# Linear differential equation

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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 (algebra), space (geometry), and change. It has no generally accepted definition.

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 differential 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.

## Contents

${\displaystyle a_{0}(x)y+a_{1}(x)y'+a_{2}(x)y''+\cdots +a_{n}(x)y^{(n)}+b(x)=0,}$

where ${\displaystyle a_{0}(x)}$, ..., ${\displaystyle a_{n}(x)}$ and ${\displaystyle b(x)}$ are arbitrary differentiable functions that do not need to be linear, and ${\displaystyle y',\ldots ,y^{(n)}}$ 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 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 x2 − 4x + 7. An example in three variables is x3 + 2xyz2yz + 1.

In mathematics, and more specifically in analysis, a holonomic function is a smooth function of 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.

## Basic terminology

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.

## Linear differential operator

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

${\displaystyle {\frac {d^{i}}{dx^{i}}}}$

in the case of univariate functions, and

${\displaystyle {\frac {\partial ^{i_{1}+\cdots +i_{n}}}{\partial x_{1}^{i_{1}}\cdots \partial x_{n}^{i_{n}}}}}$

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]

${\displaystyle a_{0}(x)+a_{1}(x){\frac {d}{dx}}+\cdots +a_{n}(x){\frac {d^{n}}{dx^{n}}},}$

where ${\displaystyle a_{0}(x),\ldots ,a_{n}(x)}$ are differentiable functions, and the nonnegative integer n is the order of the operator (if ${\displaystyle a_{n}(x)}$ 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

${\displaystyle L=a_{0}(x)+a_{1}(x){\frac {d}{dx}}+\cdots +a_{n}(x){\frac {d^{n}}{dx^{n}}},}$

is a linear differential operator, then the equation

${\displaystyle a_{0}(x)y+a_{1}(x)y'+a_{2}(x)y''+\cdots +a_{n}(x)y^{(n)}=b(x)}$

may be rewritten

${\displaystyle Ly=b(x).}$

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 ${\displaystyle Ly(x)=b(x)}$ or ${\displaystyle Ly=b}$

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 ${\displaystyle Ly=0}$.

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 ${\displaystyle Ly(x)=b(x)}$ have the form

${\displaystyle S_{0}(x)+c_{1}S_{1}(x)+\cdots +c_{n}S_{n}(x),}$

where ${\displaystyle c_{1},\ldots ,c_{n}}$ are arbitrary numbers. Typically, the hypotheses of Carathéodory's theorem are satisfied in an interval I, if the functions ${\displaystyle b,a_{0},\ldots ,a_{n}}$ are continuous in I, and there is a positive real number k such that ${\displaystyle |a_{n}(x)|>k}$ for every x in I.

## Homogeneous equation with constant coefficients

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

${\displaystyle a_{0}y+a_{1}y'+a_{2}y''+\cdots +a_{n}y^{(n)}=0}$

where ${\displaystyle a_{1},\ldots ,a_{n}}$ 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 ${\displaystyle e^{x}}$, which is the unique solution of the equation ${\displaystyle f'=f}$ such that ${\displaystyle f(0)=1}$. It follows that the nth derivative of ${\displaystyle e^{cx}}$ is ${\displaystyle c^{n}e^{cx},}$ and this allows solving homogeneous linear differential equations rather easily.

Let

${\displaystyle a_{0}y+a_{1}y'+a_{2}y''+\cdots +a_{n}y^{(n)}=0}$

be a homogeneous linear differential equation with constant coefficients (that is ${\displaystyle a_{0},\ldots ,a_{n}}$ are real or complex numbers).

Searching solutions of this equation that have the form ${\displaystyle e^{\alpha x}}$ is equivalent to searching the constants ${\displaystyle \alpha }$ such that

${\displaystyle a_{0}e^{\alpha x}+a_{1}\alpha e^{\alpha x}+a_{2}\alpha ^{2}e^{\alpha x}+\cdots +a_{n}\alpha ^{n}e^{\alpha x}=0.}$

Factoring out ${\displaystyle e^{\alpha x}}$ (which is never zero) shows that ${\displaystyle \alpha }$ must be a root of the characteristic polynomial

${\displaystyle a_{0}+a_{1}t+a_{2}t^{2}+\cdots +a_{n}t^{n}}$

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
${\displaystyle y''''-2y'''+2y''-2y'+y=0}$

has the characteristic equation

${\displaystyle z^{4}-2z^{3}+2z^{2}-2z+1=0.}$

This has zeros, i, i, and 1 (multiplicity 2). The solution basis is thus

${\displaystyle e^{ix},\;e^{-ix},\;e^{x},\;xe^{x}.}$

A real basis of solution is thus

${\displaystyle \cos x,\;\sin x,\;e^{x},\;xe^{x}.}$

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

${\displaystyle x^{k}e^{\alpha x},}$

where k is a nonnegative integer, ${\displaystyle \alpha }$ 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 ${\displaystyle \alpha }$ is a root of the characteristic polynomial of multiplicity m, the characteristic polynomial may be factored as ${\displaystyle P(t)(t-\alpha )^{m}.}$ Thus, applying the differential operator of the equation is equivalent with applying first m times the operator ${\displaystyle {\frac {d}{dx}}-\alpha ,}$ and then the operator that has P as characteristic polynomial. By the exponential shift theorem,

${\displaystyle \left({\frac {d}{dx}}-\alpha \right)\left(x^{k}e^{\alpha x}\right)=kx^{k-1}e^{\alpha x},}$

and thus one gets zero after k + 1 application of ${\displaystyle {\frac {d}{dx}}-\alpha .}$

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 aib is also a root, of the same multiplicity. Thus a real basis is obtained by using Euler's formula, and replacing ${\displaystyle x^{k}e^{(a+ib)x}}$ and ${\displaystyle x^{k}e^{(a-ib)x}}$ by ${\displaystyle x^{k}e^{ax}\cos(bx)}$ and ${\displaystyle x^{k}e^{ax}\sin(bx).}$

### Second-order case

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

${\displaystyle y''+ay'+by=0,}$

and its characteristic polynomial is

${\displaystyle r^{2}+ar+b.}$

If a and b are real, there are three cases for the solutions, depending on the discriminant ${\displaystyle D=a^{2}-4b.}$ In all three cases, the general solution depends on two arbitrary constants ${\displaystyle c_{1}}$ and ${\displaystyle c_{2}}$.

• If D > 0, the characteristic polynomial has two distinct real roots ${\displaystyle \alpha }$, and ${\displaystyle \beta }$. In this case, the general solution is
${\displaystyle c_{1}e^{\alpha x}+c_{2}e^{\beta x}.}$
• If D = 0, the characteristic polynomial has a double root ${\displaystyle -a/2}$, and the general solution is
${\displaystyle (c_{1}+c_{2}x)e^{-ax/2}.}$
• If D < 0, the characteristic polynomial has two complex conjugate roots ${\displaystyle \alpha \pm \beta i}$, and the general solution is
${\displaystyle c_{1}e^{(\alpha +\beta i)x}+c_{2}e^{(\alpha -\beta i)x},}$
which may be rewritten in real terms, using Euler's formula as
${\displaystyle e^{\alpha x}(c_{1}\cos(\beta x)+c_{2}\sin(\beta x)).}$

Finding the solution ${\displaystyle y(x)}$ satisfying ${\displaystyle y(0)=d_{1}}$ and ${\displaystyle y'(0)=d_{2},}$ one equates the values of the above general solution at 0 and its derivative there to ${\displaystyle d_{1}}$ and ${\displaystyle d_{2},}$ respectively. This results in a linear system of two linear equations in the two unknowns ${\displaystyle c_{1}}$ and ${\displaystyle c_{2}.}$ 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.

## Non-homogeneous equation with constant coefficients

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

${\displaystyle y^{(n)}(x)+a_{1}y^{(n-1)}(x)+\cdots +a_{n-1}y'(x)+a_{n}y(x)=f(x),}$

where ${\displaystyle a_{1},\ldots ,a_{n}}$ 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 the exponential response formula may be used. If, more generally, f is linear combination of functions of the form ${\displaystyle x^{n}e^{ax}}$, ${\displaystyle x^{n}\cos {ax}}$, and ${\displaystyle x^{n}\sin {ax}}$, where n is a nonnegative integer, and a a constant (which need not 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

${\displaystyle y^{(n)}+a_{1}y^{(n-1)}+\cdots +a_{n-1}y'+a_{n}y=0}$

is

${\displaystyle y=u_{1}y_{1}+\cdots +u_{n}y_{n},}$

where ${\displaystyle (y_{1},\ldots ,y_{n})}$ is a basis of the vector space of the solutions and ${\displaystyle u_{1},\ldots ,u_{n}}$ are arbitrary constants. The method of variation of constants takes its name that, instead of considering ${\displaystyle u_{1},\ldots ,u_{n}}$ 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

${\displaystyle 0=u'_{1}y_{1}+u'_{2}y_{2}+\cdots +u'_{n}y_{n}}$
${\displaystyle 0=u'_{1}y'_{1}+u'_{2}y'_{2}+\cdots +u'_{n}y'_{n}}$
${\displaystyle \cdots }$
${\displaystyle 0=u'_{1}y_{1}^{(n-2)}+u'_{2}y_{2}^{(n-2)}+\cdots +u'_{n}y_{n}^{(n-2)},}$

which imply (by product rule and induction)

${\displaystyle y^{(i)}=u_{1}y_{1}^{(i)}+\cdots +u_{n}y_{n}^{(i)}}$

for i = 1, ..., n – 1, and

${\displaystyle y^{(n)}=u_{1}y_{1}^{(n)}+\cdots +u_{n}y_{n}^{(n)}+u'_{1}y_{1}^{(n-1)}+u'_{2}y_{2}^{(n-1)}+\cdots +u'_{n}y_{n}^{(n-1)}.}$

Replacing in the original equation y and its derivative by these expression, and using the fact that ${\displaystyle y_{1},\ldots ,y_{n}}$ are solutions of the original homogeneous equation, one gets

${\displaystyle f=u'_{1}y_{1}^{(n-1)}+\cdots +u'_{n}y_{n}^{(n-1)}.}$

One has thus a system of n linear equations in ${\displaystyle u'_{1},\ldots ,u'_{n}}$, which can be solved by any method of linear algebra. Then the computation of antiderivatives gives ${\displaystyle u_{1},\ldots ,u_{n}}$ and then ${\displaystyle y=u_{1}y_{1}+\cdots +u_{n}y_{n}.}$

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.

## First-order equation with variable coefficients

Example
Solving the equation
${\displaystyle y'(x)+y(x)/x=3x.}$

The associated homogeneous equation gives

${\displaystyle y'/y=-1/x,}$

that is

${\displaystyle y=c/x.}$

Dividing the original equation by one of these solutions gives

${\displaystyle xy'+y=3x^{2}.}$

That is

${\displaystyle (xy)'=3x^{2},}$
${\displaystyle xy=x^{3}+c,}$

and

${\displaystyle y(x)=x^{2}+c/x.}$

For the initial condition

${\displaystyle y(1)=\alpha ,}$

one gets the particular solution

${\displaystyle y(x)=x^{2}+{\frac {\alpha -1}{x}}.}$

The general form of a linear ordinary differential linear equation of order 1 is, after having divided by the coefficient of ${\displaystyle y'(x)}$,

${\displaystyle y'(x)=f(x)y(x)+g(x).}$

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)

${\displaystyle {\frac {y'}{y}}=f,}$

that may easily be integrated as

${\displaystyle \log y=k+F,}$

where k is an arbitrary constant of integration and

${\displaystyle F=\int fdx}$

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

${\displaystyle y=ce^{F},}$

where ${\displaystyle c=e^{k}}$ is an arbitrary constant.

For solving the non homogeneous equation, one may multiply it by the multiplicative inverse ${\displaystyle e^{-F}}$ of a solution the homogeneous equation. This gives

${\displaystyle y'e^{-F}-yfe^{-F}=ge^{-F}.}$

As ${\displaystyle -fe^{-F}={\frac {d}{dx}}\left(e^{-F}\right),}$ the product rule allows rewriting the equation as

${\displaystyle {\frac {d}{dx}}\left(ye^{-F}\right)=ge^{-F}.}$

Thus, the general solution is

${\displaystyle y=ce^{F}+e^{F}\int ge^{-F}dx,}$

where c is a constant of integration, and ${\displaystyle F=\int fdx}$.

## System of linear differential equations

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 ${\displaystyle y',y'',\ldots ,y^{(k)}}$ appear in an equation, one may replace them by new unknown functions ${\displaystyle y_{1},\ldots ,y_{k}}$ that must satisfy the equations ${\displaystyle y'=y_{1}}$ and ${\displaystyle y_{i}'=y_{i+1},}$ 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

{\displaystyle {\begin{aligned}y_{1}'(x)&=b_{1}(x)+a_{1,1}(x)y_{1}+\cdots +a_{1,n}(x)y_{n}\\\vdots &\\y_{n}'(x)&=b_{n}(x)+a_{n,1}(x)y_{1}+\cdots +a_{n,n}(x)y_{n},\end{aligned}}}

where ${\displaystyle b_{n}}$ and the ${\displaystyle a_{i,j}}$ are functions of x. In matrix notation, this system may be written (omitting "(x)")

${\displaystyle \mathbf {y} '=A\mathbf {y} +\mathbf {b} .}$

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

${\displaystyle \mathbf {u} '=A\mathbf {u} .}$

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 ${\displaystyle U(x)}$, 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 ${\displaystyle \textstyle B=\int Adx}$ of A. In fact, in these cases, one has

${\displaystyle {\frac {d}{dx}}\exp(B)=A\exp(B).}$

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

${\displaystyle \mathbf {y} (x)=U(x)\mathbf {y_{0}} +U(x)\int U^{-1}(x)\mathbf {b} (x)\,dx,}$

where the column matrix ${\displaystyle \mathbf {y_{0}} }$ is an arbitrary constant of integration.

If initial conditions are given as

${\displaystyle \mathbf {y} (x_{0})=\mathbf {y} _{0},}$

the solution that satisfies these initial conditions is

${\displaystyle \mathbf {y} (x)=U(x)U^{-1}(x_{0})\mathbf {y_{0}} +U(x)\int _{x_{0}}^{x}U^{-1}(t)\mathbf {b} (t)\,dt.}$

## Higher order with variable coefficients

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 which 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 equation

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

${\displaystyle x^{n}y^{(n)}(x)+a_{n-1}x^{n-1}y^{(n-1)}(x)+\cdots +a_{0}y(x)=0,}$

where ${\displaystyle a_{0},\ldots ,a_{n-1}}$ are constant coefficients.

## Holonomic functions

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]

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## References

1. Gershenfeld 1999, p.9
2. Zeilberger, Doron. A holonomic systems approach to special functions identities . Journal of computational and applied mathematics. 32.3 (1990): 321-368
3. 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