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Dirac delta function

The Dirac delta function, denoted \delta(x), is a fundamental generalized function in mathematics and physics that is zero everywhere except at x = 0, where it is conceptually infinite in such a way that its integral over the real line equals 1, serving as an idealized representation of a point impulse or unit mass concentrated at the origin.^[1] This function is not a classical function but a distribution, defined rigorously as a linear functional on spaces of smooth test functions with compact support, where \langle \delta, f \rangle = f(0) for any such test function f.^[1] It possesses key properties, including the sifting or sampling property \int_{-\infty}^{\infty} f(x) \delta(x - a) \, dx = f(a), scaling \delta(ax) = \frac{1}{|a|} \delta(x) for a \neq 0, and differentiation rules like x \delta'(x) = -\delta(x), which extend its utility in integral transforms and differential equations.^[1] Introduced informally by physicist Paul Dirac in 1926 within his development of quantum mechanics, the delta function arose as a tool to handle continuous analogs of discrete sums in transformation theory and to represent sharp discontinuities in wave functions.^[2] Dirac described it with the properties that \delta(\zeta) = 0 for \zeta \neq 0 and \int \delta(\zeta) \, d\zeta = 1 over intervals containing the origin, enabling simplifications in calculations involving non-commuting observables.^[2] Although initially controversial due to its non-standard nature—lacking a well-defined value at zero and violating classical function bounds—its practical value in physics prompted further mathematical scrutiny.^[2] In the late 1940s, Laurent Schwartz established a solid theoretical framework by creating the theory of distributions, defining the Dirac delta as the distributional derivative of the Heaviside step function and integrating it into functional analysis, for which he received the Fields Medal in 1950. The Dirac delta function finds broad applications across disciplines, modeling point sources in physics such as concentrated charges in electrostatics, impulses in mechanics, and delta-correlated noise in stochastic processes. In engineering and signal processing, it represents ideal impulses for convolution operations and Fourier analysis, facilitating the study of system responses to sudden inputs.^[3] In applied mathematics, it appears in Green's functions for solving partial differential equations, such as the Poisson equation for potential theory, and in probability theory to describe Dirac measures or point masses in discrete-continuous hybrids.^[3] Its multidimensional extensions, like \delta^n(\mathbf{x}) = \prod_{i=1}^n \delta(x_i), further enable representations of point particles in higher dimensions, underscoring its enduring role in theoretical and computational modeling.^[1]

Introduction and Motivation

Physical and Mathematical Motivation

The Dirac delta function, denoted \delta(x), is conceptualized as an idealized "spike" at x = 0 that is infinitely narrow and infinitely tall, yet possesses a total integral of unity over the real line, representing a pulse with unit area concentrated at a single point.^[4] This limiting behavior arises from sequences of functions, such as narrow Gaussian pulses, whose width approaches zero while their height increases to maintain the area at 1.^[5] In physical contexts, the Dirac delta function models phenomena involving concentrated effects, such as the unit impulse in signal processing, where it represents an instantaneous signal input that probes a system's response without duration.^[6] Similarly, in mechanics, it describes a point mass distribution, where the mass is idealized as entirely located at a single position, simplifying calculations for gravitational or electrostatic potentials. It also captures instantaneous force applications, like an impulsive "punch" to a mass-spring system at rest, initiating oscillation without sustained input.^[7] Mathematically, the Dirac delta emerges as a tool for handling concentrated sources in differential equations, particularly in deriving Green's functions for equations like Poisson's equation \nabla^2 \phi = -\rho, where \rho is a point charge density modeled by \delta(\mathbf{r}).^[8] This allows solutions to represent responses to idealized point sources, such as electric potentials from discrete charges.^[9] A fundamental property is its normalization:

\int_{-\infty}^{\infty} \delta(x) \, dx = 1,

which ensures the total "strength" is preserved.^[10] Informally, it exhibits a sifting property: for a continuous function f(x),

\int_{-\infty}^{\infty} \delta(x) f(x) \, dx = f(0),

extracting the function's value at the spike's location.^[4] These traits motivate its formal treatment as a measure or distribution in rigorous analysis.

Overview of Key Features

The Dirac delta function, denoted δ(x), is not a classical function in the conventional sense but rather a generalized mathematical object known as a distribution. It cannot be assigned a pointwise value everywhere, as it is zero almost everywhere except at x=0, where it exhibits a singularity, yet it integrates to unity over the real line. This distributional nature allows it to be rigorously defined through its action on test functions, providing a framework for handling idealized point sources or impulses in physical and mathematical contexts.^[11] A defining characteristic of the Dirac delta is its sifting property, which encapsulates its role in selecting values from integrable functions. Specifically, for a suitable continuous function f(x), the integral ∫_{-∞}^{∞} δ(x - a) f(x) dx equals f(a), effectively "sifting out" the function's value at the point a. This property underscores the delta's utility as a sampling tool, enabling precise evaluation without evaluating the function across its entire domain.^[5] The scaling property further highlights the delta function's invariance under transformations that preserve its integral area of 1. For a nonzero constant a, δ(ax) = \frac{1}{|a|} δ(x), which intuitively compresses or stretches the delta while adjusting its "height" inversely to maintain unit area. This ensures dimensional consistency in applications, such as when rescaling variables in integrals involving the delta.^[12] In the context of convolution, the Dirac delta serves as the neutral or identity element, leaving convolved functions unchanged up to a shift. That is, the convolution of f(x) with δ(x - a) yields f(x - a), preserving the original function's form while translating it. This neutrality makes the delta indispensable for analyzing linear systems, where it represents an instantaneous input that reproduces the system's response unaltered.^[5]

Historical Development

Early Conceptualization

The concept of what would later become known as the Dirac delta function first appeared in informal, heuristic forms during the early 19th century in mathematical physics. Notably, Joseph Fourier employed delta-like ideas in 1822 in his work on heat conduction to handle Fourier series expansions, while Augustin-Louis Cauchy used similar notions in 1827 for evaluating integrals via the Cauchy principal value, anticipating the sifting property.^[13] These early uses, though not formalized, proved useful for representing singularities and point effects in continuous media. By the late 19th century, such ideas gained traction in physics and engineering for idealized point sources and instantaneous impulses to model physical phenomena, lacking rigorous justification but aiding solutions to differential equations. Oliver Heaviside independently developed the idea in the 1890s as part of his operational calculus for electromagnetism, where he treated the delta function—denoted symbolically—as the derivative of the unit step function to analyze transient behaviors in electrical systems. Heaviside's framework, detailed in his Electrical Papers (1892) and Electromagnetic Theory (1893–1900), applied this "impulsive" term to solve wave equations and circuit responses, emphasizing its utility in operational manipulations over strict proof. Around 1900, engineers extended these concepts to impulse functions in circuit theory, using them to model sudden voltage or current changes in telegraphic lines and electromagnetic devices, directly building on Heaviside's methods for practical signal analysis.^[14] Paul Dirac popularized the notation in 1927 within quantum mechanics, employing the delta function to represent position eigenstates in continuous spectra, justifying its use heuristically through integral properties without appealing to advanced analysis.^[15] In his paper "The Physical Interpretation of the Quantum Dynamics," Dirac described it as a tool for transforming between position and momentum representations, enabling compact expressions for wave functions and observables.^[15]

Formalization in Distribution Theory

The Dirac delta function, initially introduced informally in physics to model point-like impulses and handle singularities in integral representations, found its mathematical rigorization in the mid-20th century through the emergence of distribution theory, which bridged the gap between heuristic physical applications and pure analysis. This transition addressed longstanding issues in classical calculus, where expressions involving the delta led to apparent contradictions, such as non-zero values under integration despite being "zero everywhere except at one point." By reinterpreting such objects as continuous linear functionals on spaces of smooth test functions, mathematicians resolved these singularities, enabling rigorous treatment of differential equations and Fourier analysis without ad hoc limits or sequences.^[16] Pioneering efforts in this direction began with Sergei Sobolev in the Soviet Union during the 1930s, where he developed the concept of generalized functions specifically to tackle partial differential equations (PDEs) with irregular data. Sobolev's approach, outlined in his 1938 publications, introduced weak or generalized derivatives that allowed solutions to PDEs in a broader sense, incorporating singular terms akin to the Dirac delta without requiring pointwise definitions. This framework was instrumental for applications in wave propagation and potential theory, marking an early step toward formalizing distributions for mathematical physics.^[17] Building on these ideas, George Temple in Britain contributed significantly in the 1930s to the theory of generalized functions, motivated by the need to legitimize the Dirac delta in quantum mechanics and aerodynamics. Temple's work emphasized constructing generalized functions as limits of sequences of ordinary functions, providing a concrete operational framework that avoided the abstract topology later central to full distribution theory. His efforts, culminating in key papers and his 1955 exposition, helped transition the delta from a physical "scandal" to a tool in applied analysis.^[18] The definitive formalization occurred in France with Laurent Schwartz's development of distribution theory during the 1940s, which provided a complete and axiomatic structure for generalized functions. Schwartz defined the Dirac delta δ as the distribution acting on test functions φ (infinitely differentiable with compact support) via the pairing

\langle \delta, \phi \rangle = \phi(0),

transforming singular integrals into well-defined operations on smooth functions and eliminating inconsistencies in classical limits. This innovation, detailed in Schwartz's two-volume "Théorie des distributions" (1950–1951), elevated the delta to a cornerstone of modern analysis, influencing fields from PDEs to signal processing. In the 1950s, M. J. Lighthill further advanced and disseminated generalized function theory, particularly for physicists, through his accessible treatment that integrated Schwartz's distributions with Fourier methods. Lighthill's 1958 monograph highlighted the Dirac delta's role in convolution and transform techniques, making the formal tools practical for resolving singularities in applied problems like acoustics and fluid dynamics.^[19]

Formal Definitions

As a Dirac Measure

In measure theory, the Dirac delta function is rigorously defined as the Dirac measure, a type of Radon measure on a locally compact Hausdorff topological space X. A Radon measure is a Borel measure that is finite on compact sets, outer regular on Borel sets, and inner regular on open sets. The Dirac measure \delta_a at a point a \in X assigns to each Borel set E \subseteq X the value \delta_a(E) = 1 if a \in E and $0 otherwise.^[20] This construction ensures that \delta_a satisfies the axioms of a measure: it is positive (non-negative on all Borel sets), has total mass \delta_a(X) = 1, and is concentrated entirely at the single point a, meaning \delta_a(E) = 0 for any Borel set E not containing a. As a Radon measure, \delta_a is regular, allowing for tight control over approximations by open and compact sets, which is particularly useful in integration theory on topological spaces.^[21]^[20] The integral of a continuous function f: X \to \mathbb{R} with respect to the Dirac measure is given by \int_X f \, d\delta_a = f(a), which follows directly from the measure's point-mass nature and linearity of integration. This property extends to bounded measurable functions under appropriate conditions. In the context of probability theory, the Dirac measure \delta_a represents a degenerate probability distribution, where all probability mass of 1 is assigned to the singleton \{a\}, corresponding to a random variable that takes the value a with probability 1.^[21]^[22]^[23]

As a Distribution

In the theory of distributions, developed by Laurent Schwartz, the Dirac delta function is formalized as a continuous linear functional on the space of test functions \mathcal{C}_c^\infty(\mathbb{R}), consisting of smooth functions with compact support.^[24] The Dirac delta \delta is defined by its action on any test function \phi \in \mathcal{C}_c^\infty(\mathbb{R}) via the pairing notation \langle \delta, \phi \rangle = \phi(0).^[25] This definition captures the intuitive idea of \delta concentrating all its "mass" at the origin while vanishing elsewhere, without requiring \delta to be a classical function. To extend its utility in analysis, particularly for Fourier transforms and applications in partial differential equations, \delta is embedded in the larger space of tempered distributions \mathcal{S}'(\mathbb{R}), which are continuous linear functionals on the Schwartz space \mathcal{S}(\mathbb{R}) of smooth, rapidly decaying functions.^[26] The action remains \langle \delta, \phi \rangle = \phi(0) for all \phi \in \mathcal{S}(\mathbb{R}), ensuring continuity with respect to the Schwartz topology, as the evaluation at zero is bounded on this space.^[25] This extension allows \delta to interact with a broader class of functions, including polynomials and exponentials, while preserving its core properties. The Dirac delta is the unique distribution in \mathcal{D}'(\mathbb{R}) (or \mathcal{S}'(\mathbb{R})) with support contained in the singleton set \{0\} and total mass 1, in the sense that it is the only such object satisfying \langle \delta, \phi \rangle = \phi(0) for test functions.^[27] Distributions with point support at zero are precisely finite-order linear combinations of \delta and its derivatives, isolating \delta as the order-zero case with unit evaluation at the origin.^[28] This uniqueness underscores \delta's role as the canonical representative of impulse-like singularities in distributional theory. For continuous functions, the distributional pairing \langle \delta, f \rangle = f(0) aligns with the measure-theoretic integral against the Dirac measure at zero.^[26]

Generalizations to Other Spaces

The Dirac delta function extends to smooth manifolds by defining it locally via coordinate charts, where it acts as a distribution concentrated at a point p on the manifold M. In a chart (U, \phi) around p, with \phi(p) = 0, the delta distribution \delta_p is given by \langle \delta_p, f \rangle = f(p) for test functions f on M, pulled back to the standard delta in \mathbb{R}^n through the chart map \phi. This construction ensures invariance under diffeomorphisms, as the delta transforms with the Jacobian determinant to preserve the pairing. On Riemannian manifolds (M, g), the delta \delta_p is defined such that \int_M f \delta_p \, d\mu_g = f(p), where d\mu_g is the volume form induced by the metric g. In local coordinates, this corresponds to \delta_p(x) = \delta^n(x - x_p) / \sqrt{|\det g(x_p)|}.^[29] For submanifolds, such as curves \Gamma or surfaces \Sigma embedded in M, the delta is defined by integration over the induced measure: \langle \delta_\Gamma, \phi \rangle = \int_\Gamma \phi \, ds_g, where ds_g is the arc-length element from the metric g, and similarly for higher codimensions using the surface measure. This generalizes the Euclidean case and supports applications in geometry, like Green's functions on curved spaces.^[29] On Lie groups G, the Dirac delta \delta_g at an element g \in G is defined with respect to the Haar measure \mu, a left- (or right-) invariant Radon measure unique up to scaling, such that \langle \delta_g, f \rangle = f(g) for continuous compactly supported functions f: G \to \mathbb{C}. For compact groups, the normalized Haar measure allows the delta to appear in orthogonality relations for irreducible representations, as in \int_G D_{\lambda}(h) \overline{D_{\tilde{\lambda}}(h)} \, d\mu(h) = \delta_{\lambda \tilde{\lambda}}, where D_\lambda are matrix coefficients. In non-compact cases, the modular function \Delta_G adjusts the measure for right invariance, ensuring \delta_g(h) = \delta(h^{-1}g) / \Delta_G(g). This framework enables Fourier analysis on groups, with the delta serving as the identity for convolution algebras.^[30] In infinite-dimensional spaces, such as the dual S'(\mathbb{R}^d) of the Schwartz space or Banach spaces of functions, the Dirac delta arises as a distribution rather than a measure, defined by evaluation: \langle \delta_x, f \rangle = f(x) for x in the space, provided the test functions are sufficiently smooth. Gaussian measures on these spaces, characterized by their mean and covariance operator, interact with deltas in the sense that the support of a Gaussian measure \mu on S' often includes generalized functions like deltas, as per the Bochner-Minlos theorem, which guarantees the existence of measures whose Fourier transforms match those of finite-dimensional Gaussians. For instance, the white noise measure has covariance involving the delta, \mathbb{E}[\xi(x)\xi(y)] = \delta(x-y), enabling probabilistic constructions in quantum field theory and stochastic PDEs. However, true Dirac measures (point masses) fail to be \sigma-additive in infinite dimensions without additional structure, like cylinder sets.^[31] In the complex plane \mathbb{C}, the Dirac delta \delta(z) at z=0 is defined distributionally by \langle \delta, f \rangle = f(0) for smooth test functions f, with extensions to holomorphic test functions in generalized frameworks. Using Colombeau algebras or non-Archimedean extensions \rho \mathbb{C} \supset \mathbb{C}, \delta becomes a generalized holomorphic function (GHF) via nets of holomorphic mollifiers converging in the \rho \mathbb{C}-topology, satisfying Cauchy-Riemann equations in a distributional sense. This allows derivatives: \partial \delta / \partial \bar{z} = 0 formally, embedding \delta into the sheaf of GHFs.^[32] In several complex variables, on \mathbb{C}^n, the delta \delta_z at z \in \mathbb{C}^n generalizes to a current of integration over the point, acting as \langle \delta_z, \phi \rangle = \phi(z) for smooth test functions \phi, defined as an (n,n)-current supported at z. Holomorphic extensions treat \delta as a principal value or via convolution with approximate identities preserving pluriharmonicity, as in global representatives of Colombeau holomorphic generalized functions, enabling products and compositions in complex geometry.^[33]

Fundamental Properties

Scaling, Symmetry, and Translation

The Dirac delta distribution exhibits key transformation properties under scaling, symmetry, and translation, which are fundamental to its application in integral expressions and change of variables. These properties arise from the defining sifting action of the delta distribution on test functions and ensure consistency under affine transformations. The scaling property states that for any nonzero scalar a \in \mathbb{R},

\delta(ax) = \frac{1}{|a|} \delta(x).

This can be derived from the sifting property \int_{-\infty}^{\infty} \delta(x) f(x) \, dx = f(0) for a smooth test function f with compact support. Consider the action \int_{-\infty}^{\infty} \delta(ax) f(x) \, dx. Substitute u = ax, so dx = du / a if a > 0, yielding \frac{1}{a} \int_{-\infty}^{\infty} \delta(u) f(u/a) \, du = f(0)/a. For a < 0, the substitution reverses the integration limits, introducing an additional factor of -1, but the absolute value |a| accounts for both cases, giving \int_{-\infty}^{\infty} \delta(ax) f(x) \, dx = f(0)/|a|, which matches the action of \frac{1}{|a|} \delta(x).^[34] Symmetry follows as a special case of scaling with a = -1, since |-1| = 1, implying

\delta(-x) = \delta(x).

Thus, the Dirac delta is an even distribution. This evenness is preserved under the distributional definition, as the substitution u = -x in the sifting integral yields the same result due to the absolute value in the scaling factor.^[34] The translation property defines the shifted delta as \delta(x - b) for b \in \mathbb{R}, satisfying \int_{-\infty}^{\infty} \delta(x - b) f(x) \, dx = f(b). This follows directly from the sifting property by substituting u = x - b, so dx = du and the integral becomes \int_{-\infty}^{\infty} \delta(u) f(u + b) \, du = f(b), confirming the shift preserves the unit mass at the new point b. This property underpins the delta's role as a localization tool in integrals.^[34] For composition with a smooth invertible function g: \mathbb{R} \to \mathbb{R} such that g(0) = 0 and g'(0) \neq 0,

\delta(g(x)) = \frac{\delta(x)}{|g'(0)|}.

More generally, if g has simple zeros at points x_i where g(x_i) = 0 and g'(x_i) \neq 0, then

\delta(g(x)) = \sum_i \frac{\delta(x - x_i)}{|g'(x_i)|}.

This is proved by considering the sifting action \int_{-\infty}^{\infty} \delta(g(x)) f(x) \, dx. Near each zero x_i, a local change of variables u = g(x) with Jacobian du = g'(x) dx yields contributions f(x_i)/|g'(x_i)| from each root, summing over all simple zeros while assuming no overlap in supports. The absolute value ensures positivity and accounts for the orientation of the mapping.^[35]

Algebraic and Integral Properties

The Dirac delta function, denoted \delta, is a fundamental distribution in the theory of distributions developed by Laurent Schwartz, where it serves as the neutral element for convolution and exhibits specific algebraic behaviors when combined with other distributions or smooth functions.^[7] As a distribution, \delta is defined by its action on test functions \phi \in C_c^\infty(\mathbb{R}) via \langle \delta, \phi \rangle = \phi(0), and this definition extends naturally to linear combinations.^[36] Linearity is a core property of distributions, allowing scalar multiples and sums to act straightforwardly on test functions. For constants a, b \in \mathbb{R} and the derivative distribution \delta', the combination a\delta + b\delta' satisfies \langle a\delta + b\delta', \phi \rangle = a \phi(0) - b \phi'(0), reflecting the linear nature of the dual space pairing.^[36] This property ensures that \delta behaves algebraically like a linear functional, enabling its use in linear superpositions within partial differential equations and signal processing.^[37] Convolution with the Dirac delta preserves functions in the distributional sense, acting as the identity element. For a smooth function f, the convolution \delta * f = f, defined by \langle \delta * f, \phi \rangle = \langle f, \delta * \phi \rangle where \delta * \phi (x) = \phi(-x), yields the sifting property extended to broader classes.^[7] Classically, as an improper function, the pointwise product \delta^2 is undefined, but the self-convolution \delta * \delta = \delta holds in distribution theory since \delta is the convolution unit and the operation is associative for compactly supported distributions.^[36] Multiplication of \delta by smooth functions is well-defined in distributions, but specific cases reveal algebraic constraints. For the identity function, x \delta(x) = 0, because \langle x \delta, \phi \rangle = 0 \cdot \phi(0) = 0 for all test functions \phi, indicating that the support at zero annihilates the linear factor.^[37] This property generalizes to \langle g \delta, \phi \rangle = g(0) \phi(0) for smooth g, highlighting \delta's localized nature.^[36] The indefinite integral of \delta connects it to the Heaviside step function H(x), defined as H(x) = 0 for x < 0 and H(x) = 1 for x \geq 0. In the distributional sense, H(x) = \int_{-\infty}^x \delta(t) \, dt, and the derivative satisfies H' = \delta, verified by \langle H', \phi \rangle = -\langle H, \phi' \rangle = -\int_0^\infty \phi'(x) \, dx = \phi(0) = \langle \delta, \phi \rangle.^[7] This relation underscores the integral properties of \delta as a generalized derivative.^[37]

Composition and Indefinite Integrals

The composition of the Dirac delta distribution with a smooth function f(x) having simple zeros at points x_i (where f(x_i) = 0 and f'(x_i) \neq 0) is given by

\delta(f(x)) = \sum_i \frac{\delta(x - x_i)}{|f'(x_i)|},

where the sum is over all such zeros in the domain of interest.^[37] This formula arises from the change of variables in the defining integral property of the delta distribution, ensuring that the composition acts as a sum of deltas weighted by the inverse of the absolute derivative at each root to preserve the unit integral.^[38] This composition preserves the support of the delta distribution, concentrating it solely at the roots of f(x) = 0, as the delta vanishes elsewhere and the transformation accounts for the local behavior near those points. For example, consider f(x) = x^2 - 1, with simple zeros at x = \pm 1 where f'(x) = 2x yields |f'(\pm 1)| = 2; thus,

\delta(x^2 - 1) = \frac{1}{2} \left[ \delta(x - 1) + \delta(x + 1) \right].

^[37] The indefinite integral of the Dirac delta distribution is the Heaviside step function H(x), defined such that H(x) = 0 for x < 0 and H(x) = 1 for x > 0, satisfying the distributional derivative relation H'(x) = \delta(x).^[39] In this framework, the product H(x) \delta(x) requires careful convention for the value at the origin; when H(0) = 1/2 to ensure symmetry in Fourier analysis, it yields H(x) \delta(x) = \frac{1}{2} \delta(x), though some contexts define H(0) = 1 leading to H(x) \delta(x) = \delta(x).^[40] The rigorous identification H' = \delta holds regardless, as the delta emerges from the jump discontinuity of H(x).^[39]

Multidimensional Extensions

Properties in n Dimensions

In n-dimensional Euclidean space \mathbb{R}^n, the Dirac delta function is defined as the product of n one-dimensional delta functions: \delta^{(n)}(\mathbf{x}) = \prod_{i=1}^n \delta(x_i), where \mathbf{x} = (x_1, \dots, x_n). This construction leverages the separability in Cartesian coordinates, ensuring the normalization property \int_{\mathbb{R}^n} \delta^{(n)}(\mathbf{x}) \, d^n x = 1. The sifting property extends accordingly: for a continuous test function f(\mathbf{x}), \int_{\mathbb{R}^n} f(\mathbf{x}) \delta^{(n)}(\mathbf{x} - \mathbf{a}) \, d^n x = f(\mathbf{a}), localizing the integral to the point \mathbf{a}.^[34] The scaling property under linear transformations follows from the change-of-variables formula in multiple integrals. For an invertible n \times n matrix A, \delta^{(n)}(A \mathbf{x}) = \frac{1}{|\det A|} \delta^{(n)}(\mathbf{x}), which preserves the unit integral by compensating for the volume scaling factor |\det A|. This generalizes the one-dimensional scaling \delta(kx) = \frac{1}{|k|} \delta(x) and is essential for coordinate changes in multivariable contexts.^[35] The Dirac delta exhibits rotational invariance due to its point-like concentration at the origin, independent of direction. Specifically, for any orthogonal matrix R (satisfying R^T R = I and \det R = \pm 1), \delta^{(n)}(R \mathbf{x}) = \delta^{(n)}(\mathbf{x}), underscoring its scalar nature under rotations and reflections.^[35] Surface deltas arise when concentrating the distribution on a hypersurface defined by a level set g(\mathbf{x}) = 0, where g: \mathbb{R}^n \to \mathbb{R} is smooth with non-vanishing gradient \nabla g on the surface. The composition \delta(g(\mathbf{x})) satisfies \int_{\mathbb{R}^n} f(\mathbf{x}) \delta(g(\mathbf{x})) \, d^n x = \int_S \frac{f(\mathbf{s})}{|\nabla g(\mathbf{s})|} \, d\sigma(\mathbf{s}), where S = \{\mathbf{x} \mid g(\mathbf{x}) = 0\} and d\sigma is the induced surface measure. If the zeros of g are isolated simple points \mathbf{x}_i, this reduces to a sum \sum_i \frac{f(\mathbf{x}_i)}{|\nabla g(\mathbf{x}_i)|}. To represent the pure surface delta \delta_S such that \int \delta_S f \, d^n x = \int_S f \, d\sigma, one uses \delta_S(\mathbf{x}) = \delta(g(\mathbf{x})) |\nabla g(\mathbf{x})|. This formulation is crucial for modeling singularities on lower-dimensional manifolds in n dimensions.^[41]

Derivatives in Higher Dimensions

In the theory of distributions on \mathbb{R}^n, the partial derivative \partial_i \delta of the Dirac delta distribution \delta with respect to the i-th coordinate is defined in the weak sense by its action on a test function \phi \in \mathcal{D}(\mathbb{R}^n) as \langle \partial_i \delta, \phi \rangle = -\partial_i \phi(0).^[36] This definition arises from the general rule for distributional derivatives, where the derivative of a distribution T satisfies \langle D T, \phi \rangle = (-1)^{|D|} \langle T, D \phi \rangle for a differential operator D of order |D|.^[36] Higher-order derivatives, including mixed partials D^\alpha \delta for a multi-index \alpha = (\alpha_1, \dots, \alpha_n) with |\alpha| = \sum_k \alpha_k, are given by \langle D^\alpha \delta, \phi \rangle = (-1)^{|\alpha|} D^\alpha \phi(0).^[36] All such derivatives \delta'^{(n)} in n dimensions retain their support at the origin, meaning \langle D^\alpha \delta, \phi \rangle = 0 whenever \phi vanishes to order |\alpha| at the origin along with its first |\alpha|-1 derivatives.^[36] The Laplacian \Delta \delta = \sum_{i=1}^n \partial_{ii} \delta represents a specific second-order derivative, with \langle \Delta \delta, \phi \rangle = \sum_{i=1}^n \partial_{ii} \phi(0).^[36] This distribution \Delta \delta serves as a concentrated source term in partial differential equations, particularly in the context of the Poisson equation \Delta u = f, where higher derivatives of \delta model point-like singularities or impulses.^[42] For instance, in electrostatics and potential theory, \Delta \delta relates to the inhomogeneity driving solutions with singular behavior at a point.^[43] Regarding symmetry, the Dirac delta \delta itself is an even distribution, satisfying \langle \delta, \phi(-x) \rangle = \langle \delta, \phi(x) \rangle for test functions \phi.^[44] Its derivatives inherit parity based on the order: even-order derivatives like \partial_{ii} \delta or \Delta \delta are even distributions, while odd-order ones, such as \partial_i \delta, are odd in the i-th variable, meaning \langle \partial_i \delta, \phi(-x) \rangle = -\langle \partial_i \delta, \phi(x) \rangle when flipping the sign in the i-th coordinate.^[44] This even-odd nature follows from the parity of the test function derivatives in the defining pairing and holds for mixed partials according to the total order |\alpha|.^[44]

Representations and Approximations

Sequence Approximations

Sequence approximations, also known as delta sequences or approximations to the identity, provide a means to represent the Dirac delta distribution as the limit of ordinary functions that become increasingly concentrated at the origin while maintaining unit integral. These sequences are particularly useful for computational purposes and for developing intuition about the delta's sifting property, where the integral against a test function f approaches f(0). In the theory of distributions, such sequences converge weakly to the delta distribution as the approximating parameter tends to its limit.^[45] A prominent example is the Gaussian approximation, given by

\delta_\sigma(x) = \frac{1}{\sigma \sqrt{2\pi}} \exp\left( -\frac{x^2}{2\sigma^2} \right),

where \sigma > 0 is the standard deviation. As \sigma \to 0, the function narrows with variance approaching zero, peaking sharply at x = 0 while preserving the total area of 1. This limit yields the Dirac delta in the distributional sense.^[1] Another common sequence is the rectangular pulse, defined as

\delta_\epsilon(x) = \frac{1}{\epsilon} \rect\left( \frac{x}{\epsilon} \right),

for \epsilon > 0, where the rect function is 1 for |x| < 1/2 and 0 otherwise. This produces a uniform pulse of width \epsilon and height $1/\epsilon, with integral 1. As \epsilon \to 0, the pulse collapses to the origin, approximating the delta distribution.^[46] The convergence of these sequences to the Dirac delta is understood in the sense of distributions: for a smooth test function f with compact support,

\lim_{\sigma \to 0} \int_{-\infty}^{\infty} f(x) \delta_\sigma(x) \, dx = f(0),

and similarly for the rectangular case as \epsilon \to 0. This property holds for any delta sequence satisfying the conditions of non-negativity, unit integral, and concentration at the origin. Such approximations are foundational in analysis for proving properties of convolutions and integrals involving the delta.^[1]^[45]

Integral Representations

The Dirac delta function possesses several integral representations that facilitate its use in analytical contexts, such as solving differential equations or performing transform calculations. These representations often involve limits to ensure convergence and are interpreted distributionally. A primary example is the Fourier integral representation, which expresses the delta function as a superposition of plane waves:

\delta(x) = \frac{1}{2\pi} \int_{-\infty}^{\infty} e^{i k x} \, dk.

This form emerges from the inverse Fourier transform of the constant function 1 and holds in the distributional sense, where the integral may be regularized by finite limits or damping factors.^[47] An oscillatory variant arises by considering the real part or cosine transform with a convergence factor, leading to representations like

\delta(x) = \lim_{\epsilon \to 0^+} \frac{1}{2\pi} \int_{-\infty}^{\infty} e^{i k x} e^{-\epsilon |k|} \, dk,

which equals \frac{\epsilon}{\pi (x^2 + \epsilon^2)} for \epsilon > 0 and converges to the delta function as \epsilon \to 0^+. This is known as the Poisson kernel representation in the context of the upper half-plane, where the imaginary part of the complex function $1/(x + i \epsilon) yields the kernel \frac{1}{\pi} \frac{\epsilon}{x^2 + \epsilon^2}.^[48] In multiple dimensions, the heat kernel provides another representation, derived from the fundamental solution to the heat equation. For the one-dimensional case,

\delta(x) = \lim_{t \to 0^+} \frac{1}{\sqrt{4 \pi t}} \exp\left( -\frac{x^2}{4 t} \right),

and in n dimensions,

\delta^{(n)}(\mathbf{x}) = \lim_{t \to 0^+} \frac{1}{(4 \pi t)^{n/2}} \exp\left( -\frac{|\mathbf{x}|^2}{4 t} \right).

This limit captures the diffusive spreading that concentrates at the origin as time approaches zero, serving as a smooth approximation useful in partial differential equations.^[49]

Fourier and Other Transforms

The Fourier transform of the Dirac delta function reveals its fundamental role in signal processing and harmonic analysis. Under the convention where the Fourier transform is defined as \mathcal{F}\{f\}(\omega) = \int_{-\infty}^{\infty} f(x) e^{-2\pi i \omega x} \, dx, the transform of \delta(x) evaluates to 1, as the delta function sifts the exponential at x = 0, yielding e^{0} = 1.^[50] For a shifted delta \delta(x - x_0), the result is e^{-2\pi i \omega x_0}, reflecting the translation property of the Fourier transform where spatial shifts correspond to phase multiplications in the frequency domain.^[50] Conversely, the inverse Fourier transform of the constant function 1 (normalized appropriately) recovers the delta function, underscoring their duality: \mathcal{F}^{-1}\{1\}(\omega) = \delta(x). This pair highlights the delta's concentration in space as uniform spread in frequency.^[50] A key property emerges from the eigenfunction expansion of the translation operator. Plane waves e^{i k x} serve as eigenfunctions of the translation operator T_{x_0} f(x) = f(x - x_0), with eigenvalues e^{-i k x_0}, and their completeness relation integrates to the delta function: \delta(x - x_0) = \frac{1}{2\pi} \int_{-\infty}^{\infty} e^{i k (x - x_0)} \, dk. Thus, the delta function admits an expansion in these translation eigenfunctions, positioning it as a reproducing kernel in the frequency domain.^[51] The Laplace transform of the Dirac delta further simplifies in unilateral contexts, such as control theory and differential equations. Defined as \mathcal{L}\{f\}(s) = \int_{0}^{\infty} f(t) e^{-s t} \, dt for \operatorname{Re}(s) > 0, the transform of \delta(t) is 1, obtained by sifting the exponential at t = [0](/page/0). For a delayed impulse \delta(t - a) with a > 0, it becomes e^{-a s}, encoding the timing shift directly in the s-domain.^[52] In multidimensional settings, particularly for radially symmetric functions, the Hankel transform extends these ideas to cylindrical or spherical coordinates. The Hankel transform of order \nu of a radial delta \delta(r - \mu) (with \mu > 0) is \mu J_{\nu}(\lambda \mu), where J_{\nu} is the Bessel function of the first kind; this follows from the sifting property within the integral \int_{0}^{\infty} r J_{\nu}(\lambda r) \delta(r - \mu) \, dr = \mu J_{\nu}(\lambda \mu), often normalized by the radial measure. In n-dimensions, the Fourier transform of the n-dimensional delta \delta^{(n)}(\mathbf{x}) is 1 (up to a (2\pi)^{n/2} factor depending on convention), and for radial cases, it reduces to a Hankel transform of order (n/2 - 1), facilitating solutions to wave equations and diffusion in higher dimensions.^[53]

Advanced Topics

Dirac Comb and Periodic Extensions

The Dirac comb, also known as the Shah function or impulse train, is a periodic distribution defined as

\III(x) = \sum_{k=-\infty}^{\infty} \delta(x - k),

where \delta denotes the Dirac delta function. This infinite summation places delta functions at every integer point on the real line, creating a periodic structure with period 1. In more general contexts, a scaled version appears as

\sum_{n=-\infty}^{\infty} \frac{\delta(x - nT)}{T},

for a period T > 0, which normalizes the distribution such that its integral over any interval of length T equals 1. This scaling ensures that the comb acts as a unit-density train of impulses, facilitating its use in modeling periodic phenomena.^[54]^[55] A key property of the Dirac comb arises from the Poisson summation formula, which relates the summation of a function over a lattice to the summation of its Fourier transform over the dual lattice. For the unit-period comb \III(x), the formula implies that its Fourier transform is proportional to itself:

\mathcal{F}\{\III(x)\}(\xi) = \III(\xi),

up to a constant factor depending on the Fourier transform convention (often $2\pi or 1). This self-duality underscores the comb's role as an eigenfunction of the Fourier transform operator, highlighting its symmetry in both time and frequency domains. The result follows directly from applying the Poisson formula to the indicator function of the unit interval, whose Fourier series yields the comb.^[56]^[57] In signal processing, the Dirac comb models ideal uniform sampling through multiplication of a continuous signal by the scaled impulse train, producing a discrete-time representation. The Poisson summation formula then explains the frequency-domain effect: the spectrum of the sampled signal becomes a periodic repetition of the original spectrum, scaled by the sampling rate, which is central to the Nyquist-Shannon sampling theorem. This periodization in the frequency domain ensures perfect reconstruction if the sampling rate exceeds twice the signal's bandwidth, preventing aliasing. The Shah function's notation emphasizes this connection, with its self-Fourier property directly linking sampling intervals to spectral repetitions.^[58]^[55] The periodization aspect of the Dirac comb extends to functions beyond deltas; convolving a function with \III(x) yields its periodic extension with period 1, preserving the integral over each period equal to the original function's total integral (normalized appropriately). This property leverages the translation invariance of the delta, adapted to the comb's periodicity.^[56]

Sokhotski–Plemelj Theorem

The Sokhotski–Plemelj theorem provides a fundamental relation between the Dirac delta function and the Cauchy principal value in the context of limits involving complex variables. Specifically, it states that

\lim_{\epsilon \to 0^+} \frac{1}{x \pm i \epsilon} = \mathcal{P} \left( \frac{1}{x} \right) \mp i \pi \delta(x),

where \mathcal{P} denotes the Cauchy principal value and \delta(x) is the Dirac delta distribution.^[59] This formula expresses the distributional limit of a function with a small imaginary part, decomposing it into a singular real part and an imaginary part involving the delta function. The theorem originated with Yulian Vasil'evich Sokhotski, who introduced the key formulas in his 1873 doctoral thesis while studying the boundary behavior of Cauchy integrals.^[60] Josip Plemelj later provided more complete proofs and generalizations in 1908, particularly in the framework of Riemann-Hilbert boundary value problems, leading to the combined attribution.^[61] A standard proof sketch relies on contour integration in the complex plane. Consider the function f(z) = 1/z and integrate along a contour that avoids the real axis singularity at z = 0 by a small semicircular indentation of radius \epsilon above or below the axis. For the upper half-plane limit (+ i \epsilon), closing the contour in the upper half-plane yields no poles inside, so the integral vanishes as \epsilon \to 0, but the indentation contributes + i \pi times the residue at 0, which is 1, leading to the - i \pi \delta(x) term in the distributional sense. The real part corresponds to the principal value integral \mathcal{P} \int_{-\infty}^{\infty} dx'/ (x - x'), symmetrized over the singularity. The lower half-plane case follows analogously with opposite signs.^[62] This theorem has significant implications for the Hilbert transform, defined as \mathcal{H}f(x) = \frac{1}{\pi} \mathcal{P} \int_{-\infty}^{\infty} \frac{f(t)}{x - t} dt, which appears as the real part in the decomposition. The imaginary delta term ensures the transform's properties in Fourier space, where \mathcal{H} corresponds to multiplication by -i \operatorname{sgn}(k), facilitating analytic continuations and boundary value analyses in harmonic analysis.^[63]

Relation to Kronecker Delta

The Kronecker delta, denoted \delta_{ij}, is defined for discrete indices i and j as \delta_{ij} = 1 if i = j and \delta_{ij} = 0 otherwise.^[64] It serves as the discrete analog of the identity operator, extracting the j-th component from a vector or sum, such as \sum_i f_i \delta_{ij} = f_j.^[65] In contrast, the Dirac delta function \delta(x - y) acts in the continuous domain, where it functions as the kernel of the identity operator in function space, satisfying \int_{-\infty}^{\infty} f(x) \delta(x - y) \, dx = f(y).^[66] This sifting property parallels the Kronecker's selection in sums, highlighting their analogous roles in discrete versus continuous settings.^[65] The connection arises in the continuum limit: consider a discrete sum over a fine grid with spacing \Delta x \to 0, where \sum_n f(n \Delta x) \delta_{n m} \Delta x \to \int_{-\infty}^{\infty} f(x) \delta(x - m \Delta x) \, dx = f(m \Delta x).^[66] This transition underscores how the Dirac delta emerges as the continuous counterpart to the Kronecker delta, normalizing the discrete identity to preserve the sifting behavior under integration.^[64] In the context of Fourier series and transforms, the relation manifests through orthogonal bases: discrete Fourier series coefficients involve Kronecker deltas for orthogonality over finite points, while the continuous Fourier transform uses Dirac deltas to express completeness of the exponential basis, such as \int_{-\infty}^{\infty} e^{i 2\pi (k - k') x} \, dx = \delta(k - k').^[65] From a linear algebraic perspective, the Kronecker delta corresponds to the identity matrix in a discrete basis, where its elements form the matrix \mathbf{I} with I_{ij} = \delta_{ij}.^[64] Similarly, the Dirac delta represents the continuous identity operator, acting as an integral kernel that reproduces functions unchanged, bridging discrete matrix multiplication to continuous integral transforms.^[66]

Applications

In Probability and Statistics

In probability theory, the Dirac delta function provides a formal representation for the probability density function of a degenerate random variable, which takes a fixed value a with probability 1, denoted as P(X = a) = 1. Thus, the density is given by f_X(x) = \delta(x - a), where the integral \int_{-\infty}^{\infty} f_X(x) \, dx = 1 holds in the sense of distributions. This usage extends the concept of densities to discrete cases, allowing unified treatment of continuous and discrete distributions via generalized functions.^[67]^[68] The characteristic function of this degenerate distribution is \phi_X(t) = \mathbb{E}[e^{itX}] = e^{ita}, which follows directly from the sifting property of the delta function: \int_{-\infty}^{\infty} e^{itx} \delta(x - a) \, dx = e^{ita}. This confirms the distribution's concentration at a, as the characteristic function matches that of a deterministic constant.^[68] Regarding moments, the mean is \mathbb{E}[X] = a, while all higher-order central moments \mathbb{E}[(X - a)^k] for k \geq 2 are zero, since X = a almost surely and there is no dispersion. The variance, as the second central moment, is thus zero, underscoring the non-random nature of the variable.^[67] In stochastic processes, the Dirac delta function arises in the formal density representation of white noise, a generalized process with covariance \mathbb{E}[\dot{W}(t) \dot{W}(s)] = \delta(t - s), where \dot{W} denotes the derivative of the Wiener process; this framework facilitates stochastic integrals like \int g(t) \, dW(t). The Dirac measure, as the point mass at a, underlies the degenerate distribution in measure-theoretic probability.

In Quantum Mechanics and Physics

In quantum mechanics, the position operator \hat{x} has a continuous spectrum of eigenvalues x \in \mathbb{R}, with corresponding eigenstates denoted |x\rangle satisfying \hat{x} |x\rangle = x |x\rangle. These eigenstates form a complete basis for the Hilbert space of square-integrable functions, and their orthonormality is expressed through the Dirac delta function as \langle x | x' \rangle = \delta(x - x'). This relation encodes the fact that position eigenstates are mutually orthogonal and normalized in the distributional sense, allowing the expansion of any state |\psi\rangle as \psi(x) = \langle x | \psi \rangle = \int dx' \langle x | x' \rangle \langle x' | \psi \rangle, which relies on the completeness \int dx |x\rangle \langle x | = \hat{1}. The bras \langle x| and kets |x\rangle facilitate the Dirac notation for transitioning between position and abstract Hilbert space representations, essential for formulating observables and dynamics. A prominent application arises in the study of delta function potentials, where the potential is modeled as V(x) = -g \delta(x) with g > 0 for an attractive case, appearing in the time-independent Schrödinger equation -\frac{\hbar^2}{2m} \frac{d^2 \psi}{dx^2} - g \delta(x) \psi = E \psi. For bound states with E < 0, the solution requires integrating across the origin to handle the singularity, yielding a single bound state with energy E = -\frac{m g^2}{2 \hbar^2} and even wavefunction \psi(x) = \sqrt{\kappa} e^{-\kappa |x|}, where \kappa = \frac{m g}{\hbar^2}. This state is normalizable and decays exponentially away from the origin, demonstrating how the delta potential supports exactly one bound state despite its zero width, a feature that simplifies modeling of short-range interactions like in atomic physics or solid-state defects. The bound state energy scales quadratically with the potential strength g, highlighting the delta function's role in capturing infinite binding for infinitesimal range. In classical physics, the Dirac delta function models point sources in linear partial differential equations, particularly through Green's functions. For the one-dimensional wave equation \frac{\partial^2 u}{\partial t^2} - c^2 \frac{\partial^2 u}{\partial x^2} = f(x,t), the Green's function G(x,t; x', t') satisfies \left( \frac{\partial^2}{\partial t^2} - c^2 \frac{\partial^2}{\partial x^2} \right) G = \delta(x - x') \delta(t - t'), representing the disturbance from an impulsive source at (x', t'). The retarded Green's function, for instance, propagates causally as G(x,t; 0,0) = \frac{1}{2c} \Theta(c(t) - |x|), where \Theta is the Heaviside step function, solving initial value problems for waves emanating from a delta-like excitation. This approach extends to higher dimensions and other equations like Poisson's, underscoring the delta's utility in describing localized forces or charges in electrostatics and acoustics.^[69] The use of the delta function in position eigenstates also illustrates the Heisenberg uncertainty principle. A state localized as \psi(x) = \delta(x - x_0) has zero position uncertainty \Delta x = 0, but its momentum representation \tilde{\psi}(p) = \frac{1}{\sqrt{2\pi \hbar}} e^{-i p x_0 / \hbar} is uniform, yielding infinite momentum uncertainty \Delta p = \infty, saturating the relation \Delta x \Delta p \geq \hbar/2 in the limit. This flat momentum distribution arises because the Fourier transform of the delta function is a constant (up to normalization), emphasizing that perfect position knowledge precludes any momentum information, a foundational limit in quantum theory.^[70]

In Engineering and Other Fields

In signal processing, the Dirac delta function models an idealized impulse input to linear time-invariant systems, where the system's response to this input defines its impulse response h(t). The output y(t) of such a system to an arbitrary input x(t) is then given by the convolution integral y(t) = \int_{-\infty}^{\infty} x(\tau) h(t - \tau) \, d\tau, which simplifies to y(t) = x(t) * h(t) when x(t) = \delta(t). This approach enables the analysis and design of filters and communication systems by characterizing their behavior through impulse responses.^[6]^[71] In structural mechanics, the Dirac delta function represents concentrated point loads on beams and other elements, allowing the formulation of governing differential equations for deflections and stresses. For a beam under a point load P at position x_0, the distributed load is expressed as q(x) = P \delta(x - x_0), and the deflection is obtained by integrating the beam equation EI \frac{d^4 w}{dx^4} = q(x), where E is the modulus of elasticity and I is the moment of inertia. This method simplifies the treatment of singularities in static and dynamic analyses of structures like bridges and frames.^[72]^[73]^[74] In control theory, the Dirac delta function models impulsive controls, which apply instantaneous changes to system states, such as sudden thrusts in spacecraft maneuvers or corrective pulses in robotic systems. These controls are represented as u(t) = \sum_k c_k \delta(t - t_k), where c_k is the impulse magnitude at time t_k, enabling the analysis of hybrid systems and optimal control problems through jump conditions in differential equations.^[75]^[76]^[77] Beyond these areas, the Dirac delta function appears in electromagnetism to model point charges, where the charge density is \rho(\mathbf{r}) = q \delta(\mathbf{r} - \mathbf{r}_0), facilitating the computation of electric fields via Gauss's law in integral form. In computer graphics, it idealizes point sampling in rendering algorithms, such as selecting pixel values at exact locations during image reconstruction, though practical implementations often use smoothed approximations to avoid aliasing.^[78]^[79]^[80]^[81]

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[PDF] All about the Dirac delta function(?) - IQTI IISc
It said (in its entirety): O. Heaviside (1893-95), G. Kirchhoff (1891), P. A. M. Dirac (1926), L. Schwartz (1945). A ...
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Dirac Delta Function | Generalized PDF - Probability Course
In this section, we will use the delta function to extend the definition of the PDF to discrete and mixed random variables.Missing: composition | Show results with:composition<|control11|><|separator|>
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[PDF] Lecture 3
Probability distribution function (or cumulative probability ... Using the Dirac delta function δ( ) = 0, x ≠ 0 x ... Characteristic function and inverse for ...
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[PDF] Green's Functions in Physics Version 1 - faculty.washington.edu
May 25, 2025 · ... Dirac Delta Function . . . . . . . . . . . . . . . . . 24. 3.3 ... wave equation, the heat equation, and the scattering problem. Many ...
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The Uncertainty Principle (Stanford Encyclopedia of Philosophy)
Oct 8, 2001 · The uncertainty principle (for position and momentum) states that one cannot assign exact simultaneous values to the position and momentum of a physical system.
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[PDF] 2.161 Signal Processing: Continuous and Discrete
0 for t>T. The Dirac delta function (also known as the impulse function) can be defined as the limiting form of the unit pulse δT (t) as the duration T ...
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[PDF] Lecture 5: Solution Method for Beam Deflections
The concentrated load P can be treated as a special case of the distributed load q(x) = Pδ(x - x0), where δ is the Dirac delta function.
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[PDF] A First Course on Variational Methods in Structural Mechanics and ...
axial displacement as a function of x. Note that there is a distributed load and a point load. The point load should be modeled using a Dirac delta function.
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[PDF] Physically-based Dirac's deltas functions in the static analysis of multi
Jan 7, 2011 · Dirac's delta functions enable simple and effective representations of point loads and singularities in a variety of structural prob- lems, ...
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[PDF] Impulsive control for the short time convergence of the second order ...
Each impulsive control is defined as a linear combination of Dirac delta function and its derivative, and practically implementable in a form of piecewise ...
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[PDF] Exponential Stability Results On Fractional Order Impulsive Control ...
In many cases, some impulsive controllers were modelled using Dirac delta function and based on the properties of the Dirac delta function, the controlled.
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Differential Games with Impulse Control
In so doing, the Dirac delta function is used to describe the impulse control. Such systems represent a special case of hybrid systems.
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Gauss' law - Richard Fitzpatrick
The one-dimensional spike function $\delta(x)$ is called the Dirac delta-function after the Cambridge physicist Paul Dirac who invented it in 1927.
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[PDF] Chapter 1. Electric Charge Interaction
Again, Dirac's delta function allows using the last equation to recover Eq. (37) for discrete charges as well, so Eq. (38) may be considered as the general ...
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[PDF] CS465 Notes: Sampling and reconstruction - CS@Cornell
of sampling theory. That is the idea of an impulse function, also called the Dirac delta function, denoted δ(x). 15. Page 16. Intuitively, the delta function ...
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[PDF] Sampling and Reconstruction
The delta function. • Dirac delta function, zero width, infinite height and unit area. Page 16. Sifting and shifting. Page 17. Shah/impulse train function.