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% 4ti2 User Guide, Beginner's guide
%
% $Id: 4ti2_manual_beginner.tex 509 2015-05-12 07:43:39Z mkoeppe $
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\chapter{Beginner's guide}
\label{Chapter: Beginner's guide}

%\FourTiTwo{} is a software package that is specifically tailored for
%certain computational problems on linear spaces and integer
%lattices.
In this part, we use a few sample problems to introduce you to the
basic functionality of \FourTiTwo{}. After working through this
part, you should know about \emph{linear systems} and their
encodings in \FourTiTwo{}, and should be able to do computations
using the following functions:
\begin{itemize}
\item \File{qsolve}, \File{rays}, \File{circuits}
\item \File{zsolve}, \File{hilbert}, \File{graver}, \File{ppi}
\item \File{minimize}, \File{groebner}, \File{normalform}
\item \File{genmodel}, \File{markov}
\end{itemize}



\section{Linear systems and their encodings}

In this section you learn about the data structure \File{linear
system} and how it is specified in \FourTiTwo.

\subsection{Linear systems and integer linear systems}

In \FourTiTwo, a \emph{linear system} is defined by $d$ constraints
$Ax\sim b$ in $n$ unknowns $x$, where each constraint is either
$\leq$, $=$ or $\geq$, that is $\sim\;\in\{\leq,=,\geq\}^d$.
Moreover, one may specify sign constraints on the variables that
need to be respected in an explicit continuous/integer
representation of all solutions.

There is no particular difference in \FourTiTwo{} between a linear
system and an integer linear system. Currently, the user chooses
between one of the two by calling the appropriate functions on the
linear system.



\subsection{Specifying a linear system in \FourTiTwo}

In order to use a linear system as input, we need to specify its
parts to \FourTiTwo. As our running example, take
\[
\left(
\begin{array}{cccc}
1 & 1 & 1 & 1\\
1 & 2 & 3 & 4\\
\end{array}
\right)x
\begin{array}{c}
\leq\\
\leq\\
\end{array}
\left(
\begin{array}{c}
6\\
10\\
\end{array}
\right)
\]
with sign constraints $(1,2,2,0)$, which we will explain below.

First, we have to give our problem a project name, say
\File{PROJECT}.
\begin{itemize}
\item The matrix $A$ has to be put into the file \File{PROJECT.mat}.
\begin{myverbatim}
2 4
1 1 1 1
1 2 3 4
\end{myverbatim}
\item The relations $\sim$ then have to be specified in \File{PROJECT.rel}.
\begin{myverbatim}
1 2
< <
\end{myverbatim}
\item The right-hand side vector goes into \File{PROJECT.rhs}.
\begin{myverbatim}
1 2
6 10
\end{myverbatim}
\item And finally, the sign constraints end up in \File{PROJECT.sign}.
\begin{myverbatim}
1 4
1 2 2 0
\end{myverbatim}
\end{itemize}

{\bf Note.}
\begin{itemize}
\item The input files all have the format of a matrix, preceded by
  the matrix dimensions. As the dimensions already specify how many symbols
  have to be read, the matrix could also be given in only one line or even
  in many lines of different lengths.
\item In \FourTiTwo{} version 1.3.1 and later, all appearing numbers have to be
  integers.
\item Consequently, this implies that, at the moment, \File{qsolve}
  only supports homogeneous linear systems, that is systems with
  $b=0$, since minimal inhomogeneous solutions could have rational
  components.
\end{itemize}



\subsection{What does an explicit solution to linear systems look like?}

If the system is solved over $\R$ (using \File{qsolve}),
\FourTiTwo{} returns two sets of integer vectors:
\begin{itemize}
\item a set $H$ of support-minimal homogeneous solutions, and
\item a set $F$ defining the linear vector space the solution set
lives in.
\end{itemize}
As only \emph{homogeneous} linear systems are supported in this
version of \FourTiTwo, no list of minimal inhomogeneous solutions is
computed. Any solution $z$ of the linear system can now be written
as
\begin{equation}\label{Equation: Continuous representation}
z=\sum \alpha_j h_j+ \sum \beta_k f_k
\end{equation}
with $h_j\in H$, $f_k\in F$, and $\alpha_j\geq 0$.

If the system is solved over $\Z$ (using \File{zsolve}),
\FourTiTwo{} returns three sets of integer vectors:
\begin{itemize}
\item a set $H$ of minimal homogeneous \emph{integer} solutions,
\item a set $I$ of minimal inhomogeneous \emph{integer}
solutions, and
\item a set $F$ defining the sublattice of $\Z^n$ the solution set
lives in.
\end{itemize}
Any solution $z$ of the linear system can now be written as
\begin{equation}\label{Equation: Integer representation}
z=i+ \sum \alpha_j h_j+\sum \beta_k f_k
\end{equation}
for some $i\in I$ and with $h_j\in H$, $f_j\in F$, and
$\alpha_j\in\Z_+$.

{\bf Sign file.} Let us finally clarify what the sign file
\File{PROJECT.sign} is good for. The sign file may declare a
variable to be non-negative ($1$), to be non-positive ($-1$), or to
consider both cases independently and unite the answers ($2$). If a
nonzero sign has been assigned to a variable, the explicit
representations (\ref{Equation: Continuous representation}) and
(\ref{Equation: Integer representation}) above of a solution $z$
have to respect the sign on that variable. The default setting for
each variable is $0$ (when using \File{qsolve} and \File{zsolve}),
that is, the sign need not be respected in the explicit
representation. In our example above, the first variable is declared
to be non-negative, the second and the third one expand to $2\cdot
2=4$ orthant constraints, and the fourth variable is unconstrained.
Note, however, that \FourTiTwo{} does \emph{not} decompose the
problem internally into the four problems with sign patterns
$(1,1,1,0)$, $(1,1,-1,0)$, $(1,-1,1,0)$, and $(1,-1,-1,0)$, but
deals with them more efficiently at the same time.


\section{Brief tutorial}

\subsection{Solving linear systems over $\Z$ with \File{zsolve}}

In this example you learn about the function % s \File{qsolve} and
\File{zsolve}.

Let us have a look at the linear system
%%%% \[
%%%%   \begin{array}{rcrcrlcl}
%%%%     x & - & y & \leq & 2\\
%%%%   -3x & + & y & \leq & 1\\
%%%%     x & + & y & \geq & 1\\
%%%%       &   & y & \geq & 0\\
%%%%   \end{array}
%%%% \]
%%%% and let us solve it over $\R$, we have to create the files encoding
%%%% the linear system. Let us call our project \File{system}. Then the
%%%% input files look as follows:
%%%% \begin{center}
%%%%   \begin{tabular}{|l|l|l|l|}
%%%% \hline
%%%%     \text{ system.mat } & \text{ system.rel } & \text{ system.rhs } & \text{ system.sign }\\
%%%% \hline
%%%%   $\begin{array}{rrrr}& 3 & 2 & \\& 1 & -1\\& -3 & 1\\& 1 & 1 &\\ \end{array}$ &
%%%%   $\begin{array}{rrrrr}& 1 & 3 & \\& < &  < & > & \\ \\ \\\end{array}$ &
%%%%   $\begin{array}{rrrrr}& 1 & 3 & \\& 2 &  1 & 1 & \\ \\ \\\end{array}$ &
%%%%   $\begin{array}{rrrr}& 1 & 2 & \\& 0 &  1 & \\ \\ \\\end{array}$\\
%%%% \hline
%%%%   \end{tabular}
%%%% \end{center}
%%%% and then call
%%%% \begin{center}
%%%% {\tt ./qsolve system}
%%%% \end{center}
%%%% This call creates three files
%%%% \begin{center}
%%%%   \begin{tabular}{|l|l|l|}
%%%% \hline
%%%%     \text{ system.qinhom } & \text{ system.qhom } & \text{ system.qfree }\\
%%%% \hline
%%%%   $\begin{array}{rrrr}& 3 & 2 &\\& 0 & 1 &\\& 0 & 2 &\\& 1 & 0 &\\\end{array}$ &
%%%%   $\begin{array}{rrrr}& 2 & 2 &\\& 1 & 1 &\\& 1 & 3 & \\ \\\end{array}$ &
%%%%   $\begin{array}{rrrr}& 0 & 2 & \\ \\ \\ \\ \end{array}$\\
%%%% \hline
%%%%   \end{tabular}
%%%% \end{center}
%%%% which correspond to the explicit description of all solutions:
%%%% \begin{center}
%%%%   \begin{tabular}{cc}
%%%%     \emph{Feasible solutions} & \emph{Computed representation}\\
%%%%        \input{feasible_LP.pdf_t} & \input{solved_LP.pdf_t}    \\
%%%%   \end{tabular}
%%%% \end{center}
%%%% \[
%%%% \conv\left({1\choose 0},{2\choose 0},{0\choose 1}\right) + \cone\left({1\choose 1},{1\choose 3}\right).
%%%% \]
%%%% Note that in the second picture above, the three colored cones are
%%%% only a simplification and shall visualize the covering of the
%%%% feasible region by infinitely many shifted copies of the cone
%%%% \[
%%%% \cone\left({1\choose 1},{1\choose 3}\right),
%%%% \]
%%%% one appended to each point in
%%%% \[
%%%% \conv\left({1\choose 0},{2\choose 0},{0\choose 1}\right).
%%%% \]

%%%% Let us now turn to the integer situation, that is, let us solve the system
\[
  \begin{array}{rcrcrlcl}
    x & - & y & \leq & 2\\
  -3x & + & y & \leq & 1\\
    x & + & y & \geq & 1\\
      &   & y & \geq & 0\\
  \end{array}
\]
over $\Z$. 
%% As the linear system itself is unchanged, we can use the
%% same input files as above in order to specify the linear system.
We have to create the files encoding
the linear system. Let us call our project \File{system}. Then the
input files look as follows:
\begin{center}
  \begin{tabular}{|l|l|l|l|}
\hline
    \text{ system.mat } & \text{ system.rel } & \text{ system.rhs } & \text{ system.sign }\\
\hline
  $\begin{array}{rrrr}& 3 & 2 & \\& 1 & -1\\& -3 & 1\\& 1 & 1 &\\ \end{array}$ &
  $\begin{array}{rrrrr}& 1 & 3 & \\& < &  < & > & \\ \\ \\\end{array}$ &
  $\begin{array}{rrrrr}& 1 & 3 & \\& 2 &  1 & 1 & \\ \\ \\\end{array}$ &
  $\begin{array}{rrrr}& 1 & 2 & \\& 0 &  1 & \\ \\ \\\end{array}$\\
\hline
  \end{tabular}
\end{center}



Then % , however,
we call
\begin{center}
{\tt ./zsolve system}
\end{center}
This call creates two files
\begin{center}
  \begin{tabular}{|l|l|}
\hline
    \text{ system.zinhom } & \text{ system.zhom } %%%% & \text{ system.zfree }
    \\
\hline
  $\begin{array}{rrrr}& 4 & 2 &\\& 0 & 1 &\\& 2 & 0 &\\& 1 & 0 &\\ & 1 & 1 &\\\end{array}$ &
  $\begin{array}{rrrr}& 3 & 2 &\\& 1 & 1 &\\& 1 & 2 &\\& 1 & 3 & \\ \\\end{array}$
  %% $\begin{array}{rrrr}& 0 & 2 & \\ \\ \\ \\ \\ \end{array}$
    \\
\hline
  \end{tabular}
\end{center}
which correspond to the explicit description of all integer
solutions:
\begin{center}
  \begin{tabular}{cc}
    \emph{Feasible solutions} & \emph{Computed representation}\\
       \input{feasible.pdf_t} & \input{solved_IP.pdf_t}    \\
  \end{tabular}
\end{center}
\[
\left\{{1\choose 0},{2\choose 0},{0\choose 1},{1\choose 1}\right\} +
\monoid\left({1\choose 1},{1\choose 2},{1\choose 3}\right).
\]
Note that in the pictures above, we are only interested in the
\emph{lattice points} inside the colored regions! The full regions
are colored only for the purpose of visualizing the covering of all
feasible integer solutions by finitely many shifted copies of the
monoid
\[
\monoid\left({1\choose 1},{1\choose 2},{1\choose 3}\right).
\]

\subsection{Solving linear systems over $\Q$ with \File{qsolve}}

\File{qsolve} solves linear systems over $\Q$; however, note that it only
supports homogeneous linear systems, that is, systems with $b=0$.

%%% We should have an example here.

\begin{center}
{\tt ./qsolve system}
\end{center}
This call creates files
\begin{center}
  \begin{tabular}{|l|l|}
    \hline
    \text{ system.qhom } & \text{ system.qfree }\\
    \hline
  \end{tabular}
\end{center}

To solve an inhomogeneous system $Ax=b$, $x\geq0$, you (still) need to do some work
yourself:

\begin{enumerate}
\item Solve system $Ax-bu=0$, $x\geq 0$, $u\geq 0$ using \File{qsolve}.  
\item Keep those solutions with
  $u=0$. (These generate the recession cone (of unbounded directions).  
\item Normalize those solutions with $u>0$ to have $u=1$ (by dividing the
  vector by~$u$). Be aware that this could create rational numbers.  
\item Drop the $u$-component.
\end{enumerate}
Any solution to $Ax=b$, $x\geq 0$ can then be obtained by adding one solution
from 3.\ to a nonnegative linear combination of solutions from 2.



\subsection{Computing extreme rays and Hilbert bases}

In this example you learn about the functions \File{rays} and
\File{hilbert}.   (They are convenient versions of \File{qsolve} and
\File{zsolve} for particular cases.)

Let us consider the set of magic $3\times 3$ squares with
non-negative real entries, that is, the set of all $3\times 3$
arrays with non-negative real entries whose row sums, column sums,
and main diagonal sums all add up to the same number, the magic
constant of the square. \vspace{-0.3cm}
\begin{center}
  \input{magicsqs.pdf_t}
\end{center}
\vspace{-0.4cm} Clearly, addition of two magic squares gives another
magic square, as well as does multiplication of a magic square by a
non-negative number. Therefore, we may talk about the \emph{cone} of
magic $3\times 3$ squares. In fact, this cone is a pointed rational
polyhedral cone described by the linear system
\begin{eqnarray*}
x_{11}+x_{12}+x_{13}
& = & x_{21}+x_{22}+x_{23}\\
& = & x_{31}+x_{32}+x_{33}\\
& = & x_{11}+x_{21}+x_{31}\\
& = & x_{12}+x_{22}+x_{32}\\
& = & x_{13}+x_{23}+x_{33}\\
& = & x_{11}+x_{22}+x_{33}\\
& = & x_{31}+x_{22}+x_{13}\\
&   & x_{ij} \geq 0,\;\;\; \text{ for all } i,j=1,2,3.
\end{eqnarray*}
Bringing all $x_{ij}$ to the left-hand side of these equations, the
matrix $A_{3\times 3}$ defining this linear system is
\[
A_{3\times 3}=\left(
\begin{array}{rrrrrrrrr}
1 & 1 & 1 & -1 & -1 & -1 &  0 &  0 &  0\\
1 & 1 & 1 &  0 &  0 &  0 & -1 & -1 & -1\\
0 & 1 & 1 & -1 &  0 &  0 & -1 &  0 &  0\\
1 & 0 & 1 &  0 & -1 &  0 &  0 & -1 &  0\\
1 & 1 & 0 &  0 &  0 & -1 &  0 &  0 & -1\\
0 & 1 & 1 &  0 & -1 &  0 &  0 &  0 & -1\\
1 & 1 & 0 &  0 & -1 &  0 & -1 &  0 &  0\\
\end{array}
\right).
\]
Below, we will deal with the more interesting case of \emph{integer} magic
squares. For the moment, however, we wish to compute the extreme rays
of the magic square cone $\{z:A_{3\times 3}z=0,z\geq 0\}$.

%%%% In test-suite, this is test/rays/33}
In order to call the function \File{rays}, we only have to create
one file, say \File{magic3x3.mat}, in which we specify the problem
matrix $A_{3\times 3}$. The remaining data is set by default to
"equations only", to "homogeneous system", and to "all variables are
non-negative". Note that we are allowed to change these defaults
(except homogeneity) by specifying data in \File{magic3x3.rel} and
\File{magic3x3.sign}
\begin{center}
  \begin{tabular}{|l|}
\hline
  \text{ magic3x3.mat } \\
\hline $\begin{array}{rrrrrrrrrrr}
& 7 & 9 &   &    &    &    &    &    &    & \\
& 1 & 1 & 1 & -1 & -1 & -1 &  0 &  0 &  0 & \\
& 1 & 1 & 1 &  0 &  0 &  0 & -1 & -1 & -1 & \\
& 0 & 1 & 1 & -1 &  0 &  0 & -1 &  0 &  0 & \\
& 1 & 0 & 1 &  0 & -1 &  0 &  0 & -1 &  0 & \\
& 1 & 1 & 0 &  0 &  0 & -1 &  0 &  0 & -1 & \\
& 0 & 1 & 1 &  0 & -1 &  0 &  0 &  0 & -1 & \\
& 1 & 1 & 0 &  0 & -1 &  0 & -1 &  0 &  0 & \\
\end{array}$\\
\hline
  \end{tabular}
\end{center}
Now we call
\begin{center}
{\tt ./rays magic3x3}
\end{center}
which creates the single file
\begin{center}
  \begin{tabular}{|l|}
\hline
    \text{ magic3x3.ray }\\
\hline
  $\begin{array}{rrrrrrrrrrr}& 4 & 9 &&&&&&&&\\
  & 0 & 2 & 1 & 2 & 1 & 0 & 1 & 0 & 2 & \\
  & 1 & 2 & 0 & 0 & 1 & 2 & 2 & 0 & 1 & \\
  & 2 & 0 & 1 & 0 & 1 & 2 & 1 & 2 & 0 & \\
  & 1 & 0 & 2 & 2 & 1 & 0 & 0 & 2 & 1 & \\\end{array}$\\
\hline
  \end{tabular}
\end{center}
that corresponds to the four extremal rays of the $3\times 3$ magic
square cone: \vspace{-0.3cm}
\begin{center}
  \input{magicsqs_ray.pdf_t}
\end{center}
\vspace{-0.4cm} Every magic $3\times 3$ square is a non-negative
linear combination of these four magic squares.

If we turn now to \emph{integer} magic squares, we are looking for a
Hilbert basis of the $3\times 3$ magic square cone. As the default
settings for \File{hilbert} are the same as for \File{rays}, we can
use the same input file
%%%% In test-suite, this is test/hilbert/magic33
\begin{center}
  \begin{tabular}{|l|}
\hline
  \text{ magic3x3.mat } \\
\hline $\begin{array}{rrrrrrrrrrr}
& 7 & 9 &   &    &    &    &    &    &    & \\
& 1 & 1 & 1 & -1 & -1 & -1 &  0 &  0 &  0 & \\
& 1 & 1 & 1 &  0 &  0 &  0 & -1 & -1 & -1 & \\
& 0 & 1 & 1 & -1 &  0 &  0 & -1 &  0 &  0 & \\
& 1 & 0 & 1 &  0 & -1 &  0 &  0 & -1 &  0 & \\
& 1 & 1 & 0 &  0 &  0 & -1 &  0 &  0 & -1 & \\
& 0 & 1 & 1 &  0 & -1 &  0 &  0 &  0 & -1 & \\
& 1 & 1 & 0 &  0 & -1 &  0 & -1 &  0 &  0 & \\
\end{array}$\\
\hline
  \end{tabular}
\end{center}
for this computation. However, to compute the Hilbert basis, we call
\begin{center}
{\tt ./hilbert magic3x3}
\end{center}
which creates the single output file
\begin{center}
  \begin{tabular}{|l|}
\hline
    \text{ magic3x3.hil }\\
\hline
  $\begin{array}{rrrrrrrrrrr}& 5 & 9 &&&&&&&&\\
  & 0 & 2 & 1 & 2 & 1 & 0 & 1 & 0 & 2 & \\
  & 1 & 2 & 0 & 0 & 1 & 2 & 2 & 0 & 1 & \\
  & 2 & 0 & 1 & 0 & 1 & 2 & 1 & 2 & 0 & \\
  & 1 & 0 & 2 & 2 & 1 & 0 & 0 & 2 & 1 & \\
  & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & \\\end{array}$\\
\hline
  \end{tabular}
\end{center}
that corresponds to the five elements in the minimal Hilbert basis
of the $3\times 3$ magic square cone: \vspace{-0.3cm}
\begin{center}
  \input{magicsqs_hil.pdf_t}
\end{center}
\vspace{-0.4cm} Every integer magic $3\times 3$ square is a
non-negative \emph{integer} linear combination of these five integer
magic squares. Note that the all-$1$ square is in the interior of
the magic square cone.

See \cite[section 3.8]{deloera-hemmecke-koeppe:book} or \cite{Hemmecke:2003} for details on the algorithm implemented.

\subsection{Computing circuits and Graver bases}

In this example you learn about the functions \File{graver},
\File{ppi}, and \File{circuits}.

As an example of a Graver basis computation, let us compute the
primitive partition identities of order $n=4$. Before we do the
simple computation, let us explain what a primitive partition
identity is.

A \important{partition identity} is any identity of the form
\[
a_1+\ldots+a_k=b_1+\ldots+b_l
\]
with (generally not distinct) integer numbers $0<a_i,b_j\leq n$. A
partition identity is called \important{primitive} if no proper
subidentity exists.

For example,
\[
1+2+3=2+2+2
\]
is a partition identity which is not primitive, since it contains
the subidentity
\[
1+3=2+2
\]
which is in fact primitive.

The description of the primitive partition identities for fixed $n$,
however, is exactly the description of the Graver basis of the
matrix
\[
A_n= \left(
\begin{array}{ccccc}
 1 & 2 & 3 & \ldots & n \\
\end{array}
\right).
\]
Let us finally do the computation for $n=3$. We create an input file
\File{ppi3} for \FourTiTwo{} which looks as follows:
\begin{center}
  \begin{tabular}{|l|}
\hline
    \text{ ppi3.mat }\\
\hline
  $\begin{array}{rrrrr}
    & 1 & 3 &&\\
    & 1 & 2 & 3 & \\
  \end{array}$\\
\hline
  \end{tabular}
\end{center}
and call
\begin{myverbatim}
./graver ppi3
\end{myverbatim}
This call will create an output file \File{ppi3.gra} that looks
like:
\begin{center}
  \begin{tabular}{|l|}
\hline
    \text{ ppi3.gra }\\
\hline
  $\begin{array}{rrrrr}
    & 5 & 3 &&\\
    & 3 &  0 & -1 & \\
    & 2 & -1 &  0 & \\
    & 0 &  3 & -2 & \\
    & 1 &  1 & -1 & \\
    & 1 & -2 &  1 & \\
  \end{array}$\\
\hline
  \end{tabular}
\end{center}
Thus, there are $5$ primitive partition identities of order $n=3$:
\begin{eqnarray*}
1+1+1 & = & 3\\
1+1   & = & 2\\
2+2+2 & = & 3+3\\
1+2   & = & 3\\
1+3   & = & 2+2
\end{eqnarray*}
You may try and compute the primitive partition identities for
bigger $n$, say $n=17$, $20$, or $23$. Be aware, especially the
latter two problems take a long, long time. What is the biggest $n$
for which you can compute the primitive partition identities of
order $n$ on your machine within one hour?

Due to the very special structure of the matrix, there are
algorithmic speed-ups \cite{Haus+Koeppe+Weismantel,Koeppe,Urbaniak}.
The currently fastest algorithm to compute primitive partition
identities is implemented in the function \File{ppi} of
\FourTiTwo{}. Try running
\begin{myverbatim}
./ppi 17
\end{myverbatim}
which creates two files \File{ppi17.mat} (so we do not really have
to create this file ourselves) and the file \File{ppi17.gra}
containing the desired identities. Compare this running time with
the time taken by
\begin{myverbatim}
./graver ppi17
\end{myverbatim}
Do you notice the speed-up?

Let us now turn to the question of determining the support-minimal
partition identities. This, in fact, is the question of computing
the circuits of the matrix
\[
A_n= \left(
\begin{array}{ccccc}
 1 & 2 & 3 & \ldots & n \\
\end{array}
\right).
\]
We use the same input file
\begin{center}
  \begin{tabular}{|l|}
\hline
    \text{ ppi3.mat }\\
\hline
  $\begin{array}{rrrrr}
    & 1 & 3 &&\\
    & 1 & 2 & 3 & \\
  \end{array}$\\
\hline
  \end{tabular}
\end{center}
as above and call
\begin{myverbatim}
./circuits ppi3
\end{myverbatim}
This call will create an output file \File{ppi3.cir} that looks
like:
\begin{center}
  \begin{tabular}{|l|}
\hline
    \text{ ppi3.cir }\\
\hline
  $\begin{array}{rrrrr}
    & 3 & 3 &&\\
    & 3 &  0 & -1 & \\
    & 2 & -1 &  0 & \\
    & 0 &  3 & -2 & \\
  \end{array}$\\
\hline
  \end{tabular}
\end{center}
Thus, there are $3$ support-minimal partition identities of order
$n=3$:
\begin{eqnarray*}
1+1+1 & = & 3\\
1+1   & = & 2\\
2+2+2 & = & 3+3\\
\end{eqnarray*}
Note that support-minimal partition identities are primitive, since
the circuits of a matrix are contained in the Graver basis of this
matrix.

See the book \cite[section 3.8]{deloera-hemmecke-koeppe:book}, or Hemmecke
\cite{Hemmecke:2003b}  for details on the algorithm implemented. 
\nocite{Hemmecke:Graver-symmetries}

\subsection{Integer programming and toric Gr\"obner bases}
In this example you learn about the functions \File{minimize},
\File{groebner}, and \File{normalform}.

The following neat example is based on the example presented in
\cite{Sturmfels:03}. Let us assume that we want to give change worth
99 cents using only pennies ($1$ct), nickels ($5$ct), dimes
($10$ct), and quarters ($25$ct). Clearly,
\[
4\cdot 1+4\cdot 5+0\cdot 10+3\cdot 25=99
\]
would be one way to do it. Is this there another choice of $11$ coins
that sums up to $99$ct but uses fewer nickels and quarters (in total)?
In other words, we would like to solve
\[
\min\{x_2+x_4:x_1+x_2+x_3+x_4=11,x_1+5x_2+10x_3+25x_4=99,
x_1,x_2,x_3,x_4\in\Z_+\}
\]
Let us set up the problem in \FourTiTwo{}.
\begin{center}
  \begin{tabular}{|l|l|l|l|}
\hline
    \text{ 4coins.mat } & \text{ 4coins.zsol } & \text{ 4coins.sign } & \text{ 4coins.cost } \\
\hline
  $\begin{array}{rrrrrr}& 2 & 4 & & & \\& 1 & 1 & 1 & 1 &\\& 1 & 5 & 10 & 25 & \\ \end{array}$ &
  $\begin{array}{rrrrrr}& 1 & 4& \\& 4 & 4 & 0 & 3 & \\ \\\end{array}$ &
  $\begin{array}{rrrrrr}& 1 & 4 & \\& 1 &  1 & 1 & 1 &\\ \\\end{array}$ &
  $\begin{array}{rrrrrr}& 1 & 4 & & & \\& 0 & 1 & 0 & 1 &\\ \\
  \end{array}$\\
\hline
  \end{tabular}
\end{center}

Note that we do not have to specify a relations file
\File{4coins.rel}, since already by default all relations are
assumed to be equations. Now we simply call
\begin{center}
{\tt ./minimize 4coins}
\end{center}
which creates the single output file
\begin{center}
  \begin{tabular}{|l|}
\hline
    \text{ 4coins.min }\\
\hline
  $\begin{array}{rrrrrr}
    & 1 & 4 &&&\\
    & 4 & 1 & 4 & 2 & \\
  \end{array}$\\
\hline
  \end{tabular}
\end{center}
From this, we conclude that
\[
4\cdot 1+1\cdot 5+4\cdot 10+2\cdot 25=99
\]
is an optimal choice, using only $3$ instead of $7$ nickels and
quarters.

{\bf Remark.} %% We could also specify a list of right-hand sides in
%% \File{4coins.rhs}. The call
%% \begin{center}
%% {\tt ./minimize 4coins}
%% \end{center}
%% then creates a file \File{4coins.min} containing minima to the
%% corresponding integer programs.
Earlier versions of 4ti2 allowed to specify the right-hand side vector in a
file called \File{4coins.rhs}, instead
of giving a solution in \File{4coins.zsol}.  This is no longer supported.
\eoproof

Since we already know a feasible solution, there is another way we
might attack this problem, namely via toric Gr\"obner bases. 
(See \cite[Chapter 11]{deloera-hemmecke-koeppe:book} for an introduction to
toric ideals and their Gr\"obner bases, and also their generalizations,
lattice ideals.)
For
this, we first need to specify the matrix $A$ and the cost vector
$c$ in the two files \File{4coins.mat} and \File{4coins.cost}:
\begin{center}
  \begin{tabular}{|l|l|}
\hline
    \text{ 4coins.mat } & \text{ 4coins.cost } \\
\hline
  $\begin{array}{rrrrrr}& 2 & 4 & & & \\& 1 & 1 & 1 & 1 &\\& 1 & 5 & 10 & 25 & \\ \end{array}$ &
  $\begin{array}{rrrrrr}& 1 & 4 & & & \\& 0 & 1 & 0 & 1 &\\ \\
  \end{array}$\\
\hline
  \end{tabular}
\end{center}
Then we compute the Gr\"obner basis of the toric ideal
\[
I_A=\langle x^u-x^v:Au=Av, u,v\in\Z^4_+\rangle
\]
with respect to a term ordering $\prec$ compatible with $c$, that
is, $c^\intercal v < c^\intercal u$ implies $x^v\prec x^u$. This
toric Gr\"obner basis is computed by
\begin{center}
{\tt ./groebner 4coins}
\end{center}
and gives the output file
\begin{center}
  \begin{tabular}{|l|}
\hline
    \text{ 4coins.gro }\\
%% \hline
%%   $\begin{array}{rrrrrr}
%%     & 1 & 4 &&&\\
%%     & 4 & 4 & 0 & 3 & \\
%%   \end{array}$\\
%%%%%%%%%%%%% 4ti2 actually computes something else! See
%%%%%%%%%%%%% test-suite. --mkoeppe, 2015-04-27
\hline
  \end{tabular}
\end{center}
\boremark
  Many algorithm options are available and can be selected by command-line
  options of \File{groebner}, see section~\ref{s:ref:groebner}.  As reference to the algorithms
  we recommend the book \cite[section 11.4]{deloera-hemmecke-koeppe:book} or Hemmecke and Malkin \cite{HemmeckeMalkin2009}, as well as 
  Bigatti, LaScala, and Robbiano \cite{BigattiLascalaRobbiano99}, Gebauer and M\"oller
  \cite{Gebauer+Moeller:88}, and Ho{\c{s}}ten and Sturmfels
  \cite{HostenSturmfels:GRIN}.
  Since runnning times of the various algorithms are hard to predict, it may
  for some hard problems make sense to start several computations in parallel,
  each with different algorithms. 
\eoproof

Then we specify our feasible solution in
\begin{center}
  \begin{tabular}{|l|}
\hline
    \text{ 4coins.feas }\\
\hline
  $\begin{array}{rrrrrr}
    & 1 & 4 &&&\\
    & 4 & 4 & 0 & 3 & \\
  \end{array}$\\
\hline
  \end{tabular}
\end{center}
and call
\begin{center}
{\tt ./normalform 4coins}
\end{center}
to produce the file
\begin{center}
  \begin{tabular}{|l|}
\hline
    \text{ 4coins.nf }\\
\hline
  $\begin{array}{rrrrrr}
    & 1 & 4 &&&\\
    & 4 & 1 & 4 & 2 & \\
  \end{array}$\\
\hline
  \end{tabular}
\end{center}
that also contains the desired optimal solution.

{\bf Remark.} We could also specify a list of feasible solutions in
\File{4coins.feas}. Then the call
\begin{center}
{\tt ./normalform 4coins}
\end{center}
creates a file \File{4coins.nf} containing the minima to the
corresponding integer programs. (If $z_0$ is a feasible solution,
the corresponding integer program is defined by putting the
right-hand side to $Az_0$.) \eoproof

%%%% {\bf Rename \File{4coins.zfeas} to \File{4coins.zfea}?!}

\nocite{Malkin:truncated}

\subsection{Markov Bases in Statistics}
In this example you learn about the functions \File{markov} and
\File{genmodel}.

Let us consider the following $4\times 4$ table of non-negative
integer numbers together with all row and column sums.
\[
\begin{array}{cc}
\left(
\begin{array}{rrrr}
11 & 23 & 34 &  3\\
 4 & 15 & 12 & 11\\
17 &  2 &  3 & 25\\
16 & 12 & 22 &  7\\
\end{array}
\right)
&
\begin{array}{r}
71\\
42\\
47\\
57\\
\end{array}\\
\begin{array}{rrrr}
48 & 52 & 71 & 46\\
\end{array} & \\
\end{array}
\]
In statistics, one wishes to sample among arrays that have fixed
counts, say fixed row and column sums. In order to sample, one needs
a set of moves that, in particular, do not change the counts when
added to the current table. Clearly, these moves must have counts
$0$ and thus quite naturally lead us to the toric ideal
\[
I_A=\langle x^u-x^v:Au=Av, u,v\in\Z^{16}_+\rangle,
\]
where
\[
A=\left(
\begin{array}{rrrrrrrrrrrrrrrr}
1 &  1 & 1 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\
0 &  0 & 0 & 0 & 1 & 1 & 1 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\
0 &  0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 1 & 1 & 1 & 0 & 0 & 0 & 0\\
0 &  0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 1 & 1 & 1\\
1 &  0 & 0 & 0 & 1 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 1 & 0 & 0 & 0\\
0 &  1 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 1 & 0 & 0\\
0 &  0 & 1 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 1 & 0\\
0 &  0 & 0 & 1 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 1\\
\end{array}
\right).
\]
It turns out that for any set of fixed counts, a (minimal)
\emph{Markov basis} is given by a minimal generating set of this
toric ideal. Note that a Markov basis connects all non-negative
tables with these counts in the sense that for any two non-negative
tables $T_1$ and $T_2$ with these counts, there is a sequence of
non-negative tables $T_1=S_0,\ldots, S_N=T_2$ with the same counts
as $T_1$ and $T_2$ and such that $S_i-S_{i-1}$ or $S_{i-1}-S_{i}$ is
in the Markov basis for $i=1,\ldots, N$.

For two-way tables the situation is still very simple as our
computations with $4\times 4$ tables will now demonstrate. Write the
matrix that defines our toric ideal in the file \File{4x4.mat}:
\begin{center}
  \begin{tabular}{|l|}
\hline
  \text{ 4x4.mat } \\
\hline $\begin{array}{rrrrrrrrrrrrrrrrrr}
& 8 & 16 &   &   &   &   &   &   &   &   &   &   &   &   &   &   & \\
& 1 &  1 & 1 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & \\
& 0 &  0 & 0 & 0 & 1 & 1 & 1 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & \\
& 0 &  0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 1 & 1 & 1 & 0 & 0 & 0 & 0 & \\
& 0 &  0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 1 & 1 & 1 & \\
& 1 &  0 & 0 & 0 & 1 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & \\
& 0 &  1 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 1 & 0 & 0 & \\
& 0 &  0 & 1 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 1 & 0 & \\
& 0 &  0 & 0 & 1 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 1 & \\
\end{array}$\\
\hline
  \end{tabular}
\end{center}
Let us compute the Markov basis via the call
\begin{center}
{\tt ./markov 4x4}
\end{center}
which creates a single output file \File{4x4.mar} containing the
$36$ Markov basis elements. Up to symmetry (swapping rows or
columns), the Markov basis consists of the single move
\[
\left(
\begin{array}{rrrr}
 1 & -1 &  0 &  0\\
-1 &  1 &  0 &  0\\
 0 &  0 &  0 &  0\\
 0 &  0 &  0 &  0\\
\end{array}
\right).
\]
In fact, this elementary move is (up to symmetry) the only
representative of the minimal Markov moves for arbitrary $m\times n$
tables using row and column counts.

Creating the matrices for statistical models may be pretty
cumbersome. \FourTiTwo{} provides a litte function, \File{genmodel},
that helps the user with creating matrices for hierarchical models
defined by a complex. 

The $m\times n$ tables problem above corresponds to the complex
$\{\{1\},\{2\}\}$ on two nodes with levels $m$ and $n$,
respectively. Let us encode the complex for $3\times 6$ tables with
$1$-marginals (row and column sums) in \File{3x6.mod}.

\begin{center}
  \begin{tabular}{|l|}
\hline
    \text{ 3x6.mod }\\
\hline
  $\begin{array}{rrrr}
    & 3 &&\\
    & 3 & 6 & \\
    & 2 &&\\
    & 1 & 1 &\\
    & 1 & 2 &\\
  \end{array}$\\
\hline
  \end{tabular}
\end{center}
and call
\begin{center}
{\tt ./genmodel 3x6}
\end{center}
to produce the desired matrix file \File{3x6.mat}.

The encoding of the complex should be obvious from the example: first
we state the number of nodes and their levels, then we give the number
of maximal faces. Finally, we list each maximal face by first
specifying the number of nodes on it and then by listing these nodes.

Thus, a $3\times4\times6$ table with $2$-marginals (that is, again only counts
along coordinate axes) corresponds to the complex
$\{\{(1,2)\},\{(2,3)\},\{(3,1)\}\}$ on $3$ nodes with levels $3$,
$4$, and $6$, respectively. Thus, its encoding is in \FourTiTwo{} would
look like:
\begin{center}
  \begin{tabular}{|l|}
\hline
    \text{ 3x4x6.mod }\\
\hline
  $\begin{array}{rrrrr}
    & 3 &&\\
    & 3 & 4 & 6 & \\
    & 3 &&\\
    & 2 & 1 & 2 &\\
    & 2 & 2 & 3 &\\
    & 2 & 3 & 1 &\\
  \end{array}$\\
\hline
  \end{tabular}
\end{center}

A binary model on the bipartite graph $K_{2,3}$ then reads as
\begin{center}
  \begin{tabular}{|l|}
\hline
    \text{ k2\_3.mod }\\
\hline
  $\begin{array}{rrrrrrr}
    & 5 &&&&\\
    & 2 & 2 & 2 & 2 & 2 &\\
    & 6 &&&&\\
    & 2 & 1 & 3 &\\
    & 2 & 1 & 4 &\\
    & 2 & 1 & 5 &\\
    & 2 & 2 & 3 &\\
    & 2 & 2 & 4 &\\
    & 2 & 2 & 5 &\\
  \end{array}$\\
\hline
  \end{tabular}
\end{center}


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