%
\section{Time Integration}

This directory consists of the following primary files:

\begin{center}
\begin{tabular}{|c | c | } \hline
TimeIntegrationScheme.h & TimeIntegrationScheme.cpp \\ \hline 
TimeIntegrationGLM.h & TimeIntegrationGLM.cpp \\ \hline
*TimeIntegrationSchemes.h & Specific GLM schemes, e.g. AB, RK, IMEX, etc. \\ \hline
TimeIntegrationFIT.h & TimeIntegrationFIT.cpp \\ \hline
TimeIntegrationGEM.h & TimeIntegrationGEM.cpp \\ \hline
TimeIntegrationSDC.h & TimeIntegrationSDC.cpp \\ \hline
\end{tabular}
\end{center}

The original time-stepping interface (found in the parent class
TimeIntegrationScheme) where implemented by Vos et al. \cite{Vostime}
and a detailed explanation of the mathematics and computer science
concepts are provided there.  After the original implementation, the
time-stepping routines were refactored into a factory pattern (found
in the child class TimeIntegrationGLM with specific GLM schemes
covering both traditional and exponential schemes (compartmentalize in
the *TimeIntegrationSchemes classes). Each GLM scheme sets up one or
more (linear multistep methods) phases using the
TimeIntegrationAlgorithmGLM class, which is where the GLM algorithm is
performed using the helper class TimeIntegrationSolutionGLM.

More recently Extrapolation, Spectral-Deferred-Correction, and 
Fractional-in-time schemes have been added as sibbling classes to the 
GLM class (found in the child classes
TimeIntegrationGEM, TimeIntegrationSDC, and TimeIntegrationFIT, 
respectively). The Extrapolation, Spectral-Deferred-Correction, and 
the Fractional-in-time schemes make use of their own separate algorithm.


\subsection{General Linear Methods}
\label{subsectionGeneralLinearMethods}
General linear methods (GLMs) can be considered as the generalization
of a broad range of different numerical methods for ordinary
differential equations.  They were introduced by Butcher and they
provide a unified formulation for traditional methods such as the
Runge-Kutta methods and the linear multi-step methods such as
Adams-Bashforth.  From an implementation point of view, all these
numerical methods can be abstracted similarly and allows a high level
of generality which is the case in {\nek}. For background information
about general linear methods, see \cite{Bu06}.

\subsubsection{Introduction}
The standard initial value problem can written in the form
\begin{align*}
\frac{d\boldsymbol{y}}{dt} = \boldsymbol{f}(t,\boldsymbol{y}),\quad
\boldsymbol{y}(t_0)=\boldsymbol{y}_0
\end{align*}
where $\boldsymbol{y}$ is a vector containing the variable (or an
array of array containing the variables).

In the formulation of general linear methods, it is more convenient to consider
the ODE in autonomous form, i.e.
\begin{align*}
\frac{d\boldsymbol{\hat{y}}}{dt}=\boldsymbol{\hat{f}}(\boldsymbol{\hat{y}}),\quad
\boldsymbol{\hat{y}}(t_0)= \boldsymbol{\hat{y}}_0.
\end{align*}

\subsubsection{Formulation}
Suppose the governing differential equation is given in autonomous form, the
$n^{th}$ step of the GLM comprising
\begin{itemize}
\item $r$ steps (as in a multi-step method)
\item $s$ stages (as in a Runge-Kutta method)
\end{itemize}
is formulated as:
\begin{align*}
\boldsymbol{Y}_i &= \Delta
t\sum_{j=0}^{s-1}a_{ij}\boldsymbol{F}_j+\sum_{j=0}^{r-1}u_{ij}\boldsymbol{\hat{y}}_{j}^{[n-1]},
\qquad i=0,1,\ldots,s-1 \\
\boldsymbol{\hat{y}}_{i}^{[n]}&=\Delta
t\sum_{j=0}^{s-1}b_{ij}\boldsymbol{F}_j+\sum_{j=0}^{r-1}v_{ij}
\boldsymbol{\hat{y}}_{j}^{[n-1]}, \qquad i=0,1,\ldots,r-1
\end{align*}
where $\boldsymbol{Y}_i$ are referred to as the stage values and
$\boldsymbol{F}_j$ as the stage derivatives. Both quantities are
related by the differential equation:
\begin{align*}
\boldsymbol{F}_i = \boldsymbol{\hat{f}}(\boldsymbol{Y}_i).
\end{align*}

The matrices $A=[a_{ij}]$, $U=[u_{ij}]$,
$B=[b_{ij}]$, $V=[v_{ij}]$ are characteristic of a
specific method. Each scheme can then be uniquely defined by a partitioned
$(s+r)\times(s+r)$ matrix
\begin{align*}
\left[
\begin{array}{cc}
  A & U\\
  B & V \end{array}\right]
\end{align*}


\subsubsection{Matrix notation}
Adopting the notation:
\begin{align*}
\boldsymbol{\hat{y}}^{[n-1]}= \left[\begin{array}{c}
\boldsymbol{\hat{y}}^{[n-1]}_0\\
\boldsymbol{\hat{y}}^{[n-1]}_1\\
\vdots\\
\boldsymbol{\hat{y}}^{[n-1]}_{r-1}
\end{array}\right],\quad \boldsymbol{\hat{y}}^{[n]}= \left[\begin{array}{c}
\boldsymbol{\hat{y}}^{[n]}_0\\
\boldsymbol{\hat{y}}^{[n]}_1\\
\vdots\\
\boldsymbol{\hat{y}}^{[n]}_{r-1}
\end{array}\right],\quad \boldsymbol{Y}= \left[\begin{array}{c}
\boldsymbol{Y}_0\\
\boldsymbol{Y}_1\\
\vdots\\
\boldsymbol{Y}_{s-1}
\end{array}\right],\quad \boldsymbol{F}= \left[\begin{array}{c}
\boldsymbol{F}_0\\
\boldsymbol{F}_1\\
\vdots\\
\boldsymbol{F}_{s-1}
\end{array}\right]\quad
\end{align*}
the general linear method can be written more compactly in the following form:
\begin{align*}
\left[ \begin{array}{c} \boldsymbol{Y}\\
\boldsymbol{\hat{y}}^{[n]}
\end{array}\right] = \left[ \begin{array}{cc} A\otimes I_N & U\otimes I_N \\
B\otimes I_N & V\otimes I_N \end{array}\right] \left[ \begin{array}{c} \Delta
t\boldsymbol{F}\\
\boldsymbol{\hat{y}}^{[n-1]}
\end{array}\right]
\end{align*}
where $I_N$ is the identity matrix of dimension $N\times N$.


\subsubsection{General Linear Methods in Nektar++}
Although the GLM is essentially presented for ODE's in its autonomous form, in
Nektar++ it will be used to solve ODE's formulated in non-autonomous form.
Given the ODE,
\begin{align*}
\frac{d\boldsymbol{y}}{dt}=\boldsymbol{f}(t,\boldsymbol{y}),\quad
\boldsymbol{y}(t_0)=\boldsymbol{y}_0
\end{align*}
a single step of GLM can then be evaluated in the following way:
\begin{itemize}
\item \textbf{input}

$\boldsymbol{y}^{[n-1]}$, \emph{i.e.} the $r$
    subvectors comprising $\boldsymbol{y}^{[n-1]}_i$ -
    $t^{[n-1]}$, \emph{i.e.} the equivalent of
    $\boldsymbol{y}^{[n-1]}$ for the time variable $t$.

\item \textbf{step 1}: The stage values $\boldsymbol{Y}_i$,
$\boldsymbol{T}_i$ and the stage derivatives
$\boldsymbol{F}_i$ are calculated through the relations:
\begin{enumerate}
\item $\boldsymbol{Y}_i = \Delta
    t\sum_{j=0}^{s-1}a_{ij}\boldsymbol{F}_j+\sum_{j=0}^{r-1}u_{ij}\boldsymbol{y}_{j}^{[n-1]},
    \qquad i=0,1,\ldots,s-1$
\item $T_i\ =\ \Delta
    t\sum_{j=0}^{s-1}a_{ij}+\sum_{j=0}^{r-1}u_{ij}t_{j}^{[n-1]}, \qquad
    i=0,1,\ldots,s-1$
\item $\boldsymbol{F}_i\ =\
    f(T_i,\boldsymbol{Y}_i), \qquad i=0,1,\ldots,s-1$
\end{enumerate}

\item \textbf{step 2}: The approximation at the new time level
$\boldsymbol{y}^{[n]}$ is calculated as a linear combination of the
stage derivatives $\boldsymbol{F}_i$ and the input vector
$\boldsymbol{y}^{[n-1]}$. In addition, the time vector
$t^{[n]}$ is also updated

\begin{enumerate}
\item $\boldsymbol{y}_{i}^{[n]} = \Delta
    t\sum_{j=0}^{s-1}b_{ij}\boldsymbol{F}_j+\sum_{j=0}^{r-1}v_{ij}\boldsymbol{y}_{j}^{[n-1]},
    \qquad i=0,1,\ldots,r-1$
\item $t_{i}^{[n]}\ =\ \Delta
    t\sum_{j=0}^{s-1}b_{ij}+\sum_{j=0}^{r-1}v_{ij}t_{j}^{[n-1]}, \qquad
    i=0,1,\ldots,r-1$
\end{enumerate}

\item \textbf{output}
\begin{enumerate}
\item $\boldsymbol{y}^{[n]}$, i.e. the $r$ subvectors
    comprising $\boldsymbol{y}^{[n]}_i$.
    $\boldsymbol{y}^{[n]}_0$ corresponds to the actual approximation
    at the new time level.
\item $t^{[n]}$ where $t^{[n]}_0$ is equal to the new time
    level $t+\Delta t$.
\end{enumerate}
\end{itemize}

For a detailed description of the formulation and a deeper insight of the
numerical method see \cite{Vostime}.

\subsubsection{Types of GLM time Integration Schemes}
Nektar++ contains various classes and methods which implement the
concept of GLMs. This toolbox is capable of numerically solving the
generalised ODE using a broad range of different time-stepping
methods. We distinguish the following types of general linear methods:
\begin{itemize}
\item \textbf{Formally Explicit Methods}: These types of methods are
  considered explicit from an ODE point of view. They are
  characterised by a lower triangular coefficient matrix $A$, ''i.e.''
  $a_{ij} = 0$ for $j\geq i$. To avoid confusion, we make a further
  distinction:
  \begin{itemize}
    \item \textbf{direct explicit method}: Only forward operators are
      required.
    \item \textbf{indirect explicit method}: The inverse operator is
      required.
  \end{itemize}
  \item \textbf{Diagonally Implicit Methods}: Compared to explicit
    methods, the coefficient matrix $A$ has now non-zero entries on
    the diagonal.  This means that each stage value depend on the
    stage derivative at the same stage, requiring an implicit
    step. However, the calculation of the different stage values is
    still uncoupled. Best known are the DIRK schemes.
  \item \textbf{IMEX schemes}: These schemes support the concept of
    being able to split right hand forcing term into an explicit and
    implicit component. This is useful in advection diffusion type
    problems where the advection is handled explicitly and the
    diffusion is handled implicit.
  \item \textbf{Fully Implicit Methods}: The coefficient matrix has a
    non-zero upper triangular part. The calculation of all stages
    values is fully coupled.
  \item \textbf{Exponential Methods}: A forward Euler method has been
    extended to the exponential setting. These types of methods are
    considered explicit from an ODE point of view. They are
    characterized by a coefficient matrix of exponential values for
    each variable. Two first order schemes have been implemented:
  \begin{itemize}
    \item \textbf{Lawson-Euler explicit method}: $y_n =
      \varphi_0(z)N(y_{n-1},t_{n-1}) + \varphi_0(z)y_{n-1}$
    \item \textbf{N{\o}rsett-Euler explicit method}: $y_n =
      \varphi_1(z)N(y_{n-1},t_{n-1}) + \varphi_0(z)y_{n-1}$
  \end{itemize}
  where $\varphi_0(z) = e^{z}$ for $k = 0$, $\varphi_{k}(z) =
  \frac{\varphi_{k-1}(z) - \varphi_{k-1}(0)}{z}$ for $k \geq 1$.
  \end{itemize}
  While implemented, exponential schemes are not currently seeded.

The aim in {\nek} is to fully support the first three types of GLMs.
Fully implicit methods are currently not implemented.

\subsection{Extrapolation Integration Schemes}

In addition to the GLMs {\nek} also supports extrapolation methods:
\begin{itemize}
  \item \textbf{Extrapolation Methods}: Extrapolation methods allow 
    the construction of arbitrarily high-order time-integration 
    methods based on a series for low-order approximations.
\end{itemize}

The Extrapolation methods follow the same paradigm as the GLMs, that is
both have the same parent class and as such have the same interface.

For more details about Extrapolation methods, interested readers can refer
to \cite{Deuflhard1985}.

\subsection{Spectral Deferred Correction Integration Schemes}

In addition to the GLMs {\nek} also supports spectral deferred correction 
methods:
\begin{itemize}
  \item \textbf{Spectral Deferred Correction Methods}: Spectral Deferred 
    Correction methods allow the construction of arbitrarily high-order 
    time-integration methods based on a series for low-order approximations.
\end{itemize}

The Spectral Deferred Correction methods follow the same paradigm as the 
GLMs, that is both have the same parent class and as such have the same 
interface.

For more details about Spectral Deferred Correction methods, interested
readers can refer to \cite{Dutt2000, Minion2003, Ketcheson2014}.

\subsection{Fractional-in-time Integration Schemes}

In addition to the GLMs {\nek} also supports fractional methods:
\begin{itemize}
  \item \textbf{Fractional-in-time Methods}: A fast convolution
    algorithm for computing solutions to (Caputo) time-fractional
    differential equations. This is an explicit solver that expresses
    the solution as an integral over a Talbot curve, which is
    discretized with quadrature. First-order quadrature is currently
    implemented (Soon be expanded to forth order).
\end{itemize}

The Fractional methods follow the same paradigm as the GLMs, that is
both have the same parent class and as such have the same interface.

\subsection{Usage}
The goal of abstracting the concept of general linear methods and
fractional methods is to provide users with a single interface for
time-stepping, independent of the chosen method. The classes tree
allows the user to numerically integrate ODE's using high-order
complex schemes, as if it was done using the Forward-Euler method.
Switching between time-stepping schemes is as easy as changing a
parameter in an input file.

In the already implemented solvers the time-integration schemes have
been set up according to the nature of the equations.  For example the
incompressible Navier-Stokes equations solver allows the use of three
different Implicit-Explicit time-schemes if solving the equations with
a splitting-scheme.  This is because this kind of scheme has an
explicit and an implicit operator that combined solve the ODE's
system.

Once aware of the problem's nature and implementation, the user can
easily switch between some (depending on the problem) of the following
time-integration schemes:

\begin{center}
\scriptsize
\begin{tabular}{p{2.5cm}p{4cm}p{8cm}}
\toprule
Method         & Variant & Order / Free Parameters \\
ForwardEuler   &      & Forward-Euler scheme\\
BackwardEuler  &      & Backward-Euler scheme\\

AdamsBashforth &      & Adams-Bashforth Forward multi-step scheme of order 1-4\\

AdamsMoulton   &      & Adams-Moulton Forward multi-step scheme of order 1-4\\

BDFImplicit    &      & BDF multi-step implicit scheme of order 1-4\\

RungeKutta     &      & Runge-Kutta multi-stage explicit scheme of
                        order 1-5, (2 - midpoint, 3 - Ralston's, 4 - Classic)\\

RungeKutta     & SSP  & Strong Stability Preserving (SSP) variant of
                        the RungeKutta multi-stage explicit scheme of
                        order 1-3\\

DIRK           &      & Diagonally Implicit Runge-Kutta (DIRK) scheme of
                        order 2-3\\

CNAB           &      & Crank-Nicolson-Adams-Bashforth (CNAB) of order 2\\

MCNAB          &      & Modified Crank-Nicolson-Adams-Bashforth (MCNAB)
                        of order 2\\

IMEX           &      & Implicit-Explicit (IMEX) scheme using Backward
                        Different Formula Extrapolation of order 1-4\\

IMEX           & Gear & IMEX Gear variant of order 2\\

IMEX           & dirk & IMEX DIRK variant with free parameters (s, sigma), 
                        where s is the number of implicit stage schemes,
                        sigma is the number of explicit stage scheme and
                        p is the combined order of the scheme (Note: IMEX 
                        DIRK schemes should not be used with IncNavierStokesSolver).\\

               & dirk & Forward-Backward Euler, first order, free parameters (1,1)\\
               & dirk & Forward-Backward Euler, first order, free parameters (1,2)\\
               & dirk & Implicit-Explicit Midpoint, second order, free parameters (1,2)\\
               & dirk & L-stable, two stage, second order, free parameters (2,2)\\
               & dirk & L-stable, two stage, second order, free parameters (2,3)\\
               & dirk & L-stable, two stage, third order, free parameters (2,3)\\
               & dirk & L-stable, three stage, third order, free parameters (3,4)\\
               & dirk & L-stable, four stage, third order, free parameters (4,4)\\

EulerExponential & Lawson  & Exponential Euler scheme using the
                             Lawson variant of order 1 \\

                 & N{\o}rsett & Exponential Euler scheme using the
                                N{\o}rsett variant of order 1-4
                                (Higher order not yet properly seeded) \\
ExtrapolationMethod & ExplicitEuler & Extrapolation method of arbitrary order
                                      using a series of first-order explict Euler
                                      scheme\\
                    & ExplicitMidpoint & Extrapolation method of arbitrary order
                                      using a series of second-order explict midpoint
                                      scheme\\
                    & ImplicitEuler & Extrapolation method of arbitrary order
                                      using a series of first-order implicit Euler
                                      scheme\\
                    & ImplicitMidpoint & Extrapolation method of arbitrary order
                                         using a series of second-order implicit Euler
                                         scheme\\
ExplicitSDC & Equidistant & Explicit Spectral Deferred Correction method of arbitrary order
                            using equidistant quadrature points and first-order explicit
                            Euler schemes\\
            & GaussLobattoChebyshev & Explicit Spectral Deferred Correction method of arbitrary order
                                      using Gauss-Lobatto-Chebyshev quadrature points and first-order
                                      explicit Euler schemes\\
            & GaussLobattoLegendre & Explicit Spectral Deferred Correction method of arbitrary order
                                     using Gauss-Lobatto-Legendre quadrature points and first-order
                                     explicit Euler schemes\\
            & GaussRadauChebyshev & Explicit Spectral Deferred Correction method of arbitrary order
                                    using Gauss-Radau-Chebyshev quadrature points and first-order
                                    explicit Euler schemes\\
            & GaussRadauLegendre & Explicit Spectral Deferred Correction method of arbitrary order
                                   using Gauss-Radau-Legendre quadrature points and first-order
                                   explicit Euler schemes\\
ImplicitSDC & GaussRadauLegendre & Implicit Spectral Deferred Correction method of arbitrary order
                                   using Gauss-Radau-Legendre quadrature points and first-order
                                   implicit Euler schemes\\
FractionalInTime &            & Fractional-in-time scheme of order 1-4
                              (Higher order not yet properly seeded),
                              free parameters, none,
                              one (alpha),
                              two (alpha, base), or
                              six (alpha, base, nQuadPts, sigma, mu0, nu) \\
                    
\bottomrule
\end{tabular}
\end{center}

Note: in many cases the first order schemes reduce to either a forward
or backward Euler scheme.

{\nek} input file for your problem will require a string corresponding
to the time-stepping method and the order, and optionally the variant and
free parameters. In addition, parameters that define your integration
in time, the time-step and number of steps or final time. For
example:\\
\begin{lstlisting}[style=XMLStyle]
 <TIMEINTEGRATIONSCHEME>
   <METHOD> IMEX </METHOD>
   <VARIANT> DIRK </VARIANT>
   <ORDER> 2 </ORDER>
   <FREEPARAMETERS> 2 3 </FREEPARAMETERS>
 </TIMEINTEGRATIONSCHEME>

 <SOLVERINFO>
   <I PROPERTY="EQTYPE"                VALUE="UnsteadyAdvectionDiffusion"  />
   <I PROPERTY="Projection"            VALUE="DisContinuous"               />
   <I PROPERTY="AdvectionAdvancement"  VALUE="Explicit"                    />
   <I PROPERTY="AdvectionType"         VALUE="WeakDG"                      />
   <I PROPERTY="DiffusionAdvancement"  VALUE="Implicit"                    />
   <I PROPERTY="UpwindType"            VALUE="Upwind"                      />
 </SOLVERINFO>
 
 <PARAMETERS>
   <P> TimeStep      = 0.0005              </P>
   <P> NumSteps      = 200                 </P>
   <P> wavefreq      = PI                  </P>
   <P> epsilon       = 1.0                 </P>
   <P> ax            = 2.0                 </P>
   <P> ay            = 2.0                 </P>
</PARAMETERS>
\end{lstlisting}

The old schema which specified the method, variant, order, and free
parameters as a single string has been deprecated but remains
backwards compatible.


\subsection{Implementation of a time-dependent problem}
In order to implement a new solver which takes advantage of the
time-integration class in {\nek}, two main ingredients are required:
\begin{itemize}
\item A main files in which the time-integration of you ODE's system is
initialized and performed.
\item A class representing the spatial discretization of your problem, which
reduces your system of PDE's to a system of ODE's.
\end{itemize}

Your pseudo-main file, where you actually loop over the time steps, will look
like
\begin{lstlisting}[style=C++Style]
NekDouble timestep    = 0.1; 
NekDouble time        = 0.0; 
int NumSteps          = 1000;

YourClass solver(Input);
    
LibUtilities::TimeIntegrationScheme  TIME_SCHEME;
LibUtilities::TimeIntegrationSchemeOperators ODE;

ODE.DefineOdeRhs(&YourClass::YourExplicitOperatorFunction,solver);
ODE.DefineProjection(&YourClass::YourProjectionFunction,solver);
ODE.DefineImplicitSolve(&YourClass::YourImplicitOperatorFunction,solver);
    
Array<OneD, LibUtilities::TimeIntegrationSchemeSharedPtr> IntScheme;
    
LibUtilities::TimeIntegrationSolutionSharedPtr ode_solution;
    
Array<OneD, Array<OneD, NekDouble> > U;

TIME_SCHEME = LibUtilities::eForwardEuler; 
int numMultiSteps=1; 
IntScheme = Array<OneD,LibUtilities::TimeIntegrationSchemeSharedPtr>(numMultiSteps); 
LibUtilities::TimeIntegrationSchemeKey IntKey(TIME_SCHEME); 
IntScheme[0] = LibUtilities::TimeIntegrationSchemeManager()[IntKey]; 
ode_solution = IntScheme[0]->InitializeScheme(timestep,U,time,ODE);

for(int n = 0; n < NumSteps; ++n) {
    U = IntScheme[0]->TimeIntegrate(timestep,ode_solution,ODE);
}
\end{lstlisting}

We can distinguish three different sections in the code above

\subsubsection{Definitions} 
\begin{lstlisting}[style=C++Style]
NekDouble timestep    = 0.1; 
NekDouble time        = 0.0;
int NumSteps          = 1000;

YourClass equation(Input);
    
LibUtilities::TimeIntegrationScheme  TIME_SCHEME;
LibUtilities::TimeIntegrationSchemeOperators ODE;

ODE.DefineOdeRhs(&YourClass::YourExplicitOperatorFunction,equation);
ODE.DefineProjection(&YourClass::YourProjectionFunction, equation);
ODE.DefineImplicitSolve(&YourClass::YourImplicitOperatorFunction, equation);
\end{lstlisting}
In this section you define the basic parameters (like time-step, initial time,
etc.) and the time-integration objects.
The operators are not all required, it depends on the nature of your problem and
on the type of time integration schemes you want to use. In this case, the
problem has been set up to work just with Forward-Euler, then for sure you will
not need the implicit operator.
An object named \texttt{equation} has been initialized, is an object of type
\texttt{YourClass}, where your spatial discretization and the functions which
actually represent your operators are implemented. An example of this class will
be shown later in this page.

\subsubsection{Initialisations}
\begin{lstlisting}[style=C++Style]
Array<OneD,LibUtilities::TimeIntegrationSchemeSharedPtr> IntScheme;
    
LibUtilities::TimeIntegrationSolutionSharedPtr ode_solution;
    
Array<OneD, Array<OneD, NekDouble> > U;

TIME_SCHEME = LibUtilities::eForwardEuler; 
int numMultiSteps=1; 
IntScheme = Array<OneD,LibUtilities::TimeIntegrationSchemeSharedPtr>(numMultiSteps); 
LibUtilities::TimeIntegrationSchemeKey IntKey(TIME_SCHEME); 
IntScheme[0] = LibUtilities::TimeIntegrationSchemeManager()[IntKey];
ode_solution = IntScheme[0]->InitializeScheme(timestep,U,time,ODE);
\end{lstlisting}
The second part consists in the scheme initialization. In this example we set up
just Forward-Euler, but we can set up more then one time-integration scheme and
quickly switch between them from the input file.
Forward-Euler does not require any other scheme for the start-up procedure. High
order multi-step schemes may need lower-order schemes for the start up.

\subsubsection{Integration}
\begin{lstlisting}[style=C++Style]
for(int n = 0; n < NumSteps; ++n) {
    U = IntScheme[0]->TimeIntegrate(timestep,ode_solution,ODE);
}
\end{lstlisting}
The last step is the typical time-loop, where you iterate in time to get
your new solution at each time-level.
The solution at time $t^{n+1}$ is stored into vector \texttt{U} (you need
to properly initialize this vector).
\texttt{U} is an Array of Arrays, where the first dimension corresponds to
the number of variables (eg. u,v,w) and the second dimension corresponds to the
variables size (e.g. the number of modes or the number of physical points).

The variable \texttt{ODE} is an object which contains the methods. A class
representing a PDE equation (or a system of equations) must have a series of
functions representing the implicit/explicit part of the method, which
represents the reduction of the PDE's to a system of ODE's.
The spatial discretization and the definition of this method should be
implemented in \texttt{YourClass}.
\texttt{\&YourClass::YourExplicitOperatorFunction} is a functor, i.e. a pointer
to a function where the method is implemented. \texttt{equation} is a pointer
to the object, i.e. the class, where the function/method is implemented.
Here a pseudo-example of the .h file of your hypothetical class representing the
set of equations.
The implementation of the functions is meant to be in the related .cpp file.

\begin{lstlisting}[style=C++Style]
class YourCalss
{ 
public:
    YourClass(INPUT);
    
    ~YourClass(void);

    void YourExplicitOperatorFunction(
        const Array<OneD, Array<OneD, NekDouble> > & inarray,
              Array<OneD, Array<OneD, NekDouble> > & outarray,
        const NekDouble time);

    void YourProjectionFunction(
        const Array<OneD, Array<OneD, NekDouble> > & inarray,
              Array<OneD, Array<OneD, NekDouble> > & outarray, const
              NekDouble time);

    void YourImplicitOperatorFunction(
        const Array<OneD, Array<OneD, NekDouble> > & inarray,
              Array<OneD, Array<OneD, NekDouble> > & outarray, const
              NekDouble time,
        const NekDouble lambda);
    
    void InternalMethod1(...);

    NekDouble internalvariale1;

protected:
    ...

private:
    ...
};
\end{lstlisting}

 
\subsection{Strongly imposed essential boundary conditions}
Dirichlet boundary conditions can be strongly imposed by lifting the known
Dirichlet solution.
This is equivalent to decompose the approximate solution $y$ into an
known part, $y^{\mathcal{D}}$, which satisfies the Dirichlet boundary
conditions, and an unknown part, $y^{\mathcal{H}}$, which is zero on
the Dirichlet boundaries, i.e.
\begin{align*}
y = y^{\mathcal{D}} + y^{\mathcal{H}}
\end{align*}

In a Finite Element discretisation, this corresponds to splitting the solution
vector of coefficients $\boldsymbol{y}$ into the known Dirichlet
degrees of freedom $\boldsymbol{y}^{\mathcal{D}}$ and the unknown
homogeneous degrees of freedom $\boldsymbol{y}^{\mathcal{H}}$.
Ordering the known coefficients first, this corresponds to:
\begin{align*}
\boldsymbol{y} = \left[ \begin{array}{c}
\boldsymbol{y}^{\mathcal{D}} \\
\boldsymbol{y}^{\mathcal{H}} \end{array} \right]
\end{align*}

The generalised formulation of the general linear method (i.e. the introduction
of a left hand side operator) allows for an easier treatment of these types of
boundary conditions. To better appreciate this, consider the equation for the
stage values for an explicit general linear method where both the left and right
hand side operator are linear operators, i.e. they can be represented by a
matrix.
\begin{align*}
\boldsymbol{M}\boldsymbol{Y}_i = \Delta
t\sum_{j=0}^{i-1}a_{ij}\boldsymbol{L}\boldsymbol{Y}_j+\sum_{j=0}^{r-1}u_{ij}\boldsymbol{y}_{j}^{[n-1]},
\qquad i=0,1,\ldots,s-1
\end{align*}

In case of a lifted known solution, this can be written as:
\begin{align*}
\left[ \begin{array}{cc}
\boldsymbol{M}^{\mathcal{DD}} & \boldsymbol{M}^{\mathcal{DH}} \\
\boldsymbol{M}^{\mathcal{HD}} & \boldsymbol{M}^{\mathcal{HH}} \end{array} \right]
\left[ \begin{array}{c}
\boldsymbol{Y}^{\mathcal{D}}_i \\
\boldsymbol{Y}^{\mathcal{H}}_i \end{array} \right]
= \Delta t\sum_{j=0}^{i-1}a_{ij}
\left[ \begin{array}{cc}
\boldsymbol{L}^{\mathcal{DD}} & \boldsymbol{L}^{\mathcal{DH}} \\
\boldsymbol{L}^{\mathcal{HD}} & \boldsymbol{L}^{\mathcal{HH}} \end{array} \right]
\left[ \begin{array}{c}
\boldsymbol{Y}^{\mathcal{D}}_j \\
\boldsymbol{Y}^{\mathcal{H}}_j \end{array} \right]
+\sum_{j=0}^{r-1}u_{ij}
\left[ \begin{array}{c}
\boldsymbol{y}^{\mathcal{D}[n-1]}_j \\
\boldsymbol{y}^{\mathcal{H}[n-1]}_j \end{array} \right],\\
\qquad i=0,1,\ldots,s-1
\end{align*}
 
In order to calculate the stage values correctly, the explicit operator
should now be implemented to do the following:
\begin{align*}
\left[ \begin{array}{c}
\boldsymbol{b}^{\mathcal{D}} \\
\boldsymbol{b}^{\mathcal{H}} \end{array} \right]
=
\left[ \begin{array}{cc}
\boldsymbol{L}^{\mathcal{DD}} & \boldsymbol{L}^{\mathcal{DH}} \\
\boldsymbol{L}^{\mathcal{HD}} & \boldsymbol{L}^{\mathcal{HH}} \end{array} \right]
\left[ \begin{array}{c}
\boldsymbol{y}^{\mathcal{D}} \\
\boldsymbol{y}^{\mathcal{H}} \end{array} \right]
\end{align*}
    
Note that only the homogeneous part $\boldsymbol{b}^{\mathcal{H}}$ will be used
to calculate the stage values. This means essentially that only the bottom part
of the operation above, i.e.
$\boldsymbol{L}^{\mathcal{HD}}\boldsymbol{y}^{\mathcal{D}} +
\boldsymbol{L}^{\mathcal{HH}}\boldsymbol{y}^{\mathcal{H}}$ is required. However,
sometimes it might be more convenient to use/implement routines for the explicit
operator that also calculate $\boldsymbol{b}^{\mathcal{D}}$.\\
 
An implicit method should solve the system:
\begin{align*}
\left(\left[ \begin{array}{cc}
\boldsymbol{M}^{\mathcal{DD}} & \boldsymbol{M}^{\mathcal{DH}} \\
\boldsymbol{M}^{\mathcal{HD}} & \boldsymbol{M}^{\mathcal{HH}} \end{array} \right]
- \lambda \left[ \begin{array}{cc}
\boldsymbol{L}^{\mathcal{DD}} & \boldsymbol{L}^{\mathcal{DH}} \\
\boldsymbol{L}^{\mathcal{HD}} & \boldsymbol{L}^{\mathcal{HH}} \end{array} \right]\right)
\left[ \begin{array}{c}
\boldsymbol{y}^{\mathcal{D}} \\
\boldsymbol{y}^{\mathcal{H}} \end{array} \right]
=
\left[ \begin{array}{cc}
\boldsymbol{H}^{\mathcal{DD}} & \boldsymbol{H}^{\mathcal{DH}} \\
\boldsymbol{H}^{\mathcal{HD}} & \boldsymbol{H}^{\mathcal{HH}} \end{array} \right]
\left[ \begin{array}{c}
\boldsymbol{y}^{\mathcal{D}} \\
\boldsymbol{y}^{\mathcal{H}} \end{array} \right]
=
\left[ \begin{array}{c}
\boldsymbol{b}^{\mathcal{D}} \\
\boldsymbol{b}^{\mathcal{H}} \end{array} \right]
\end{align*}
    
for the unknown vector $\boldsymbol{y}$. This can be done in three steps:
\begin{itemize}
\item Set the known solution $\boldsymbol{y}^{\mathcal{D}}$
\item Calculate the modified right hand side term
 $\boldsymbol{b}^{\mathcal{H}} -
 \boldsymbol{H}^{\mathcal{HD}}\boldsymbol{y}^{\mathcal{D}}$
\item Solve the system below for the unknown
 $\boldsymbol{y}^{\mathcal{H}}$, i.e.
 $\boldsymbol{H}^{\mathcal{HH}}\boldsymbol{y}^{\mathcal{H}} =
 \boldsymbol{b}^{\mathcal{H}} -
 \boldsymbol{H}^{\mathcal{HD}}\boldsymbol{y}^{\mathcal{D}}$
\end{itemize}

\subsection{How to add a new GLM time-stepping method}

To add a new GLM time integration scheme, follow the steps below:
\begin{itemize}
\item Choose a name for the method, and create a header starting with
  the method name followed by \texttt{TimeIntegrationScheme.h}.  Add the
  header file to the \texttt{SchemeInitializor.cpp} and register the
  method name with the factory, via REGISTER.
\item Add the header file of the \texttt{CmakeLists.txt} in the parent
  directory.
\item In the header file create a new class starting with the method
  name followed by \texttt{TimeIntegrationScheme}. The class must be
  able to handle any variants, orders, and free parameters. A simple
  example is in \texttt{EulerTimeIntegrationSchemes.h} which has two
  variants, Forward and Backward. In general the class must have the
  following methods:
\begin{itemize}
  \item Constructor - creates the number of phases required and set
    the scheme data via a call to \texttt{SetupSchemeData}.
  \item \texttt{GetName} - returns the method name.
  \item \texttt{GetTimeStability} - returns time stability.
  \item \texttt{SetupSchemeData} - sets up the Butcher tableau for
    each phase of the sheme (TimeIntegrationAlgorithmGLM).
\end{itemize}

\item Add documentation for the method (especially indicating what the
  auxiliary parameters of the input and output vectors of the
  multi-step method represent)
\end{itemize}

\subsection{Examples of already implemented time stepping schemes}

What follows are some examples time-stepping schemes currently
implemented in {\nek}, to give an idea of what is required to add one
of them.

\subsubsection{Forward Euler}
\begin{align*}
\left[\begin{array}{c|c}
A & U \\
\hline
B & V
\end{array}\right] =
\left[\begin{array}{c|c}
0 & 1 \\
\hline
1 & 1
\end{array}\right],\qquad
\boldsymbol{y}^{[n]}=
\left[\begin{array}{c}
\boldsymbol{y}^{[n]}_0
\end{array}\right]=
\left[\begin{array}{c}
\boldsymbol{m}\left(t^n,\boldsymbol{y}^{n}\right)
\end{array}\right]
\end{align*}

\subsubsection{Backward Euler}
\begin{align*}
\left[\begin{array}{c|c}
A & U \\
\hline
 B & V
\end{array}\right] =
\left[\begin{array}{c|c}
1 & 1 \\
\hline
1 & 1
\end{array}\right],\qquad
\boldsymbol{y}^{[n]}=
\left[\begin{array}{c}
\boldsymbol{y}^{[n]}_0
\end{array}\right]=
\left[\begin{array}{c}
\boldsymbol{m}\left(t^n,\boldsymbol{y}^{n}\right)
\end{array}\right]
\end{align*}

\subsubsection{2$^{nd}$ order Adams-Bashforth compact form}
\begin{align*}
\left[\begin{array}{c|c}
A & U \\
\hline
B & V
\end{array}\right] =
\left[\begin{array}{c|cc}
0 & 1 & 0 \\
\hline
\frac{3}{2} & 1 & \frac{-1}{2}  \\
1 & 0 & 0
\end{array}\right],\qquad
\boldsymbol{y}^{[n]}=
\left[\begin{array}{c}
\boldsymbol{y}^{[n]}_0\\
\boldsymbol{y}^{[n]}_1\\
\end{array}\right]=
\left[\begin{array}{c}
\boldsymbol{m}\left(t^n,\boldsymbol{y}^{n}\right)\\
\Delta t \boldsymbol{l}\left(t^{n-1},\boldsymbol{y}^{n-1}\right)
\end{array}\right]
\end{align*}

\subsubsection{2$^{nd}$ order Adams-Bashforth classic form}
\begin{align*}
\left[\begin{array}{c|c}
A & U \\
\hline
B & V
\end{array}\right] =
\left[\begin{array}{c|ccc}
0 & 1 & \frac{3}{2} & \frac{-1}{2} \\
\hline
0 & 1 & \frac{3}{2} & \frac{-1}{2} \\
1 & 0 & 0 & 0 \\
0 & 0 & 1 & 0 \\
\end{array}\right],\qquad
\boldsymbol{y}^{[n]}=
\left[\begin{array}{c}
\boldsymbol{y}^{[n]}_0\\
\boldsymbol{y}^{[n]}_1\\
\boldsymbol{y}^{[n]}_2
\end{array}\right]=
\left[\begin{array}{c}
\boldsymbol{m}\left(t^n,\boldsymbol{y}^{n}\right) \\
\Delta t \boldsymbol{l}\left(t^n,\boldsymbol{y}^{n}\right)\\
\Delta t \boldsymbol{l}\left(t^{n-1},\boldsymbol{y}^{n-1}\right)
\end{array}\right]
\end{align*}

\begin{notebox}
Adams-Bashforth and other explicit methods can be writen using a
compact Bucther Tableau that reduces the number of steps. Throughout
{\nek} this compact form is utilized. This classic form is shown for
educational purposes only.
\end{notebox}

\subsubsection{1$^{st}$ order IMEX Euler-Backward/ Euler-Forward}
\begin{align*}
\left[ \begin{array}{cc|c}
A^{\mathrm{IM}} & A^{\mathrm{EM}} & U \\
\hline
B^{\mathrm{IM}} & B^{\mathrm{EM}} & V
\end{array} \right ] =
\left[\begin{array}{cc|c}
\left [ \begin{array}{c} 1 \end{array} \right ] & \left [ \begin{array}{c} 0 \end{array} \right ] &
\left [ \begin{array}{cc} 1 & 1 \end{array} \right ] \\
\hline
\left [ \begin{array}{c} 1 \\ 0 \end{array} \right ] & \left [ \begin{array}{c} 0 \\ 1 \end{array} \right ]&
\left [ \begin{array}{cc} 1 & 1 \\ 0 & 0 \end{array} \right ]
\end{array}\right] \quad \mathrm{with}\quad
\boldsymbol{y}^{[n]}=
\left[\begin{array}{c}
\boldsymbol{y}^{[n]}_0\\
\boldsymbol{y}^{[n]}_1
\end{array}\right]=
\left[\begin{array}{c}
\boldsymbol{m}\left(t^n,\boldsymbol{y}^{n}\right)\\
\Delta t \boldsymbol{l}\left ( t^n, \boldsymbol{y}^{n}\right)
\end{array}\right]
\end{align*}

\subsubsection{2$^{nd}$ order IMEX Backward Different Formula \& Extrapolation}
\begin{align*}
\left[ \begin{array}{cc|c}
A^{\mathrm{IM}} & A^{\mathrm{EM}} & U \\
\hline
B^{\mathrm{IM}} & B^{\mathrm{EM}} & V
\end{array} \right ] =
\left[\begin{array}{cc|c}
\left [ \begin{array}{c} \frac{2}{3} \end{array} \right ] & \left [ \begin{array}{c} 0 \end{array} \right ] &
\left [ \begin{array}{cccc} \frac{4}{3} & -\frac{1}{3} & \frac{4}{3} &  -\frac{2}{3} \end{array} \right ] \\
\hline
\left [ \begin{array}{c} \frac{2}{3} \\ 0 \\ 0 \\ 0 \end{array} \right ] & \left [ \begin{array}{c} 0 \\0 \\ 1 \\ 0 \end{array} \right ]&
\left [ \begin{array}{cccc} \frac{4}{3} & -\frac{1}{3} & \frac{4}{3} & -\frac{2}{3} \\ 1 & 0 & 0 & 0 \\ 0 & 0 & 0 & 0\\ 0 & 0 & 1 & 0 \end{array} \right ]
\end{array}\right]
\end{align*}
with
\begin{align*}
\boldsymbol{y}^{[n]}=
\left[\begin{array}{c}
\boldsymbol{y}^{[n]}_0\\
\boldsymbol{y}^{[n]}_1 \\
\boldsymbol{y}^{[n]}_2 \\
\boldsymbol{y}^{[n]}_3
\end{array}\right]=
\left[\begin{array}{l}
\boldsymbol{m}\left(t^n,\boldsymbol{y}^{n}\right)\\
\boldsymbol{m}\left(t^{n-1},\boldsymbol{y}^{n-1}\right)\\
\Delta t \boldsymbol{l}\left ( t^n, \boldsymbol{y}^{n}\right)\\
\Delta t \boldsymbol{l}\left ( t^{n-1}, \boldsymbol{y}^{n-1}\right)
\end{array}\right]
\end{align*}

\begin{notebox}
The first two rows are normalised so the coefficient on
$\boldsymbol{y}_n^{[n+1]}$ is one. In the standard formulation it is $3/2$.
\end{notebox}

\subsubsection{3rdorder IMEX Backward Different Formula \& Extrapolation}
\begin{align*}
  \left[ \begin{array}{cc|c}
  A^{\mathrm{IM}} & A^{\mathrm{EM}} & U \\
  \hline
  B^{\mathrm{IM}} & B^{\mathrm{EM}} & V
  \end{array} \right ] =
  \left[\begin{array}{cc|c}
   \left [ \begin{array}{c} \frac{6}{11} \end{array} \right ] & \left [ \begin{array}{c} 0 \end{array} \right ] &
   \left [ \begin{array}{cccccc} \frac{18}{11} & -\frac{9}{11} & \frac{2}{11} & \frac{18}{11} &  -\frac{18}{11} & \frac{6}{11} \end{array} \right ] \\
  \hline
   \left [ \begin{array}{c} \frac{6}{11}\\ 0 \\ 0 \\ 0 \\0 \\0 \end{array} \right ] & \left [ \begin{array}{c} 0 \\0 \\ 0 \\ 1 \\ 0 \\ 0 \end{array} \right ]&
   \left [ \begin{array}{cccccc} \frac{18}{11} & -\frac{9}{11} & \frac{2}{11} & \frac{18}{11} & -\frac{18}{11} & \frac{6}{11} \\ 1 & 0 & 0 & 0 & 0 & 0 \\ 0 & 1 & 0 & 0 & 0 & 0 \\ 0 & 0 & 0 & 0 & 0 & 0\\ 0 & 0 & 0 & 1 & 0 & 0 \\ 0 & 0 &  0 & 0 & 1 & 0 \end{array} \right ]
  \end{array}\right]
\end{align*}
with
\begin{align*}
  \boldsymbol{y}^{[n]}=
  \left[\begin{array}{l}
  \boldsymbol{y}^{[n]}_0 \\
  \boldsymbol{y}^{[n]}_1 \\
  \boldsymbol{y}^{[n]}_2 \\
  \boldsymbol{y}^{[n]}_3 \\
  \boldsymbol{y}^{[n]}_4 \\
  \boldsymbol{y}^{[n]}_5
  \end{array}\right]=
  \left[\begin{array}{l}
  \boldsymbol{m}\left(t^n,\boldsymbol{y}^{n}\right)\\
  \boldsymbol{m}\left(t^{n-1},\boldsymbol{y}^{n-1}\right)\\
  \boldsymbol{m}\left(t^{n-2},\boldsymbol{y}^{n-2}\right)\\
  \Delta t \boldsymbol{l}\left ( t^n, \boldsymbol{y}^{n}\right)\\
  \Delta t \boldsymbol{l}\left ( t^{n-1}, \boldsymbol{y}^{n-1}\right)\\
  \Delta t \boldsymbol{l}\left ( t^{n-2}, \boldsymbol{y}^{n-2}\right)
  \end{array}\right]
\end{align*}

\begin{notebox}
The first two rows are normalised so the coefficient on
$\boldsymbol{y}_n^{[n+1]}$ is one. In the standard formulation it is $11/6$.
\end{notebox}

\subsubsection{2$^{nd}$ order Adams-Moulton}
\begin{align*}
  \left[\begin{array}{c|c}
  A & U \\
  \hline
  B & V
  \end{array}\right] =
  \left[\begin{array}{c|cc}
  \frac{1}{2} & 1 & \frac{1}{2} \\
  \hline
  \frac{1}{2} & 1 & \frac{1}{2} \\
  1 & 0 & 0
  \end{array}\right],\qquad
  \boldsymbol{y}^{[n]}=
  \left[\begin{array}{c}
  \boldsymbol{y}^{[n]}_0\\
  \boldsymbol{y}^{[n]}_1\\
  \end{array}\right]=
  \left[\begin{array}{c}
  \boldsymbol{m}\left(t^n,\boldsymbol{y}^{n}\right)\\
  \Delta t \boldsymbol{l}(t^{n},\boldsymbol{y}^{n})
  \end{array}\right]
\end{align*}

\subsubsection{Midpoint Method}
\begin{align*}
  \left[\begin{array}{c|c}
  A & U \\
  \hline
  B & V
  \end{array}\right] =
  \left[\begin{array}{cc|c}
  0 & 0 & 1 \\
  \frac{1}{2} & 0 & 1 \\
  \hline
  0 & 1 & 1
  \end{array}\right],\qquad
  \boldsymbol{y}^{[n]}=
  \left[\begin{array}{c}
  \boldsymbol{y}^{[n]}_0
  \end{array}\right]=
  \left[\begin{array}{c}
  \boldsymbol{m}\left(t^n,\boldsymbol{y}^{n}\right)
  \end{array}\right]
\end{align*}

\subsubsection{RK4: the standard fourth-order Runge-Kutta scheme}
\begin{align*}
  \left[\begin{array}{c|c}
  A & U \\
  \hline
  B & V
  \end{array}\right] =
  \left[\begin{array}{cccc|c}
  0 & 0 & 0 & 0 & 1 \\
  \frac{1}{2} & 0 & 0 & 0 & 1 \\
  0 & \frac{1}{2} & 0 & 0 & 1 \\
  0 & 0 & 1 & 0 & 1 \\
  \hline
  \frac{1}{6} & \frac{1}{3} & \frac{1}{3} & \frac{1}{6} & 1
  \end{array}\right],\qquad
  \boldsymbol{y}^{[n]}=
  \left[\begin{array}{c}
  \boldsymbol{y}^{[n]}_0
  \end{array}\right]=
  \left[\begin{array}{c}
  \boldsymbol{m}\left(t^n,\boldsymbol{y}^{n}\right)
  \end{array}\right]
\end{align*}

\subsubsection{2$^{nd}$ order Diagonally Implicit Runge-Kutta (DIRK)}
\begin{align*}
  \left[\begin{array}{c|c}
  A & U \\
  \hline
  B & V
  \end{array}\right] =
  \left[\begin{array}{cc|c}
  \lambda & 0 & 1 \\
  \left(1-\lambda\right) & \lambda & 1 \\
  \hline
  \left(1-\lambda\right) & \lambda & 1
  \end{array}\right]\quad \mathrm{with}\quad \lambda=\frac{2-\sqrt{2}}{2},\qquad
  \boldsymbol{y}^{[n]}=
  \left[\begin{array}{c}
  \boldsymbol{y}^{[n]}_0
  \end{array}\right]=
  \left[\begin{array}{c}
  \boldsymbol{m}\left(t^n,\boldsymbol{y}^{n}\right)
  \end{array}\right]
\end{align*}

\subsubsection{3$^{rd}$ order Diagonally Implicit Runge-Kutta (DIRK)}
\begin{align*}
  \left[\begin{array}{c|c}
  A & U \\
  \hline
  B & V
  \end{array}\right] =
  \left[\begin{array}{ccc|c}
  \lambda & 0 & 0 & 1 \\
  \frac{1}{2}\left(1-\lambda\right) & \lambda & 0 & 1 \\
  \frac{1}{4}\left(-6\lambda^2+16\lambda-1\right) & \frac{1}{4}\left(6\lambda^2-20\lambda+5\right) & \lambda & 1 \\
  \hline
  \frac{1}{4}\left(-6\lambda^2+16\lambda-1\right) & \frac{1}{4}\left(6\lambda^2-20\lambda+5\right) & \lambda & 1
  \end{array}\right]
\end{align*}
with
\begin{align*}
  \lambda=0.4358665215,\qquad
  \boldsymbol{y}^{[n]}=
  \left[\begin{array}{c}
  \boldsymbol{y}^{[n]}_0
  \end{array}\right]=
  \left[\begin{array}{c}
  \boldsymbol{m}\left(t^n,\boldsymbol{y}^{n}\right)
  \end{array}\right]
\end{align*}

\subsubsection{3$^{rd}$ order L-stable, three stage IMEX DIRK(3,4,3)}
\tiny
\begin{align*}
  &\left[\begin{array}{cc|c}
  A^{\mathrm{IM}} & A^{\mathrm{EM}} & U \\
  \hline
  B^{\mathrm{IM}} & B^{\mathrm{EM}} & V
  \end{array}\right] =\\
  &\left[\begin{array}{cc|c}
  \left[\begin{array}{cccc}
  0 & 0 & 0 & 0 \\
  0 & \lambda & 0 & 0 \\
  0 & \frac{1}{2}\left(1-\lambda\right) & \lambda & 0 \\
  0 & \frac{1}{4}\left(-6\lambda^2+16\lambda-1\right) & \frac{1}{4}\left(6\lambda^2-20\lambda+5\right) & \lambda
  \end{array}\right] &
  \left[\begin{array}{cccc}
  0 & 0 & 0 & 0 \\
  \lambda & 0 & 0 & 0 \\
  0.3212788860 & 0.3966543747 & 0 & 0 \\
  -0.105858296 & 0.5529291479 & 0.5529291479 & 0
  \end{array}\right] &
  \left[\begin{array}{c}
  1\\
  1\\
  1\\
  1
  \end{array}\right] \\
  \hline
  \left[\begin{array}{cccc}
  0 & \frac{1}{4}\left(-6\lambda^2+16\lambda-1\right) & \frac{1}{4}\left(6\lambda^2-20\lambda+5\right) & \lambda
  \end{array}\right] &
  \left[\begin{array}{cccc}
  0 & \frac{1}{4}\left(-6\lambda^2+16\lambda-1\right) & \frac{1}{4}\left(6\lambda^2-20\lambda+5\right) & \lambda
  \end{array}\right] &
  \left[\begin{array}{c}
  1
  \end{array}\right]
  \end{array}\right]
\end{align*}
\normalsize
with
\begin{align*}
  \lambda=0.4358665215,\qquad
  \boldsymbol{y}^{[n]}=
  \left[\begin{array}{c}
  \boldsymbol{y}^{[n]}_0
  \end{array}\right]=
  \left[\begin{array}{c}
  \boldsymbol{m}\left(t^n,\boldsymbol{y}^{n}\right)
  \end{array}\right]
\end{align*}

\subsubsection{1$^{st}$ order Lawson Euler Exponential}
\begin{align*}
\left[\begin{array}{c|c}
A & U \\
\hline
B & V
\end{array}\right] =
\left[\begin{array}{c|c}
0 & 1 \\
\hline
\varphi_0(z) & \varphi_0(z)
\end{array}\right],\qquad
\boldsymbol{y}^{[n]}=
\left[\begin{array}{c}
\boldsymbol{y}^{[n]}_0
\end{array}\right]=
\left[\begin{array}{c}
\boldsymbol{m}\left(t^n,\boldsymbol{y}^{n}\right)
\end{array}\right]
\end{align*}

\subsubsection{1$^{st}$ order N{\o}rsett Euler Exponential}
\begin{align*}
\left[\begin{array}{c|c}
A & U \\
\hline
B & V
\end{array}\right] =
\left[\begin{array}{c|c}
0 & 1 \\
\hline
\varphi_1(z) & \varphi_0(z)
\end{array}\right],\qquad
\boldsymbol{y}^{[n]}=
\left[\begin{array}{c}
\boldsymbol{y}^{[n]}_0
\end{array}\right]=
\left[\begin{array}{c}
\boldsymbol{m}\left(t^n,\boldsymbol{y}^{n}\right)
\end{array}\right]
\end{align*}

\begin{notebox}
The phi functions, $\varphi_0(z)$ and $\varphi_1(z)$ are defined as
part of the discussion of GLM in Section \ref{subsectionGeneralLinearMethods}.
\end{notebox}
