In mathematics of stochastic systems, the Runge–Kutta method is a technique for the approximate numerical solution of a stochastic differential equation. It is a generalisation of the Runge–Kutta method for ordinary differential equations to stochastic differential equations (SDEs). Importantly, the method does not involve knowing derivatives the coefficient functions in the SDEs.
Consider the Itō diffusion
X
satisfying the following Itō stochastic differential equation
d
X
t
=
a
(
X
t
)
d
t
+
b
(
X
t
)
d
W
t
,
with initial condition
X
0
=
x
0
, where
W
t
stands for the Wiener process, and suppose that we wish to solve this SDE on some interval of time
[
0
,
T
]
. Then the basic Runge–Kutta approximation to the true solution
X
is the Markov chain
Y
defined as follows:
partition the interval
[
0
,
T
]
into
N
subintervals of width
δ
=
T
/
N
>
0
:
0
=
τ
0
<
τ
1
<
⋯
<
τ
N
=
T
;
set
Y
0
:=
x
0
;
recursively compute
Y
n
for
1
≤
n
≤
N
by
Y
n
+
1
:=
Y
n
+
a
(
Y
n
)
δ
+
b
(
Y
n
)
Δ
W
n
+
1
2
(
b
(
Υ
^
n
)
−
b
(
Y
n
)
)
(
(
Δ
W
n
)
2
−
δ
)
δ
−
1
/
2
,
where
Δ
W
n
=
W
τ
n
+
1
−
W
τ
n
and
Υ
^
n
=
Y
n
+
a
(
Y
n
)
δ
+
b
(
Y
n
)
δ
1
/
2
.
The random variables
Δ
W
n
are independent and identically distributed normal random variables with expected value zero and variance
δ
.
This scheme has strong order 1, meaning that the approximation error of the actual solution at a fixed time scales with the time step
δ
. It has also weak order 1, meaning that the error on the statistics of the solution scales with the time step
δ
. See the references for complete and exact statements.
The functions
a
and
b
can be time-varying without any complication. The method can be generalized to the case of several coupled equations; the principle is the same but the equations become longer.
A newer Runge—Kutta scheme also of strong order 1 straightforwardly reduces to the Improved Euler scheme for deterministic ODEs. Consider the vector stochastic process
X
→
(
t
)
∈
R
n
that satisfies the general Ito SDE
d
X
→
=
a
→
(
t
,
X
→
)
d
t
+
b
→
(
t
,
X
→
)
d
W
,
where drift
a
→
and volatility
b
→
are sufficiently smooth functions of their arguments. Given time step
h
, and given the value
X
→
(
t
k
)
=
X
→
k
, estimate
X
→
(
t
k
+
1
)
by
X
→
k
+
1
for time
t
k
+
1
=
t
k
+
h
via
K
→
1
=
h
a
→
(
t
k
,
X
→
k
)
+
(
Δ
W
k
−
S
k
h
)
b
→
(
t
k
,
X
→
k
)
,
K
→
2
=
h
a
→
(
t
k
+
1
,
X
→
k
+
K
→
1
)
+
(
Δ
W
k
+
S
k
h
)
b
→
(
t
k
+
1
,
X
→
k
+
K
→
1
)
,
X
→
k
+
1
=
X
→
k
+
1
2
(
K
→
1
+
K
→
2
)
,
where
Δ
W
k
=
h
Z
k
for normal random
Z
k
∼
N
(
0
,
1
)
;
and where
S
k
=
±
1
, each alternative chosen with probability
1
/
2
.
The above describes only one time step. Repeat this time step
(
t
m
−
t
0
)
/
h
times in order to integrate the SDE from time
t
=
t
0
to
t
=
t
m
.
The scheme integrates Stratonovich SDEs to
O
(
h
)
provided one sets
S
k
=
0
throughout (instead of choosing
±
1
).
Higher-order schemes also exist, but become increasingly complex. Rößler developed many schemes for Ito SDEs.
Whereas Komori developed schemes for Stratonovich SDEs.
