General definition

Given F, a function of x, y, and derivatives of y. Then an equation of the form

${\displaystyle F\left(x,y,y',\ldots ,y^{(n-1)}\right)=y^{(n)}}$

is called an explicit ordinary differential equation of order n.^[8][9]

More generally, an implicit ordinary differential equation of order n takes the form:^[10]

${\displaystyle F\left(x,y,y',y'',\ \ldots ,\ y^{(n)}\right)=0}$

There are further classifications:

Autonomous

A differential is autonomous if it does not depend on the variable x.

Linear

A differential equation is linear if ${\displaystyle F}$ can be written as a linear combination of the derivatives of y; that is, if can be rewritten as

${\displaystyle y^{(n)}=\sum _{i=0}^{n-1}a_{i}(x)y^{(i)}+r(x)}$

where a _i (x) and r (x) are continuous functions of x.^[8][11][12]

The function r(x) is called the source term, leading to further classification.^[11][13]

Homogeneous

A linear differential equation is homogeneous if r(x) = 0. In this case, there is always the "trivial solution" y = 0.

Nonhomogeneous (or inhomogeneous)

A linear differential equation is nonhomogeneous if r(x) ≠ 0.

Non-linear

A differential equation that is not linear.

System of ODEs

A number of coupled differential equations form a system of equations. If y is a vector whose elements are functions; y(x) = [y₁(x), y₂(x),..., y_m(x)], and F is a vector-valued function of y and its derivatives, then

${\displaystyle \mathbf {y} ^{(n)}=\mathbf {F} \left(x,\mathbf {y} ,\mathbf {y} ',\mathbf {y} '',\ldots ,\mathbf {y} ^{(n-1)}\right)}$

is an explicit system of ordinary differential equations of order n and dimension m. In column vector form:

${\displaystyle {\begin{pmatrix}y_{1}^{(n)}\\y_{2}^{(n)}\\\vdots \\y_{m}^{(n)}\end{pmatrix}}={\begin{pmatrix}f_{1}\left(x,\mathbf {y} ,\mathbf {y} ',\mathbf {y} '',\ldots ,\mathbf {y} ^{(n-1)}\right)\\f_{2}\left(x,\mathbf {y} ,\mathbf {y} ',\mathbf {y} '',\ldots ,\mathbf {y} ^{(n-1)}\right)\\\vdots \\f_{m}\left(x,\mathbf {y} ,\mathbf {y} ',\mathbf {y} '',\ldots ,\mathbf {y} ^{(n-1)}\right)\end{pmatrix}}}$

These are not necessarily linear. The implicit analogue is:

${\displaystyle \mathbf {F} \left(x,\mathbf {y} ,\mathbf {y} ',\mathbf {y} '',\ldots ,\mathbf {y} ^{(n)}\right)={\boldsymbol {0}}}$

where 0 = (0, 0, ..., 0) is the zero vector. In matrix form

${\displaystyle {\begin{pmatrix}f_{1}(x,\mathbf {y} ,\mathbf {y} ',\mathbf {y} '',\ldots ,\mathbf {y} ^{(n)})\\f_{2}(x,\mathbf {y} ,\mathbf {y} ',\mathbf {y} '',\ldots ,\mathbf {y} ^{(n)})\\\vdots \\f_{m}(x,\mathbf {y} ,\mathbf {y} ',\mathbf {y} '',\ldots ,\mathbf {y} ^{(n)})\end{pmatrix}}={\begin{pmatrix}0\\0\\\vdots \\0\end{pmatrix}}}$

For a system of the form ${\displaystyle \mathbf {F} \left(x,\mathbf {y} ,\mathbf {y} '\right)={\boldsymbol {0}}}$, some sources also require that the Jacobian matrix ${\displaystyle {\frac {\partial \mathbf {F} (x,\mathbf {u} ,\mathbf {v} )}{\partial \mathbf {v} }}}$ be non-singular in order to call this an implicit ODE [system]; an implicit ODE system satisfying this Jacobian non-singularity condition can be transformed into an explicit ODE system. In the same sources, implicit ODE systems with a singular Jacobian are termed differential algebraic equations (DAEs). This distinction is not merely one of terminology; DAEs have fundamentally different characteristics and are generally more involved to solve than (nonsingular) ODE systems.^[14][15][16] Presumably for additional derivatives, the Hessian matrix and so forth are also assumed non-singular according to this scheme, although note that any ODE of order greater than one can be (and usually is) rewritten as system of ODEs of first order,^[17] which makes the Jacobian singularity criterion sufficient for this taxonomy to be comprehensive at all orders.

The behavior of a system of ODEs can be visualized through the use of a phase portrait.

Solutions

Given a differential equation

${\displaystyle F\left(x,y,y',\ldots ,y^{(n)}\right)=0}$

a function u: I ⊂ R → R, where I is an interval, is called a solution or integral curve for F, if u is n-times differentiable on I, and

${\displaystyle F(x,u,u',\ \ldots ,\ u^{(n)})=0\quad x\in I.}$

Given two solutions u: J ⊂ R → R and v: I ⊂ R → R, u is called an extension of v if I ⊂ J and

${\displaystyle u(x)=v(x)\quad x\in I.\,}$

A solution that has no extension is called a maximal solution. A solution defined on all of R is called a global solution.

A general solution of an nth-order equation is a solution containing n arbitrary independent constants of integration. A particular solution is derived from the general solution by setting the constants to particular values, often chosen to fulfill set 'initial conditions or boundary conditions'.^[18] A singular solution is a solution that cannot be obtained by assigning definite values to the arbitrary constants in the general solution.^[19]

In the context of linear ODE, the terminology particular solution can also refer to any solution of the ODE (not necessarily satisfying the initial conditions), which is then added to the homogeneous solution (a general solution of the homogeneous ODE), which then forms a general solution of the original ODE. This is the terminology used in the guessing method section in this article, and is frequently used when discussing the method of undetermined coefficients and variation of parameters.

Solutions of finite duration

For non-linear autonomous ODEs it is possible under some conditions to develop solutions of finite duration,^[20] meaning here that from its own dynamics, the system will reach the value zero at an ending time and stays there in zero forever after. These finite-duration solutions can't be analytical functions on the whole real line, and because they will be non-Lipschitz functions at their ending time, they are not included in the uniqueness theorem of solutions of Lipschitz differential equations.

As example, the equation:

${\displaystyle y'=-{\text{sgn}}(y){\sqrt {|y|}},\,\,y(0)=1}$

Admits the finite duration solution:

${\displaystyle y(x)={\frac {1}{4}}\left(1-{\frac {x}{2}}+\left|1-{\frac {x}{2}}\right|\right)^{2}}$

Singular solutions

The theory of singular solutions of ordinary and partial differential equations was a subject of research from the time of Leibniz, but only since the middle of the nineteenth century has it received special attention. A valuable but little-known work on the subject is that of Houtain (1854). Darboux (from 1873) was a leader in the theory, and in the geometric interpretation of these solutions he opened a field worked by various writers, notably Casorati and Cayley. To the latter is due (1872) the theory of singular solutions of differential equations of the first order as accepted circa 1900.

Reduction to quadratures

The primitive attempt in dealing with differential equations had in view a reduction to quadratures. As it had been the hope of eighteenth-century algebraists to find a method for solving the general equation of the nth degree, so it was the hope of analysts to find a general method for integrating any differential equation. Gauss (1799) showed, however, that complex differential equations require complex numbers. Hence, analysts began to substitute the study of functions, thus opening a new and fertile field. Cauchy was the first to appreciate the importance of this view. Thereafter, the real question was no longer whether a solution is possible by means of known functions or their integrals, but whether a given differential equation suffices for the definition of a function of the independent variable or variables, and, if so, what are the characteristic properties.

Fuchsian theory

Two memoirs by Fuchs^[21] inspired a novel approach, subsequently elaborated by Thomé and Frobenius. Collet was a prominent contributor beginning in 1869. His method for integrating a non-linear system was communicated to Bertrand in 1868. Clebsch (1873) attacked the theory along lines parallel to those in his theory of Abelian integrals. As the latter can be classified according to the properties of the fundamental curve that remains unchanged under a rational transformation, Clebsch proposed to classify the transcendent functions defined by differential equations according to the invariant properties of the corresponding surfaces f = 0 under rational one-to-one transformations.

Lie's theory

From 1870, Sophus Lie's work put the theory of differential equations on a better foundation. He showed that the integration theories of the older mathematicians can, using Lie groups, be referred to a common source, and that ordinary differential equations that admit the same infinitesimal transformations present comparable integration difficulties. He also emphasized the subject of transformations of contact.

Lie's group theory of differential equations has been certified, namely: (1) that it unifies the many ad hoc methods known for solving differential equations, and (2) that it provides powerful new ways to find solutions. The theory has applications to both ordinary and partial differential equations.^[22]

A general solution approach uses the symmetry property of differential equations, the continuous infinitesimal transformations of solutions to solutions (Lie theory). Continuous group theory, Lie algebras, and differential geometry are used to understand the structure of linear and non-linear (partial) differential equations for generating integrable equations, to find its Lax pairs, recursion operators, Bäcklund transform, and finally finding exact analytic solutions to DE.

Symmetry methods have been applied to differential equations that arise in mathematics, physics, engineering, and other disciplines.

Sturm–Liouville theory

Sturm–Liouville theory is a theory of a special type of second order linear ordinary differential equation. Their solutions are based on eigenvalues and corresponding eigenfunctions of linear operators defined via second-order homogeneous linear equations. The problems are identified as Sturm-Liouville Problems (SLP) and are named after J.C.F. Sturm and J. Liouville, who studied them in the mid-1800s. SLPs have an infinite number of eigenvalues, and the corresponding eigenfunctions form a complete, orthogonal set, which makes orthogonal expansions possible. This is a key idea in applied mathematics, physics, and engineering.^[23] SLPs are also useful in the analysis of certain partial differential equations.

Local existence and uniqueness theorem simplified

The theorem can be stated simply as follows.^[25] For the equation and initial value problem:

$${\displaystyle y'=F(x,y)\,,\quad y_{0}=y(x_{0})}$$

if F and ∂F/∂y are continuous in a closed rectangle

$${\displaystyle R=[x_{0}-a,x_{0}+a]\times [y_{0}-b,y_{0}+b]}$$

in the x-y plane, where a and b are real (symbolically: a, b ∈ R) and × denotes the Cartesian product, square brackets denote closed intervals, then there is an interval

$${\displaystyle I=[x_{0}-h,x_{0}+h]\subset [x_{0}-a,x_{0}+a]}$$

for some h ∈ R where the solution to the above equation and initial value problem can be found. That is, there is a solution and it is unique. Since there is no restriction on F to be linear, this applies to non-linear equations that take the form F(x, y), and it can also be applied to systems of equations.

Global uniqueness and maximum domain of solution

When the hypotheses of the Picard–Lindelöf theorem are satisfied, then local existence and uniqueness can be extended to a global result. More precisely:^[26]

For each initial condition (x₀, y₀) there exists a unique maximum (possibly infinite) open interval

${\displaystyle I_{\max }=(x_{-},x_{+}),x_{\pm }\in \mathbb {R} \cup \{\pm \infty \},x_{0}\in I_{\max }}$

such that any solution that satisfies this initial condition is a restriction of the solution that satisfies this initial condition with domain ${\displaystyle I_{\max }}$.

In the case that ${\displaystyle x_{\pm }\neq \pm \infty }$, there are exactly two possibilities

• explosion in finite time: ${\displaystyle \limsup _{x\to x_{\pm }}\|y(x)\|\to \infty }$
• leaves domain of definition: ${\displaystyle \lim _{x\to x_{\pm }}y(x)\ \in \partial {\bar {\Omega }}}$

where Ω is the open set in which F is defined, and ${\displaystyle \partial {\bar {\Omega }}}$ is its boundary.

Note that the maximum domain of the solution

• is always an interval (to have uniqueness)
• may be smaller than ${\displaystyle \mathbb {R} }$
• may depend on the specific choice of (x₀, y₀).

Example.

${\displaystyle y'=y^{2}}$

This means that F(x, y) = y², which is C¹ and therefore locally Lipschitz continuous, satisfying the Picard–Lindelöf theorem.

Even in such a simple setting, the maximum domain of solution cannot be all ${\displaystyle \mathbb {R} }$ since the solution is

${\displaystyle y(x)={\frac {y_{0}}{(x_{0}-x)y_{0}+1}}}$

which has maximum domain:

${\displaystyle {\begin{cases}\mathbb {R} &y_{0}=0\\[4pt]\left(-\infty ,x_{0}+{\frac {1}{y_{0}}}\right)&y_{0}>0\\[4pt]\left(x_{0}+{\frac {1}{y_{0}}},+\infty \right)&y_{0}<0\end{cases}}}$

This shows clearly that the maximum interval may depend on the initial conditions. The domain of y could be taken as being ${\displaystyle \mathbb {R} \setminus (x_{0}+1/y_{0}),}$ but this would lead to a domain that is not an interval, so that the side opposite to the initial condition would be disconnected from the initial condition, and therefore not uniquely determined by it.

The maximum domain is not ${\displaystyle \mathbb {R} }$ because

${\displaystyle \lim _{x\to x_{\pm }}\|y(x)\|\to \infty ,}$

which is one of the two possible cases according to the above theorem.

Reduction to a first-order system

Any explicit differential equation of order n,

${\displaystyle F\left(x,y,y',y'',\ \ldots ,\ y^{(n-1)}\right)=y^{(n)}}$

can be written as a system of n first-order differential equations by defining a new family of unknown functions

${\displaystyle y_{i}=y^{(i-1)}.\!}$

for i = 1, 2, ..., n. The n-dimensional system of first-order coupled differential equations is then

${\displaystyle {\begin{array}{rcl}y_{1}'&=&y_{2}\\y_{2}'&=&y_{3}\\&\vdots &\\y_{n-1}'&=&y_{n}\\y_{n}'&=&F(x,y_{1},\ldots ,y_{n}).\end{array}}}$

more compactly in vector notation:

${\displaystyle \mathbf {y} '=\mathbf {F} (x,\mathbf {y} )}$

where

${\displaystyle \mathbf {y} =(y_{1},\ldots ,y_{n}),\quad \mathbf {F} (x,y_{1},\ldots ,y_{n})=(y_{2},\ldots ,y_{n},F(x,y_{1},\ldots ,y_{n})).}$

Separable equations

Differential equation	Solution method	General solution
First-order, separable in x and y (general case, see below for special cases)[27] ${\displaystyle {\begin{aligned}P_{1}(x)Q_{1}(y)+P_{2}(x)Q_{2}(y)\,{\frac {dy}{dx}}&=0\\P_{1}(x)Q_{1}(y)\,dx+P_{2}(x)Q_{2}(y)\,dy&=0\end{aligned}}}$	Separation of variables (divide by P2Q1).	${\displaystyle \int ^{x}{\frac {P_{1}(\lambda )}{P_{2}(\lambda )}}\,d\lambda +\int ^{y}{\frac {Q_{2}(\lambda )}{Q_{1}(\lambda )}}\,d\lambda =C}$
First-order, separable in x[25] ${\displaystyle {\begin{aligned}{\frac {dy}{dx}}&=F(x)\\dy&=F(x)\,dx\end{aligned}}}$	Direct integration.	${\displaystyle y=\int ^{x}F(\lambda )\,d\lambda +C}$
First-order, autonomous, separable in y[25] ${\displaystyle {\begin{aligned}{\frac {dy}{dx}}&=F(y)\\dy&=F(y)\,dx\end{aligned}}}$	Separation of variables (divide by F).	${\displaystyle x=\int ^{y}{\frac {d\lambda }{F(\lambda )}}+C}$
First-order, separable in x and y[25] ${\displaystyle {\begin{aligned}P(y){\frac {dy}{dx}}+Q(x)&=0\\P(y)\,dy+Q(x)\,dx&=0\end{aligned}}}$	Integrate throughout.	${\displaystyle \int ^{y}P(\lambda )\,d\lambda +\int ^{x}Q(\lambda )\,d\lambda =C}$

General first-order equations

Differential equation	Solution method	General solution
First-order, homogeneous[25] ${\displaystyle {\frac {dy}{dx}}=F\left({\frac {y}{x}}\right)}$	Set y = ux, then solve by separation of variables in u and x.	${\displaystyle \ln(Cx)=\int ^{y/x}{\frac {d\lambda }{F(\lambda )-\lambda }}}$
First-order, separable[27] ${\displaystyle {\begin{aligned}yM(xy)+xN(xy)\,{\frac {dy}{dx}}&=0\\yM(xy)\,dx+xN(xy)\,dy&=0\end{aligned}}}$	Separation of variables (divide by xy).	${\displaystyle \ln(Cx)=\int ^{xy}{\frac {N(\lambda )\,d\lambda }{\lambda [N(\lambda )-M(\lambda )]}}}$ If N = M, the solution is xy = C.
Exact differential, first-order[25] ${\displaystyle {\begin{aligned}M(x,y){\frac {dy}{dx}}+N(x,y)&=0\\M(x,y)\,dy+N(x,y)\,dx&=0\end{aligned}}}$ where ${\displaystyle {\frac {\partial M}{\partial y}}={\frac {\partial N}{\partial x}}}$	Integrate throughout.	${\displaystyle {\begin{aligned}F(x,y)&=\int ^{x}M(\lambda ,y)\,d\lambda +\int ^{y}Y(\lambda )\,d\lambda \\&=\int ^{y}N(x,\lambda )\,d\lambda +\int ^{x}X(\lambda )\,d\lambda =C\end{aligned}}}$ where $${\displaystyle Y(y)=N(x,y)-{\frac {\partial }{\partial y}}\int ^{x}M(\lambda ,y)\,d\lambda }$$ and $${\displaystyle X(x)=M(x,y)-{\frac {\partial }{\partial x}}\int ^{y}N(x,\lambda )\,d\lambda }$$
Inexact differential, first-order[25] ${\displaystyle {\begin{aligned}M(x,y){\frac {dy}{dx}}+N(x,y)&=0\\M(x,y)\,dy+N(x,y)\,dx&=0\end{aligned}}}$ where ${\displaystyle {\frac {\partial M}{\partial y}}\neq {\frac {\partial N}{\partial x}}}$	Integration factor μ(x, y) satisfying ${\displaystyle {\frac {\partial (\mu M)}{\partial y}}={\frac {\partial (\mu N)}{\partial x}}}$	If μ(x, y) can be found in a suitable way, then ${\displaystyle {\begin{aligned}F(x,y)=&\int ^{x}\mu (\lambda ,y)M(\lambda ,y)\,d\lambda +\int ^{y}Y(\lambda )\,d\lambda \\=&\int ^{y}\mu (x,\lambda )N(x,\lambda )\,d\lambda +\int ^{x}X(\lambda )\,d\lambda =C\end{aligned}}}$ where $${\displaystyle Y(y)=N(x,y)-{\frac {\partial }{\partial y}}\int ^{x}\mu (\lambda ,y)M(\lambda ,y)\,d\lambda }$$ and $${\displaystyle X(x)=M(x,y)-{\frac {\partial }{\partial x}}\int ^{y}\mu (x,\lambda )N(x,\lambda )\,d\lambda }$$

General second-order equations

Differential equation	Solution method	General solution
Second-order, autonomous[28] ${\displaystyle {\frac {d^{2}y}{dx^{2}}}=F(y)}$	Multiply both sides of equation by 2dy/dx, substitute ${\displaystyle 2{\frac {dy}{dx}}{\frac {d^{2}y}{dx^{2}}}={\frac {d}{dx}}\left({\frac {dy}{dx}}\right)^{2}}$, then integrate twice.	${\displaystyle x=\pm \int ^{y}{\frac {d\lambda }{\sqrt {2\int ^{\lambda }F(\varepsilon )\,d\varepsilon +C_{1}}}}+C_{2}}$

Linear to the nth order equations

Differential equation	Solution method	General solution
First-order, linear, inhomogeneous, function coefficients[25] ${\displaystyle {\frac {dy}{dx}}+P(x)y=Q(x)}$	Integrating factor: ${\displaystyle e^{\int ^{x}P(\lambda )\,d\lambda }.}$	Armour formula: ${\displaystyle y=e^{-\int ^{x}P(\lambda )\,d\lambda }\left[\int ^{x}e^{\int ^{\lambda }P(\varepsilon )\,d\varepsilon }Q(\lambda )\,d\lambda +C\right]}$
Second-order, linear, inhomogeneous, function coefficients ${\displaystyle {\frac {d^{2}y}{dx^{2}}}+2p(x){\frac {dy}{dx}}+\left(p(x)^{2}+p'(x)\right)y=q(x)}$	Integrating factor: ${\displaystyle e^{\int ^{x}P(\lambda )\,d\lambda }}$	${\displaystyle y=e^{-\int ^{x}P(\lambda )\,d\lambda }\left[\int ^{x}\left(\int ^{\xi }e^{\int ^{\lambda }P(\varepsilon )\,d\varepsilon }Q(\lambda )\,d\lambda \right)d\xi +C_{1}x+C_{2}\right]}$
Second-order, linear, inhomogeneous, constant coefficients[29] ${\displaystyle {\frac {d^{2}y}{dx^{2}}}+b{\frac {dy}{dx}}+cy=r(x)}$	Complementary function yc: assume yc = eαx, substitute and solve polynomial in α, to find the linearly independent functions ${\displaystyle e^{\alpha _{j}x}}$. Particular integral yp: in general the method of variation of parameters, though for very simple r(x) inspection may work.[25]	${\displaystyle y=y_{c}+y_{p}}$ If b2 > 4c, then ${\displaystyle y_{c}=C_{1}e^{-{\frac {x}{2}}\,\left(b+{\sqrt {b^{2}-4c}}\right)}+C_{2}e^{-{\frac {x}{2}}\,\left(b-{\sqrt {b^{2}-4c}}\right)}}$ If b2 = 4c, then ${\displaystyle y_{c}=(C_{1}x+C_{2})e^{-{\frac {bx}{2}}}}$ If b2 < 4c, then ${\displaystyle y_{c}=e^{-{\frac {bx}{2}}}\left[C_{1}\sin \left(x\,{\frac {\sqrt {4c-b^{2}}}{2}}\right)+C_{2}\cos \left(x\,{\frac {\sqrt {4c-b^{2}}}{2}}\right)\right]}$
nth-order, linear, inhomogeneous, constant coefficients[29] ${\displaystyle \sum _{j=0}^{n}b_{j}{\frac {d^{j}y}{dx^{j}}}=r(x)}$	Complementary function yc: assume yc = eαx, substitute and solve polynomial in α, to find the linearly independent functions ${\displaystyle e^{\alpha _{j}x}}$. Particular integral yp: in general the method of variation of parameters, though for very simple r(x) inspection may work.[25]	${\displaystyle y=y_{c}+y_{p}}$ Since αj are the solutions of the polynomial of degree n: ${\textstyle \prod _{j=1}^{n}(\alpha -\alpha _{j})=0}$, then: for αj all different, $${\displaystyle y_{c}=\sum _{j=1}^{n}C_{j}e^{\alpha _{j}x}}$$ for each root αj repeated kj times, $${\displaystyle y_{c}=\sum _{j=1}^{n}\left(\sum _{\ell =1}^{k_{j}}C_{j,\ell }x^{\ell -1}\right)e^{\alpha _{j}x}}$$ for some αj complex, then setting α = χj + iγj, and using Euler's formula, allows some terms in the previous results to be written in the form $${\displaystyle C_{j}e^{\alpha _{j}x}=C_{j}e^{\chi _{j}x}\cos(\gamma _{j}x+\varphi _{j})}$$ where ϕj is an arbitrary constant (phase shift).