Heaviside step function

The Heaviside step function, or the unit step function, usually denoted by Template:Mvar or Template:Mvar (but sometimes Template:Mvar, Template:Math or Template:Math), is a step function named after Oliver Heaviside, the value of which is zero for negative arguments and one for positive arguments. Different conventions concerning the value Template:Math are in use. It is an example of the general class of step functions, all of which can be represented as linear combinations of translations of this one.

The function was originally developed in operational calculus for the solution of differential equations, where it represents a signal that switches on at a specified time and stays switched on indefinitely. Heaviside developed the operational calculus as a tool in the analysis of telegraphic communications and represented the function as Template:Math.

Formulation

Taking the convention that Template:Math, the Heaviside function may be defined as:

• a piecewise function: <math display="block">H(x) := \begin{cases} 1, & x \geq 0 \\ 0, & x < 0 \end{cases}</math>
• using the Iverson bracket notation: <math display="block">H(x) := [x \geq 0]</math>
• an indicator function: <math display="block">H(x) := \mathbf{1}_{x \geq 0}=\mathbf 1_{\mathbb R_+}(x)</math>

For the alternative convention that Template:Math, it may be expressed as:

• a piecewise function: <math display="block">H(x) := \begin{cases} 1, & x > 0 \\ \frac{1}{2}, & x = 0 \\ 0, & x < 0 \end{cases}</math>
• a linear transformation of the sign function, <math display="block">H(x) := \frac{1}{2} \left(\mbox{sgn}\, x + 1\right)</math>
• the arithmetic mean of two Iverson brackets, <math display="block">H(x) := \frac{[x\geq 0] + [x>0]}{2}</math>
• a one-sided limit of the two-argument arctangent <math display="block">H(x) =: \lim_{\epsilon\to0^{+}} \frac{\mbox{atan2}(\epsilon,-x)}{\pi}</math>
• a hyperfunction <math display="block">H(x) =: \left(1-\frac{1}{2\pi i}\log z,\ -\frac{1}{2\pi i}\log z\right)</math> or equivalently <math display="block">H(x) =: \left( -\frac{\log -z}{2\pi i}, -\frac{\log -z}{2\pi i}\right)</math> where Template:Math is the principal value of the complex logarithm of Template:Mvar

Other definitions which are undefined at Template:Math include:

• a piecewise function: <math display="block">H(x) := \begin{cases} 1, & x > 0 \\ 0, & x < 0 \end{cases}</math>
• the derivative of the ramp function: <math display="block">H(x) := \frac{d}{dx} \max \{ x, 0 \}\quad \mbox{for } x \ne 0</math>
• in terms of the absolute value function as

<math display="block"> H(x) = \frac{x + |x|}{2x}</math>

Relationship with Dirac delta

The Dirac delta function is the weak derivative of the Heaviside function: <math display="block">\delta(x)= \frac{d}{dx} H(x).</math> Hence the Heaviside function can be considered to be the integral of the Dirac delta function. This is sometimes written as <math display="block">H(x) := \int_{-\infty}^x \delta(s)\,ds</math> although this expansion may not hold (or even make sense) for Template:Math, depending on which formalism one uses to give meaning to integrals involving Template:Mvar. In this context, the Heaviside function is the cumulative distribution function of a random variable which is almost surely 0. (See Constant random variable.)

Analytic approximations

Approximations to the Heaviside step function are of use in biochemistry and neuroscience, where logistic approximations of step functions (such as the Hill and the Michaelis–Menten equations) may be used to approximate binary cellular switches in response to chemical signals.

File:Step function approximation.png

<math>\tfrac{1}{2} + \tfrac{1}{2} \tanh(kx) = \frac{1}{1+e^{-2kx}}</math>
approaches the step function as Template:Math.

For a smooth approximation to the step function, one can use the logistic function <math display="block">H(x) \approx \tfrac{1}{2} + \tfrac{1}{2}\tanh kx = \frac{1}{1+e^{-2kx}},</math>

where a larger Template:Mvar corresponds to a sharper transition at Template:Math. If we take Template:Math, equality holds in the limit: <math display="block">H(x)=\lim_{k \to \infty}\tfrac{1}{2}(1+\tanh kx)=\lim_{k \to \infty}\frac{1}{1+e^{-2kx}}.</math>

There are many other smooth, analytic approximations to the step function.<ref>{{#invoke:Template wrapper|{{#if:|list|wrap}}|_template=cite web |_exclude=urlname, _debug, id |url = https://mathworld.wolfram.com/{{#if:HeavisideStepFunction%7CHeavisideStepFunction.html}} |title = Heaviside Step Function |author = Weisstein, Eric W. |website = MathWorld |access-date = |ref = Template:SfnRef }}</ref> Among the possibilities are: <math display="block">\begin{align}

H(x) &= \lim_{k \to \infty} \left(\tfrac{1}{2} + \tfrac{1}{\pi}\arctan kx\right)\\
H(x) &= \lim_{k \to \infty}\left(\tfrac{1}{2} + \tfrac12\operatorname{erf} kx\right)

\end{align}</math>

These limits hold pointwise and in the sense of distributions. In general, however, pointwise convergence need not imply distributional convergence, and vice versa distributional convergence need not imply pointwise convergence. (However, if all members of a pointwise convergent sequence of functions are uniformly bounded by some "nice" function, then convergence holds in the sense of distributions too.)

In general, any cumulative distribution function of a continuous probability distribution that is peaked around zero and has a parameter that controls for variance can serve as an approximation, in the limit as the variance approaches zero. For example, all three of the above approximations are cumulative distribution functions of common probability distributions: the logistic, Cauchy and normal distributions, respectively.

Non-Analytic approximations

Approximations to the Heaviside step function could be made through Smooth transition function like <math> 1 \leq m \to \infty </math>: <math display="block">\begin{align}f(x) &= \begin{cases} {\displaystyle \frac{1}{2}\left(1+\tanh\left(m\frac{2x}{1-x^2}\right)\right)}, & |x| < 1 \\ \\ 1, & x \geq 1 \\ 0, & x \leq -1 \end{cases}\end{align}</math>

Integral representations

Often an integral representation of the Heaviside step function is useful: <math display="block">\begin{align}

H(x)&=\lim_{ \varepsilon \to 0^+} -\frac{1}{2\pi i}\int_{-\infty}^\infty \frac{1}{\tau+i\varepsilon} e^{-i x \tau} d\tau \\
&=\lim_{ \varepsilon \to 0^+} \frac{1}{2\pi i}\int_{-\infty}^\infty \frac{1}{\tau-i\varepsilon} e^{i x \tau} d\tau.

\end{align}</math>

where the second representation is easy to deduce from the first, given that the step function is real and thus is its own complex conjugate.

Zero argument

Since Template:Mvar is usually used in integration, and the value of a function at a single point does not affect its integral, it rarely matters what particular value is chosen of Template:Math. Indeed when Template:Mvar is considered as a distribution or an element of Template:Math (see [[Lp space|Template:Math space]]) it does not even make sense to talk of a value at zero, since such objects are only defined almost everywhere. If using some analytic approximation (as in the examples above) then often whatever happens to be the relevant limit at zero is used.

There exist various reasons for choosing a particular value.

• Template:Math is often used since the graph then has rotational symmetry; put another way, Template:Math is then an odd function. In this case the following relation with the sign function holds for all Template:Mvar: <math display="block"> H(x) = \tfrac12(1 + \sgn x).</math>

Also, H(x) + H(-x) = 1 for all x.

• Template:Math is used when Template:Mvar needs to be right-continuous. For instance cumulative distribution functions are usually taken to be right continuous, as are functions integrated against in Lebesgue–Stieltjes integration. In this case Template:Mvar is the indicator function of a closed semi-infinite interval: <math display="block"> H(x) = \mathbf{1}_{[0,\infty)}(x).</math> The corresponding probability distribution is the degenerate distribution.
• Template:Math is used when Template:Mvar needs to be left-continuous. In this case Template:Mvar is an indicator function of an open semi-infinite interval: <math display="block"> H(x) = \mathbf{1}_{(0,\infty)}(x).</math>
• In functional-analysis contexts from optimization and game theory, it is often useful to define the Heaviside function as a set-valued function to preserve the continuity of the limiting functions and ensure the existence of certain solutions. In these cases, the Heaviside function returns a whole interval of possible solutions, Template:Math.

Discrete form

An alternative form of the unit step, defined instead as a function <math>H : \mathbb{Z} \rarr \mathbb{R}</math> (that is, taking in a discrete variable Template:Mvar), is:

<math display="block">H[n]=\begin{cases} 0, & n < 0, \\ 1, & n \ge 0, \end{cases} </math>

or using the half-maximum convention:<ref>Template:Cite book</ref>

<math display="block">H[n]=\begin{cases} 0, & n < 0, \\ \tfrac12, & n = 0,\\ 1, & n > 0, \end{cases} </math>

where Template:Mvar is an integer. If Template:Mvar is an integer, then Template:Math must imply that Template:Math, while Template:Math must imply that the function attains unity at Template:Math. Therefore the "step function" exhibits ramp-like behavior over the domain of Template:Closed-closed, and cannot authentically be a step function, using the half-maximum convention.

Unlike the continuous case, the definition of Template:Math is significant.

The discrete-time unit impulse is the first difference of the discrete-time step

<math display="block"> \delta[n] = H[n] - H[n-1].</math>

This function is the cumulative summation of the Kronecker delta:

<math display="block"> H[n] = \sum_{k=-\infty}^{n} \delta[k] </math>

where

<math display="block"> \delta[k] = \delta_{k,0} </math>

is the discrete unit impulse function.

Antiderivative and derivative

The ramp function is an antiderivative of the Heaviside step function: <math display="block">\int_{-\infty}^{x} H(\xi)\,d\xi = x H(x) = \max\{0,x\} \,.</math>

The distributional derivative of the Heaviside step function is the Dirac delta function: <math display="block"> \frac{d H(x)}{dx} = \delta(x) \,.</math>

Fourier transform

The Fourier transform of the Heaviside step function is a distribution. Using one choice of constants for the definition of the Fourier transform we have <math display="block">\hat{H}(s) = \lim_{N\to\infty}\int^N_{-N} e^{-2\pi i x s} H(x)\,dx = \frac{1}{2} \left( \delta(s) - \frac{i}{\pi} \operatorname{p.v.}\frac{1}{s} \right).</math>

Here Template:Math is the distribution that takes a test function Template:Mvar to the Cauchy principal value of <math>\textstyle\int_{-\infty}^\infty \frac{\varphi(s)}{s} \, ds</math>. The limit appearing in the integral is also taken in the sense of (tempered) distributions.

Unilateral Laplace transform

The Laplace transform of the Heaviside step function is a meromorphic function. Using the unilateral Laplace transform we have: <math display="block">\begin{align}

\hat{H}(s) &= \lim_{N\to\infty}\int^N_{0} e^{-sx} H(x)\,dx\\
&= \lim_{N\to\infty}\int^N_{0} e^{-sx} \,dx\\
&= \frac{1}{s} \end{align}</math>

When the bilateral transform is used, the integral can be split in two parts and the result will be the same.

See also

• Gamma function
• Dirac delta function
• Indicator function
• Iverson bracket
• Laplace transform
• Laplacian of the indicator
• List of mathematical functions
• Macaulay brackets
• Negative number
• Rectangular function
• Sign function
• Sine integral
• Step response

External links

• Digital Library of Mathematical Functions, NIST, [1].
• Template:Cite book
• {{#invoke:citation/CS1|citation

|CitationClass=web }}

• Template:Cite book
• Template:Cite book