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In mathematics, a **linear differential equation** is a differential equation that is defined by a linear polynomial in the unknown function and its derivatives, that is an equation of the form

**Mathematics** includes the study of such topics as quantity, structure, space, and change.

A **differential equation** is a mathematical equation that relates some function with its derivatives. In applications, the functions usually represent physical quantities, the derivatives represent their rates of change, and the equation defines a relationship between the two. Because such relations are extremely common, differential equations play a prominent role in many disciplines including engineering, physics, economics, and biology.

In mathematics, an **equation** is a statement that asserts the equality of two expressions. The word *equation* and its cognates in other languages may have subtly different meanings; for example, in French an *équation* is defined as containing one or more variables, while in English any equality is an equation.

- Basic terminology
- Linear differential operator
- Homogeneous equation with constant coefficients
- Second-order case
- Non-homogeneous equation with constant coefficients
- First-order equation with variable coefficients
- System of linear differential equations
- Higher order with variable coefficients
- Cauchy–Euler equation
- Holonomic functions
- See also
- References
- External links

where , ..., and are arbitrary differentiable functions that do not need to be linear, and are the successive derivatives of an unknown function y of the variable x.

In calculus, a **differentiable function** of one real variable is a function whose derivative exists at each point in its domain. As a result, the graph of a differentiable function must have a (non-vertical) tangent line at each point in its domain, be relatively smooth, and cannot contain any breaks, bends, or cusps.

This is an ordinary differential equation (ODE). A linear differential equation may also be a linear partial differential equation (PDE), if the unknown function depends on several variables, and the derivatives that appear in the equation are partial derivatives. In this article, only ordinary differential equations are considered.

In mathematics, an **ordinary differential equation** (**ODE**) is a differential equation containing one or more functions of one independent variable and the derivatives of those functions. The term *ordinary* is used in contrast with the term partial differential equation which may be with respect to *more than* one independent variable.

In mathematics, a **partial differential equation** (**PDE**) is a differential equation that contains beforehand unknown multivariable functions and their partial derivatives. PDEs are used to formulate problems involving functions of several variables, and are either solved by hand, or used to create a computer model. A special case is ordinary differential equations (ODEs), which deal with functions of a single variable and their derivatives.

In mathematics, a **partial derivative** of a function of several variables is its derivative with respect to one of those variables, with the others held constant. Partial derivatives are used in vector calculus and differential geometry.

A linear differential equation or a system of linear equations such that the associated homogeneous equations have constant coefficients may be solved by quadrature (mathematics), which means that the solutions may be expressed in terms of integrals. This is also true for a linear equation of order one, with non-constant coefficients. An equation of order two or higher with non-constant coefficients cannot, in general, be solved by quadrature. For order two, Kovacic's algorithm allows deciding whether there are solutions in terms of integrals, and computing them if any.

In mathematics, * quadrature* is a historical term which means the process of determining area. This term is still used nowadays in the context of differential equations, where "solving an equation by quadrature" means expressing its solution in terms of integrals.

In calculus, an **antiderivative**, **primitive function**, **primitive integral** or **indefinite integral** of a function *f* is a differentiable function *F* whose derivative is equal to the original function *f*. This can be stated symbolically as . The process of solving for antiderivatives is called **antidifferentiation** and its opposite operation is called differentiation, which is the process of finding a derivative.

The solutions of linear differential equations with polynomial coefficients are called holonomic functions. This class of functions is stable under sums, products, differentiation, integration, and contains many usual functions and special functions such as exponential function, logarithm, sine, cosine, inverse trigonometric functions, error function, Bessel functions and hypergeometric functions. Their representation by the defining differential equation and initial conditions allows making algorithmic (on these functions) most operations of calculus, such as computation of antiderivatives, limits, asymptotic expansion, and numerical evaluation to any precision, with a certified error bound.

In mathematics, a **polynomial** is an expression consisting of variables and coefficients, that involves only the operations of addition, subtraction, multiplication, and non-negative integer exponents of variables. An example of a polynomial of a single indeterminate, *x*, is *x*^{2} − 4*x* + 7. An example in three variables is *x*^{3} + 2*xyz*^{2} − *yz* + 1.

In mathematics, and more specifically in analysis, a **holonomic function** is a smooth function in several variables that is a solution of a system of linear homogeneous differential equations with polynomial coefficients and satisfies a suitable dimension condition in terms of D-modules theory. More precisely, a holonomic function is an element of a holonomic module of smooth functions. Holonomic functions can also be described as **differentiably finite functions**, also known as **D-finite functions**. When a power series in the variables is the Taylor expansion of a holonomic function, the sequence of its coefficients, in one or several indices, is also called *holonomic*. **Holonomic sequences** are also called **P-recursive sequences**: they are defined recursively by multivariate recurrences satisfied by the whole sequence and by suitable specializations of it. The situation simplifies in the univariate case: any univariate sequence that satisfies a linear homogeneous recurrence relation with polynomial coefficients, or equivalently a linear homogeneous difference equation with polynomial coefficients, is holonomic.

The **derivative** of a function of a real variable measures the sensitivity to change of the function value with respect to a change in its argument. Derivatives are a fundamental tool of calculus. For example, the derivative of the position of a moving object with respect to time is the object's velocity: this measures how quickly the position of the object changes when time advances.

The highest order of derivation that appears in a differentiable equation is the *order* of the equation. The term *b*(*x*), which does not depend on the unknown function and its derivatives, is sometimes called the *constant term* of the equation (by analogy with algebraic equations), even when this term is a non-constant function. If the constant term is the zero function, then the differential equation is said to be *homogeneous*, as it is a homogeneous polynomial in the unknown function and its derivatives. The equation obtained by replacing, in a linear differential equation, the constant term by the zero function is the *associated homogeneous equation*. A differential equation has *constant coefficients* if only constant functions appear as coefficients in the associated homogeneous equation.

In mathematics, an **algebraic equation** or **polynomial equation** is an equation of the form

In mathematics, a **homogeneous polynomial** is a polynomial whose nonzero terms all have the same degree. For example, is a homogeneous polynomial of degree 5, in two variables; the sum of the exponents in each term is always 5. The polynomial is not homogeneous, because the sum of exponents does not match from term to term. A polynomial is homogeneous if and only if it defines a homogeneous function. An **algebraic form**, or simply **form**, is a function defined by a homogeneous polynomial. A **binary form** is a form in two variables. A *form* is also a function defined on a vector space, which may be expressed as a homogeneous function of the coordinates over any basis.

In mathematics, a **constant function** is a function whose (output) value is the same for every input value. For example, the function is a constant function because the value of is 4 regardless of the input value .

A *solution* of a differential equation is a function that satisfies the equation. The solutions of a homogeneous linear differential equation form a vector space. In the ordinary case, this vector space has a finite dimension, equal to the order of the equation. All solutions of a linear differential equation are found by adding to a particular solution any solution of the associated homogeneous equation.

A *basic differential operator* of order i is a mapping that maps any differentiable function to its ith derivative, or, in the case of several variables, to one of its partial derivatives of order i. It is commonly denoted

in the case of univariate functions, and

in the case of functions of n variables. The basic differential operators include the derivative of order 0, which is the identity mapping.

A **linear differential operator** (abbreviated, in this article, as *linear operator* or, simply, *operator*) is a linear combination of basic differential operators, with differentiable functions as coefficients. In the univariate case, a linear operator has thus the form^{ [1] }

where are differentiable functions, and the nonnegative integer n is the *order* of the operator (if is not the zero function).

Let L be a linear differential operator. The application of L to a function f is usually denoted *Lf* or *Lf*(*X*), if one needs to specify the variable (this must not be confused with a multiplication). A linear differential operator is a linear operator, since it maps sums to sums and the product by a scalar to the product by the same scalar.

As the sum of two linear operators is a linear operator, as well as the product (on the left) of a linear operator by a differentiable function, the linear differential operators form a vector space over the real numbers or the complex numbers (depending on the nature of the functions that are considered). They form also a free module over the ring of differentiable functions.

The language of operators allows a compact writing for differentiable equations: if

is a linear differential operator, then the equation

may be rewritten

There may be several variants to this notation; in particular the variable of differentiation may appear explicitly or not in y and the right-hand and of the equation, such as or

The *kernel* of a linear differential operator is its kernel as a linear mapping, that is the vector space of the solutions of the (homogeneous) differential equation .

In the case of an ordinary differential operator of order n, Carathéodory's existence theorem implies that, under very mild conditions, the kernel of L is a vector space of dimension n, and that the solutions of the equation have the form

where are arbitrary numbers. Typically, the hypotheses of Carathéodory's theorem are satisfied in an interval I, if the functions are continuous in I, and there is a positive real number k such that for every x in I.

A homogeneous linear differential equation has *constant coefficients* if it has the form

where are (real or complex) numbers. In other words, it has constant coefficients if it is defined by a linear operator with constant coefficients.

The study of these differential equations with constant coefficients dates back to Leonhard Euler, who introduced the exponential function , which is the unique solution of the equation such that . It follows that the nth derivative of is and this allows solving homogeneous linear differential equations rather easily.

Let

be a homogeneous linear differential equation with constant coefficients (that is are real or complex numbers).

Searching solutions of this equation that have the form is equivalent to searching the constants such that

Factoring out (which is never zero) shows that must be a root of the characteristic polynomial

of the differential equation.

When these roots are all distinct, one has *n* distinct solutions that are not necessarily real, even if the coefficients of the equation are real. These solutions can be shown to be linearly independent, by considering the Vandermonde determinant of the values of these solutions at *x* = 0, ..., *n* – 1. Together they form a basis of the vector space of solutions of the differential equation (that is, the kernel of the differential operator).

Example |
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has the characteristic equation This has zeros, A real basis of solution is thus |

In the case where the characteristic polynomial has only simple roots, the preceding provides a complete basis of the solutions vector space. In the case of multiple roots, more linearly independent solutions are needed for having a basis. These have the form

where k is a nonnegative integer, is a root of the characteristic polynomial of multiplicity m, and *k* < *m*. For proving that these functions are solutions, one may remark that if is a root of the characteristic polynomial of multiplicity m, the characteristic polynomial may be factored as Thus, applying the differential operator of the equation is equivalent with applying first m times the operator and then the operator that has P as characteristic polynomial. By the exponential shift theorem,

and thus one gets zero after *k* + 1 application of

As, by the fundamental theorem of algebra, the sum of the multiplicities of the roots of a polynomial equals the degree of the polynomial, the number of above solutions equals the order of the differential equation, and these solutions form a base of the vector space of the solutions.

In the common case where the coefficients of the equation are real, it is generally more convenient to have a basis of the solutions consisting of real-valued functions. Such a basis may be obtained from the preceding basis by remarking that, if *a* + *ib* is a root of the characteristic polynomial, then *a* – *ib* is also a root, of the same multiplicity. Thus a real basis is obtained by using Euler's formula, and replacing and by and

A homogeneous linear differential equation of the second order may be written

and its characteristic polynomial is

If a and b are real, there are three cases for the solutions, depending on the discriminant In all three cases, the general solution depends on two arbitrary constants and .

- If
*D*> 0, the characteristic polynomial has two distinct real roots , and . In this case, the general solution is

- If
*D*= 0, the characteristic polynomial has a double root , and the general solution is

- If
*D*< 0, the characteristic polynomial has two complex conjugate roots , and the general solution is

- which may be rewritten in real terms, using Euler's formula as

Finding the solution satisfying and one equates the values of the above general solution at 0 and its derivative there to and respectively. This results in a linear system of two linear equations in the two unknowns and Solving this system gives the solution for a so called Cauchy problem, in which the values at 0 for the solution of the DEQ and its derivative are specified.

A non-homogeneous equation of order n with constant coefficients may be written

where are real or complex numbers, *f* is a given function of x, and y is the unknown function (for sake of simplicity, "(*x*)" will be omitted in the following).

There are several methods for solving such an equation. The best method depends of the nature of the function f that makes the equation non-homogeneous. If f is a linear combination of exponential and sinusoidal functions, then exponential response formula may be used. If, more generally, f is linear combination of functions of the form , and , where n is a nonnegative integer and a a constant (which need not to be the same in each term), then the method of undetermined coefficients may be used. Still more general, the annihilator method applies when f satisfies a homogeneous linear differential equation, typically, a holonomic function.

The most general method is the variation of constants, which is presented here.

The general solution of the associated homogeneous equation

is

where is a basis of the vector space of the solutions and are arbitrary constants. The method of variation of constants takes its name that, instead of considering as constants, they are considered as functions that have to be determined for making y a solution of the non-homogeneous equation. For this purpose, one adds the constraints

which imply (by product rule and induction)

for *i* = 1, ..., *n* – 1, and

Replacing in the original equation y and its derivative by these expression, and using the fact that are solutions of the original homogeneous equation, one gets

One has thus a system of n linear equations in , which can be solved by any method of linear algebra. Then the computation of antiderivatives gives and then

As antiderivatives are defined up to the addition of a constant, one finds again that the general solution of the non-homogeneous equation is the sum of an arbitrary solution and the general solution of the associated homogeneous equation.

Example |
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Solving the equation The associated homogeneous equation gives that is Dividing the original equation by one of these solutions gives That is and For the initial condition one gets the particular solution |

The general form of a linear ordinary differential linear equation of order 1 is, after having divided by the coefficient of ,

In the case of a homogeneous equation (that is *g*(*x*) is the zero function), the equation may be rewritten as (omitting "(*x*)" for sake of simplification)

that may easily be integrated as

where k is an arbitrary constant of integration and

is an antiderivative of f. Thus, the general solution of the homogeneous equation is

where is an arbitrary constant.

For solving the non homogeneous equation, one may multiply it by the multiplicative inverse of a solution the homogeneous equation. This gives

As the product rule allows rewriting the equation as

Thus, the general solution is

where c is a constant of integration, and .

A system of linear differential equations consists of several linear differential equations that involve several unknown functions. In general one restricts the study to systems such that the number of unknown functions equals the number of equations.

An arbitrary linear ordinary differential equation and a system of such equations can be converted into a first order system of linear differential equations by adding variables for all but the highest order derivatives. That is, if appear in an equation, one may replace them by new unknown functions that must satisfy the equations and for *i* = 1, ..., *k* – 1.

A linear system of the first order, which has n unknown functions and n differential equations may normally be solved for the derivatives of the unknown functions. If it is not the case this is a differential-algebraic system, and this is a different theory. Therefore, the systems that are considered here have the form

where and the are functions of x. In matrix notation, this system may be written (omitting "(*x*)")

The solving method is similar to that of a single first order linear differential equations, but with complications stemming from noncommutativity of matrix multiplication.

Let

be the homogeneous equation associated to the above matrix equation. Its solutions form a vector space of dimension n, and are therefore the columns of a square matrix of functions , whose determinant is not the zero function. If *n* = 1, or A is a matrix of constants, or, more generally, if A is differentiable and commutes with its derivative, then one may choose for U the exponential of an antiderivative of A. In fact, in these cases, one has

In the general case there is no closed-form solution for the homogeneous equation, and one has to use either a numerical method, or an approximation method such as Magnus expansion.

Knowing the matrix U, the general solution of the non-homogeneous equation is

where the column matrix is an arbitrary constant of integration.

If initial conditions are given as

the solution that satisfies these initial conditions is

A linear ordinary equation of order one with variable coefficients may be solved by quadrature, which means that the solutions may be expressed in terms of integrals. This is not the case for order at least two. This is the main result of Picard–Vessiot theory, a theory that was initiated by Émile Picard and Ernest Vessiot, and whose recent developments are called differential Galois theory.

The impossibility of solving by quadrature can be compared with the Abel–Ruffini theorem, which states that an algebraic equation of degree at least five cannot, in general, be solved by radicals. This analogy extends to the proof methods and motivates the denomination of differential Galois theory

Similarly to the algebraic case, the theory allows deciding which equations may be solved by quadrature, and if possible solving them. However, for both theories, the necessary computations are extremely difficult, even with the most powerful computers.

Nevertheless, the case of order two with rational coefficients has been completely solved by Kovacic's algorithm.

Cauchy–Euler equations are examples of equations of any order, with variable coefficients, that can be solved explicitly. These are the equations of the form

where are constant coefficients.

A holonomic function, also called a *D-finite function* is a function that is a solution of a homogeneous linear differential equation with polynomial coefficients.

Most functions that are commonly considered in mathematics are holonomic or quotients of holonomic functions. In fact, holonomic functions include polynomials, algebraic functions, logarithm, exponential function, sine, cosine, hyperbolic sine, hyperbolic cosine, inverse trigonometric and inverse hyperbolic functions, and many special functions such as Bessel functions and hypergeometric functions.

Holonomic functions have several closure properties; in particular, sums, products, derivative and integrals of holonomic functions are holonomic. Moreover, these closure properties are effective, in the sense that there are algorithms for computing the differential equation of the result of any of these operations, knowing the differential equations of the input.^{ [2] }

Usefulness of the concept of holonomic functions results of Zeilberger's theorem, which follows.^{ [2] }

A *holonomic sequence* is a sequence of numbers that may be generated by a recurrence relation with polynomial coefficients. The coefficients of the Taylor series at a point of a holonomic function form a holonomic sequence. Conversely, if the sequence of the coefficients of a power series is holonomic, then the series defines a holonomic function (even if the radius of convergence is zero). There are efficient algorithms for both conversions, that is for computing the recurrence relation from the differential equation, and *vice versa*. ^{ [2] }

It follows that, if one represents (in a computer) holonomic functions by their defining differential equations and initial conditions, most calculus operations can be done automatically on these functions, such as derivative, indefinite and definite integral, fast computation of Taylor series (thanks of the recurrence relation on its coefficients), evaluation to a high precision with certified bound of the approximation error, limits, localization of singularities, asymptotic behavior at infinity and near singularities, proof of identities, etc.^{ [3] }

In mathematics, a **recurrence relation** is an equation that recursively defines a sequence or multidimensional array of values, once one or more initial terms are given: each further term of the sequence or array is defined as a function of the preceding terms.

In mathematics, a **differential operator** is an operator defined as a function of the differentiation operator. It is helpful, as a matter of notation first, to consider differentiation as an abstract operation that accepts a function and returns another function.

In mathematics, a **Green's function** is the impulse response of an inhomogeneous linear differential equation defined on a domain, with specified initial conditions or boundary conditions.

In mathematics, the **classical orthogonal polynomials** are the most widely used orthogonal polynomials: the Hermite polynomials, Laguerre polynomials, Jacobi polynomials.

In mathematics, a **generalized hypergeometric series** is a power series in which the ratio of successive coefficients indexed by *n* is a rational function of *n*. The series, if convergent, defines a **generalized hypergeometric function**, which may then be defined over a wider domain of the argument by analytic continuation. The generalized hypergeometric series is sometimes just called the hypergeometric series, though this term also sometimes just refers to the Gaussian hypergeometric series. Generalized hypergeometric functions include the (Gaussian) hypergeometric function and the confluent hypergeometric function as special cases, which in turn have many particular special functions as special cases, such as elementary functions, Bessel functions, and the classical orthogonal polynomials.

In the theory of partial differential equations, **elliptic operators** are differential operators that generalize the Laplace operator. They are defined by the condition that the coefficients of the highest-order derivatives be positive, which implies the key property that the principal symbol is invertible, or equivalently that there are no real characteristic directions.

In mathematics, a **homogeneous function** is one with multiplicative scaling behaviour: if all its arguments are multiplied by a factor, then its value is multiplied by some power of this factor.

In mathematical analysis a **pseudo-differential operator** is an extension of the concept of differential operator. Pseudo-differential operators are used extensively in the theory of partial differential equations and quantum field theory.

In mathematics, a **Cauchy-Euler equation** is a linear homogeneous ordinary differential equation with variable coefficients. It is sometimes referred to as an *equidimensional* equation. Because of the particularly simple equidimensional structure the differential equation can be solved explicitly.

In mathematics, the **resultant** of two polynomials is a polynomial expression of their coefficients, which is equal to zero if and only if the polynomials have a common root, or, equivalently, a common factor. In some older texts, the resultant is also called the **eliminant**.

In mathematics, a **polynomial transformation** consists of computing the polynomial whose roots are a given function of the roots of polynomial. Polynomial transformations such as Tschirnhaus transformations are often used to simplify the solution of algebraic equations.

A differential equation can be **homogeneous** in either of two respects.

In algebra, a multivariate polynomial

In mathematics, the **characteristic equation** is an algebraic equation of degree n upon which depends the solution of a given nth-order differential equation or difference equation. The characteristic equation can only be formed when the differential or difference equation is linear and homogeneous, and has constant coefficients. Such a differential equation, with y as the dependent variable and *a*_{n}, *a*_{n − 1}, ..., *a*_{1}, *a*_{0} as constants,

In mathematics, the **Cauchy–Kowalevski theorem** is the main local existence and uniqueness theorem for analytic partial differential equations associated with Cauchy initial value problems. A special case was proven by Augustin Cauchy (1842), and the full result by Sophie Kovalevskaya (1875).

In mathematics, the **exponential response formula** (ERF), also known as **exponential response and complex replacement**, is a method used to find a particular solution of a non-homogeneous linear ordinary differential equation of any order. The exponential response formula is applicable to non-homogeneous linear ordinary differential equations with constant coefficients if the function is polynomial, sinusoidal, exponential or the combination of the three. The general solution of a non-homogeneous linear ordinary differential equation is a superposition of the general solution of the associated homogeneous ODE and a particular solution to the non-homogeneous ODE. Alternative methods for solving ordinary differential equations of higher order are method of undetermined coefficients and method of variation of parameters.

The **Fuchsian theory** of linear differential equations, which is named after Lazarus Immanuel Fuchs, provides a characterization of various types of singularities and the relations among them.

In mathematics a **P-recursive equation** is a linear equation of sequences where the coefficient sequences can be represented as polynomials. P-recursive equations are **linear recurrence equations****with polynomial coefficients**. These equations play an important role in different areas of mathematics, specifically in combinatorics. The sequences which are solutions of these equations are called holonomic, P-recursive or D-finite.

- ↑ Gershenfeld 1999, p.9
- 1 2 3 Zeilberger, Doron.
*A holonomic systems approach to special functions identities*. Journal of computational and applied mathematics. 32.3 (1990): 321-368 - ↑ Benoit, A., Chyzak, F., Darrasse, A., Gerhold, S., Mezzarobba, M., & Salvy, B. (2010, September).
*The dynamic dictionary of mathematical functions (DDMF)*. In International Congress on Mathematical Software (pp. 35-41). Springer, Berlin, Heidelberg.

- Birkhoff, Garrett & Rota, Gian-Carlo (1978),
*Ordinary Differential Equations*, New York: John Wiley and Sons, Inc., ISBN 0-471-07411-X - Gershenfeld, Neil (1999),
*The Nature of Mathematical Modeling*, Cambridge, UK.: Cambridge University Press, ISBN 978-0-521-57095-4 - Robinson, James C. (2004),
*An Introduction to Ordinary Differential Equations*, Cambridge, UK.: Cambridge University Press, ISBN 0-521-82650-0

- http://eqworld.ipmnet.ru/en/solutions/ode.htm
- Dynamic Dictionary of Mathematical Function. Automatic and interactive study of many holonomic functions.

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