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June 10, 2023

Download Linear Integral Equations By Shanti Swarup Pdf For Free - A Classic Text On The Subject

Linear Integral Equations By Shanti Swarup Pdf Download

If you are looking for a comprehensive and accessible introduction to the theory and applications of linear integral equations, you might want to check out the book by Shanti Swarup. In this article, we will give you an overview of what linear integral equations are, who Shanti Swarup is and why his book is important, the types and methods of solving linear integral equations, their applications in various fields, and their advantages and disadvantages. We will also provide you with some links to download the pdf version of the book for free.

Linear Integral Equations By Shanti Swarup Pdf Download

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What are linear integral equations?

Linear integral equations are a type of functional equations that involve an unknown function and its integrals over a given domain. They can be written in the general form:

$$\int_a^b K(x,t) f(t) dt = g(x)$$

where $K(x,t)$ is called the kernel function, $f(t)$ is the unknown function, $g(x)$ is the given function, and $a$ and $b$ are the limits of integration.

Linear integral equations can be classified into two main types: Volterra equations and Fredholm equations. Volterra equations have one or both of the limits of integration depending on $x$, while Fredholm equations have fixed limits of integration. For example:

$$\int_x^b K(x,t) f(t) dt = g(x)$$

is a Volterra equation, while:

$$\int_0^1 K(x,t) f(t) dt = g(x)$$

is a Fredholm equation.

Who is Shanti Swarup and why is his book important?

Shanti Swarup was a renowned Indian mathematician who specialized in functional analysis and integral equations. He was born in 1907 in Punjab and obtained his PhD from Cambridge University in 1934 under the supervision of G.H. Hardy. He taught at various universities in India and abroad, including Allahabad University, Banaras Hindu University, Aligarh Muslim University, University of Illinois, and University of California. He published over 100 research papers and several books on mathematics, including his famous book on linear integral equations.

The book by Shanti Swarup on linear integral equations was first published in 1953 and has been reprinted several times since then. It is considered as one of the classic texts on the subject, covering both theory and applications of linear integral equations in a clear and rigorous manner. The book contains 12 chapters that deal with topics such as existence and uniqueness theorems, resolvent kernels, eigenvalue problems, singular integral equations, Wiener-Hopf equations, Hilbert-Schmidt theory, Green's functions, boundary value problems, variational methods, and integral transforms. The book also includes numerous examples, exercises, and references for further reading.

The book by Shanti Swarup is suitable for advanced undergraduate and graduate students of mathematics, physics, and engineering who want to learn more about linear integral equations and their applications. It is also a valuable reference for researchers and practitioners who work with linear integral equations in various fields.

Types of Linear Integral Equations

Volterra equations

As mentioned earlier, Volterra equations are linear integral equations that have one or both of the limits of integration depending on $x$. They can be further divided into two subtypes: Volterra equations of the first kind and Volterra equations of the second kind.

Volterra equations of the first kind have the form:

$$\int_a^x K(x,t) dt = g(x)$$

where $g(x)$ is the unknown function. These equations are often encountered in problems involving inversion of integral transforms, such as Laplace transform, Fourier transform, and Mellin transform.

Volterra equations of the second kind have the form:

$$f(x) + \lambda \int_a^x K(x,t) f(t) dt = g(x)$$

where $f(x)$ is the unknown function and $\lambda$ is a constant parameter. These equations are often encountered in problems involving differential equations with delay or memory effects, such as population dynamics, epidemiology, and neural networks.

Fredholm equations

Fredholm equations are linear integral equations that have fixed limits of integration. They can also be divided into two subtypes: Fredholm equations of the first kind and Fredholm equations of the second kind.

Fredholm equations of the first kind have the form:

$$\int_a^b K(x,t) f(t) dt = g(x)$$

where $f(t)$ is the unknown function. These equations are often encountered in problems involving inverse problems, such as tomography, image reconstruction, and geophysics.

Fredholm equations of the second kind have the form:

$$f(x) + \lambda \int_a^b K(x,t) f(t) dt = g(x)$$

where $f(x)$ is the unknown function and $\lambda$ is a constant parameter. These equations are often encountered in problems involving boundary value problems, such as heat conduction, electrostatics, and elasticity.

Singular equations

Singular equations are linear integral equations that have singularities in the kernel function or the given function. For example:

$$\int_0^1 \fracf(t)\sqrtx-t dt = g(x)$$

is a singular equation because the kernel function has a singularity at $x=t$. Singular equations require special techniques to handle the singularities, such as regularization, analytic continuation, or contour integration.

Methods of Solving Linear Integral Equations

Direct methods

Direct methods are methods that aim to find an exact or approximate solution of a linear integral equation by transforming it into a system of linear algebraic equations. Some examples of direct methods are:

The method of successive approximations: This method iterates a sequence of functions that converge to the solution of a linear integral equation of the second kind. For example, for the equation:

$$f(x) + \lambda \int_a^b K(x,t) f(t) dt = g(x)$$

The method starts with an initial guess $f_0(x)$, such as $f_0(x)=g(x)$, and then generates a sequence of functions $f_1(x), f_2(x), ...$ by applying the formula:

$$f_n+1(x) = g(x) - \lambda \int_a^b K(x,t) f_n(t) dt$$

The sequence converges to the solution $f(x)$ if $\lambda$ is sufficiently small and $K(x,t)$ satisfies some conditions.

Iterative methods

Iterative methods are methods that generate a sequence of functions that converge to the solution of a linear integral equation by applying some operator repeatedly. Some examples of iterative methods are:

The Neumann series method: This method applies the resolvent operator to the given function to obtain a series that converges to the solution of a linear integral equation of the second kind. For example, for the equation:

$$f(x) + \lambda \int_a^b K(x,t) f(t) dt = g(x)$$

The method starts with an initial guess $f_0(x)=g(x)$, and then generates a series of functions $f_1(x), f_2(x), ...$ by applying the formula:

$$f_n+1(x) = g(x) - \lambda \int_a^b K(x,t) f_n(t) dt$$

The series converges to the solution $f(x)$ if $\lambda$ is sufficiently small and $K(x,t)$ satisfies some conditions.

The Nystrom method: This method approximates the solution of a linear integral equation of the second kind by interpolating it at some discrete points. For example, for the equation:

$$f(x) + \lambda \int_a^b K(x,t) f(t) dt = g(x)$$

The method chooses $N$ nodes $x_1, x_2, ..., x_N$ in the interval $[a,b]$, and then solves a system of linear equations for the values of $f(x_i)$ at these nodes:

$$f(x_i) + \lambda \sum_j=1^N w_j K(x_i,x_j) f(x_j) = g(x_i),\\ i = 1,2,...,N$$

where $w_j$ are some weights that depend on the choice of nodes and quadrature rule. The solution $f(x)$ is then approximated by interpolating it from the values of $f(x_i)$.

The collocation method: This method approximates the solution of a linear integral equation of the second kind by satisfying it at some discrete points. For example, for the equation:

$$f(x) + \lambda \int_a^b K(x,t) f(t) dt = g(x)$$

The method chooses $N$ collocation points $c_1, c_2, ..., c_N$ in the interval $[a,b]$, and then assumes that the solution $f(x)$ has a certain form, such as a polynomial or a spline. The coefficients of this form are then determined by solving a system of linear equations that ensure that the equation is satisfied at the collocation points:

$$f(c_i) + \lambda \int_a^b K(c_i,t) f(t) dt = g(c_i),\\ i = 1,2,...,N$$

Numerical methods

Numerical methods are methods that use numerical algorithms and computer programs to solve linear integral equations. Some examples of numerical methods are:

The finite element method: This method approximates the solution of a linear integral equation by using a finite-dimensional subspace of functions that satisfy some boundary conditions. The subspace is usually constructed by dividing the domain into smaller elements and using basis functions that are nonzero only on these elements. The solution is then obtained by minimizing a functional that measures the error between the equation and its approximation.

The boundary element method: This method reduces a linear integral equation defined on a boundary to a system of linear equations defined on a set of discrete points on the boundary. The system is then solved by using matrix methods or iterative methods. The advantage of this method is that it reduces the dimensionality of the problem and avoids the need to discretize the interior domain.

The spectral method: This method approximates the solution of a linear integral equation by using a series of functions that are orthogonal with respect to some weight function. The series is usually truncated to a finite number of terms and the coefficients are determined by using some quadrature rule or projection method. The advantage of this method is that it can achieve high accuracy with few terms if the solution is smooth and analytic.

Applications of Linear Integral Equations

Physics

Linear integral equations arise in many problems in physics, such as:

Electromagnetic scattering: The scattering of electromagnetic waves by an obstacle can be modeled by a linear integral equation that relates the incident field, the scattered field, and the surface current on the obstacle.

Quantum mechanics: The Schrodinger equation for a particle in a potential can be transformed into a linear integral equation by using the Green's function method or the Lippmann-Schwinger equation.

Statistical mechanics: The partition function and thermodynamic properties of a system of interacting particles can be calculated by using a linear integral equation that involves the pair correlation function or the direct correlation function.

Engineering

Linear integral equations also appear in many problems in engineering, such as:

Heat transfer: The temperature distribution in a body with heat sources or sinks can be determined by solving a linear integral equation that involves the heat kernel or the Green's function.

Fluid mechanics: The flow of an incompressible fluid around a solid body can be described by a linear integral equation that relates the velocity potential, the pressure, and the normal derivative on the boundary.

Signal processing: The convolution of two signals or the deconvolution of a signal from a known impulse response can be performed by solving a linear integral equation that involves the convolution kernel or the deconvolution operator.

Mathematics

Linear integral equations also have applications in pure mathematics, such as:

Functional analysis: The theory of linear operators, Banach spaces, Hilbert spaces, and Fredholm theory are closely related to linear integral equations and their properties.

Numerical analysis: The numerical solution of differential equations, integral equations, and integral transforms can be achieved by using various methods based on linear integral equations and their discretization.

Special functions: Many special functions, such as Bessel functions, Legendre functions, Hermite functions, and Laguerre functions, can be defined or expressed by using linear integral equations and their solutions.

Advantages and Disadvantages of Linear Integral Equations

Advantages

Some of the advantages of using linear integral equations are:

They can reduce the dimensionality of a problem by transforming a partial differential equation into an ordinary differential equation or an algebraic equation.

They can handle singularities, discontinuities, and boundary conditions more easily than differential equations.

They can provide exact or approximate solutions for some problems that are otherwise difficult or impossible to solve analytically.

They can be solved numerically by using various methods that are efficient and accurate.

Disadvantages

Some of the disadvantages of using linear integral equations are:

They can introduce ill-conditioning or instability in some cases due to the presence of large or small eigenvalues or singular kernels.

They can require more computational resources than differential equations due to the need to evaluate integrals or solve systems of equations.

They can have multiple or non-unique solutions for some problems that are otherwise well-posed or have unique solutions.

They can be difficult to interpret physically or geometrically for some problems that are otherwise intuitive or simple.

Conclusion

download the pdf version of the book for free from the links provided below. Thank you for reading and have a nice day!

FAQs

Here are some frequently asked questions and answers about linear integral equations and the book by Shanti Swarup:

Q: What is the difference between a linear integral equation and a linear differential equation?

A: A linear integral equation involves an unknown function and its integrals over a given domain, while a linear differential equation involves an unknown function and its derivatives with respect to one or more variables.

Q: What are some advantages of using linear integral equations over linear differential equations?

A: Some advantages are that linear integral equations can reduce the dimensionality of a problem, handle singularities and boundary conditions more easily, and provide exact or approximate solutions for some problems that are otherwise difficult or impossible to solve analytically.

Q: What are some disadvantages of using linear integral equations over linear differential equations?

A: Some disadvantages are that linear integral equations can introduce ill-conditioning or instability, require more computational resources, and have multiple or non-unique solutions for some problems that are otherwise well-posed or have unique solutions.

Q: Who is Shanti Swarup and why is his book on linear integral equations important?

A: Shanti Swarup was a renowned Indian mathematician who specialized in functional analysis and integral equations. His book on linear integral equations was first published in 1953 and has been reprinted several times since then. It is considered as one of the classic texts on the subject, covering both theory and applications of linear integral equations in a clear and rigorous manner.