integrals because the integral of $|f(y)g(x-y)|$ over $(x,y)$ is $\Vert f{\Vert }_1\Vert g{\Vert }_1<\infty$. Set $u=x-y$ in the inner integral:The factorization of the complex exponential is the mechanism behind the simplification. The discrete convolution version similarly turns coefficient sums into products of generating functions.

Store an integer in a nonempty array of numeral digits, with the least significant digit first and a fixed radix $b\ge 2$. Normalized digits satisfy $0\le a_i<b$. High-order zeros do not change the value. Adding digit arrays coefficientwise or multiplying them by discrete convolution gives the correct value before normalization, because these operations add or multiply the corresponding powers of the radix. Exact temporary arithmetic, or a radix and accumulator size chosen to prevent integer overflow, is required.

For sequences $a,b$ indexed by the integers, discrete convolution adds products whose indices sum to the output index:For finite sequences, treat every unspecified entry as zero; the sum then has finitely many nonzero terms. For infinite sequences, a convergence hypothesis is needed, for example absolute summability of both sequences.

Take $a=(1,2,1)$ at indices $0,1,2$ and $b=(1,1)$ at indices $0,1$. Their convolution is $(1,3,3,1)$ at indices $0,1,2,3$:Equivalently, $c_n=a_n+a_{n-1}$: add an unshifted and a one-step shifted copy of $a$. This explains the output by superposition, with every index included.

The same products appear in polynomial multiplication and generating functions:A term $a_m{z}^m$ times $b_k{z}^k$ contributes to coefficient $m+k$, precisely the index condition in discrete convolution. In probability, $a$ counts the outcomes of two independent fair coin tosses by their number of heads, while $b$ counts the outcomes of one more toss. Dividing the output by eight gives the probability mass function $(1,3,3,1)/8$ of the binomial distribution with $n=3$ and $p=1/2$. No new arithmetic is needed to pass from coefficient multiplication to counting independent sums.

The full finite convolution is different from circular convolution, where indices wrap modulo a chosen length. Wrapping this four-term output to length three adds its last entry to its first and gives $(2,3,3)$. When using a discrete Fourier transform to compute the full convolution, zero padding to length at least the sum of the input lengths minus one prevents this wraparound. ^[ref]

A linear time-invariant system has finite impulse response when its impulse response is nonzero at only finitely many indices. For a causal filter supported at indices $0,\dots ,M-1$,This is a finite discrete convolution. The coefficients $h[j]$ specify the complete operation; they need not be positive or equal. Such a filter has a simple bounded-input estimate, $|y[n]|\le \Vert x{\Vert }_{\infty }{\sum }_j|h[j]|$, so bounded inputs remain bounded. The moving average chooses $h[j]=1/M$.

The frequency response gives the change in complex amplitude of a sustained oscillation under a linear time-invariant system. If the impulse response is finite or has absolute summability, substituting $x[n]=e^{\mathrm{i}\omega n}$ into its discrete convolution yieldsThus $|H(\omega )|$ is the amplitude gain and its complex argument is the phase change. Real sinusoids follow by taking real or imaginary parts. Here $\omega$ is radians per sample; $\omega =2\pi f$ for $f$ cycles per sample. A physical sampling rate converts that normalized frequency into cycles per second.

For a trailing moving average,with removable singularities evaluated from the finite sum. In particular $H(0)=1$. For $M=5$ and $\omega =2\pi /5$, the five roots of unity sum to zero, so the oscillation is canceled. At small positive frequencies in the first pass region, the real sine ratio is positive and the phase factor gives a delay of $(M-1)/2$ samples.

Increasing $M$ moves the first zero to $\omega =2\pi /M$, narrowing the main region of large gain. It does not create an ideal low-frequency cutoff: later side lobes transmit some higher-frequency components, and suppressing a component is only helpful when that component is unwanted. The frequency plot is the same operation as the delayed-sample average; the five-vector diagram explains one exact zero geometrically.

A linear time-invariant system combines two properties. Linearity means that the output from a linear combination of inputs is the same linear combination of their outputs. Time invariance means that shifting an input by an integer delay shifts its output by exactly that delay. The operation has no preference for a particular time origin.

For discrete time, write the unit impulse as $\delta [n]=1$ at $n=0$ and zero otherwise, a Kronecker delta. Its response $h[n]=T[\delta ][n]$ is the impulse response. A finite input can be decomposed into shifted impulses, givingThe superposition principle supplies the sum and time invariance supplies the shifted copies of $h$. Infinite sums need convergence assumptions; for a bounded input and an impulse response $h$ with absolute summability, the discrete convolution converges absolutely. For a physical system with stored initial energy, this construction describes the response with zero initial state; a separate free response accounts for nonzero initial conditions.

The gain is reuse. Measure or calculate one impulse response, and the convolution predicts the response to every allowed signal without solving a new dynamical problem each time. Alternatively, the frequency response predicts how each oscillatory component changes. ^[ref].

Use the following general Banach algebra facts: in a complex commutative unital Banach algebra, maximal ideals are exactly the kernels of algebra characters, meaning nonzero complex multiplicative linear functionals. Such a character satisfies $\varphi (1)=1$ and $|\varphi (f)|\le \Vert f\Vert$, hence is continuous. The maximal ideal space is given the Gelfand topology, that is, pointwise convergence of characters on algebra elements. These facts follow from closedness of maximal ideals, the Gelfand-Mazur theorem for their quotients, and the spectral bound for character values. They do not identify any of the three spaces by themselves.

For the Wiener algebra, write $f={\sum }_{n\in \mathbb{Z}}{a}_n{u}^n$, where $u(e^{it})=e^{it}$ and $\Vert f{\Vert }_A=\sum |a_n|$. The Fourier series converges absolutely in the algebra norm. If $\varphi$ is a character, set $z=\varphi (u)$. Since $u$ is invertible and $\Vert u{\Vert }_A=\Vert u^{-1}{\Vert }_A=1$, multiplicativity and the norm bound give $|z|\le 1$, $|z^{-1}|\le 1$. Thus $|z|=1$. Continuity now givesConversely, for every $z\in \mathbb{T}$, evaluation is nonzero, linear and multiplicative, and $|f(z)|\le \sum |a_n|$ makes it continuous. Distinct $z$ give distinct characters because they have different values on $u$. Hence the character space of the Wiener algebra is the circle.

For the analytic Wiener algebra, the same expression uses only $n\ge 0$. A character is again determined by $z=\varphi (u)$, but $u^{-1}$ is no longer in this algebra, so only $|z|\le 1$ is forced. Continuity gives $\varphi (f)={\sum }_{n\ge 0}{a}_n{z}^n$. Conversely, every $z$ in the closed unit disk defines evaluation on this absolutely convergent power series. It is bounded and multiplicative, since multiplication of the series is absolutely convergent discrete convolution. Distinct $z$ again differ on $u$. Therefore the character space of the analytic Wiener algebra is the closed disk, including its interior and boundary, not just the original boundary circle.

For $C(\mathbb{T})$ with the supremum norm, let $M=\ker \varphi$ for a character. Suppose the functions in $M$ had no common zero. At each point choose some $f\in M$ nonzero there. Continuity supplies a neighborhood where it remains nonzero; compactness gives a finite subcover associated to $f_1,\dots ,f_k\in M$. Thenis continuous and strictly positive everywhere. It belongs to $M$ because $M$ is an ideal, but it is invertible in $C(\mathbb{T})$ with continuous inverse $1/g$. This would imply $1\in M$, a contradiction. There is therefore a common zero $z$ of $M$. The point-evaluation kernel $I_z=\{f:f(z)=0\}$ is maximal, and $M\subseteq I_z$ forces equality. Since $\varphi (1)=1$, writing $f=f(z)1+(f-f(z)1)$ then proves $\varphi (f)=f(z)$. Conversely every evaluation is a character, and $u$ distinguishes the points. Thus the character space of continuous functions on the circle is the circle.

These are topological identifications as well as bijections. The evaluation map $z\mapsto {\varphi }_z$ is continuous for the Gelfand topology, because each fixed algebra element is a continuous function of $z$: absolute convergence gives uniform convergence on the circle or closed disk in the first two cases. The parameter spaces are compact, while the character spaces are Hausdorff as subspaces of the pointwise topology. A continuous bijection from a compact space to a Hausdorff space is a homeomorphism. Consequently the three maximal ideal spaces are, respectively,In each case the maximal ideal associated to a point is the kernel of the evaluation just described. The spelling “Weiner” in the source refers to the Wiener algebra.

Termwise differentiation of a Fourier series multiplies a mode by its wavenumber, soThe convolution theorem turns pointwise multiplication into discrete convolution:soThe assumed real analyticity justifies these termwise operations through rapid decay of the Fourier coefficients.

Differentiating the truncated Fourier expansion givesThe $n$th Fourier coefficient of the product $c{u}_x$ is the discrete convolutionProjecting the advection equation onto modes $|n|\le d$ therefore givesThusEquivalently, ifthenThis is the Fourier spectral method for variable-coefficient advection.

For each nonnegative integer $z$, the events $\{X=z-y,Y=y\}$, $0\le y\le z$, are pairwise disjoint and their union is $\{X+Y=z\}$. By countable additivity and then independence,This is the discrete convolution of the two probability mass functions. If the convention for natural numbers excludes zero, set both probability mass functions equal to zero there; the formula remains valid. Also $p_Z(z)=0$ for negative $z$.

Use schoolbook multiplication on the digit coefficients:

function Mul(a, b: number): number
    new(result, len(a)+len(b))
    for k := 0 to len(result)-1 do
        result[k] := 0
    for i := 0 to len(a)-1 do
        for j := 0 to len(b)-1 do
            result[i+j] := result[i+j] + a[i]*b[j]
    Normal(result)
    return result

This is discrete convolution of the digit arrays. Expanding the product showsbefore normalization. Normalization preserves the value and establishes the digit bounds. Allocating fresh storage makes input aliasing harmless. The convolution uses $O(LM)$ coefficient products for input lengths $L,M$. Intermediate products and sums need sufficiently wide or arbitrary-precision arithmetic; fitting each normalized digit into a small integer does not alone prevent accumulator overflow.

The Wiener algebra consists of periodic functions with absolutely summable Fourier coefficients, with norm ${\sum }_k|a_k|$. Multiplication corresponds to discrete convolution of coefficients, so the norm is submultiplicative. Consequently $e^f={\sum }_{n\ge 0}{f}^n/n!$ also belongs to the algebra when $f$ does. Every such Fourier series is uniformly convergent and defines a continuous function.

Zero padding extends a finite sequence by zero entries to a specified length. For input lengths $r,s$, their full discrete convolution has length at most $r+s-1$. Padding both inputs to $N\ge r+s-1$ allows circular convolution to represent this full result without folded entries.