Commit 001b6d80 authored by Michael Keller's avatar Michael Keller
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working on NP proof

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\chapter{Computational Hardness}
We show that the problem of finding a perfect
solution to a pixel farming problem is NP complete.
To do this we will first formulate the problem as
a decision problem. In a second step we will
show that the problem is in NP. Lastly we will
prove that pixel farming can be used to find
hamiltonian paths, which will imply that finding
perfect solutions for pixel farming is NP complete.
\section{The decision version of pixel farming}
The decision version of the pixel farming
problem we define very similarly to the
regular version. However, instead of asking
for a field with the maximum score, we ask
for a field with a score at least $s$. Formally:
Does a field $F$ for relation function $R$ exist
such that the score $S(F, R)$ is at least $s$:
\begin{displaymath}
s \leq S(F, R)
\end{displaymath}
\section{Pixel Farming is in NP}
Assume we are given a field $F$ and a relation
function $R$. By computing $S(F, R)$, which
can be done in Polynomial time in relation to the field
size $X \cdot Y$, we can easily test is the score
is at least a certain value. Further, testing whether
our field $F$ meets the requirements specified by $D$,
our probability distribution specifying how much of
the field should consist of a certain crop, can also
be done in polynomial time in relation to the field
size $X \cdot Y$ and number of crops $C$.
\section{Pixel Farming finds Hamiltonian Paths}
It is known that finding Hamiltonian Paths is NP
complete. Thus, reducing the decision version
of hamiltonian paths (explained below) to the decision
version of pixel farming demonstrates that pixel
farming is NP complete.
\paragraph{Hamiltonian Paths}
Let $G = (V, E)$ be an undirected graph. We formulate the
decision version of the hamiltonian path problem as follows:
Does there exists a path in $G$ of length at least $l$
where every vertex in the path is visited only once?
\paragraph{Reduction} We assume we are given a graph $G$,
a length $l$ and a polynomial time algorithm in $X \cdot Y$
and $C$ for the pixel farming decision problem. We are asked
to solve the hamiltonian path decision problem in polynomial
time in relation to the size of $G$.
In polynomial time we build the following pixel farming
decision problem. Our crops are as follows:
\begin{displaymath}
\Cps = V \cup \{ \abs{V} + 1, \dots, \abs{V} + 1 + l + \}
\end{displaymath}
\ No newline at end of file
......@@ -13,7 +13,24 @@ We assume that we are given four inputs:
$R(c_a, c_b)$, the more crop $c_a$ likes $c_b$.
\item A probability distribution $D$ over our crops $\Cps$
that tells us how many pixels of the field should
contain a certain crop.
contain a certain crop. $D$ should satisfy
$D(c) \cdot X \cdot Y \in \N \quad \forall c \in \Cps$.
\end{enumerate}
Further let us define a neighborhood function that gives
us all neighboring pixels of a given pixel:
\begin{displaymath}
N(x, y) = \{(a, b) \in \N^2 \ | \ a < X \land b < Y \land \abs{a - x} \leq 1 \land \abs{b - y} \leq 1 \land (a, b) \neq (x, y) \}
\end{displaymath}
and we are looking for a matrix $F \in \Cps^{X \times Y}$
that maximizes the following score function:
\begin{displaymath}
S(F, R) = \sum_{i = 0}^{X-1} \sum_{j = 0}^{Y-1} \sum_{n \in N(i, j)} R(F_{(i, j)}, F_n)
\end{displaymath}
where we also fulfill the distribution constraint:
\begin{align*}
I((i, j), c) &= \begin{cases}
1 & F((i, j)) = c\\
0 & otherwise.
\end{cases}\\
\forall c \in \Cps : D(c) \cdot X \cdot Y &= \sum_{i = 0}^{X-1} \sum_{j = 0}^{Y-1} I((i, j), c)
\end{align*}
\ No newline at end of file
......@@ -4,3 +4,13 @@
year={1996},
publisher={Hartley \& Marks}
}
@book{alma991170823569005501,
author = {Cormen, Thomas H.},
edition = {Third edition},
isbn = {978-0-262-03384-8},
keywords = {Computer programming},
language = {eng},
publisher = {The MIT Press},
title = {Introduction to algorithms},
year = {2009},
}
......@@ -2,6 +2,10 @@
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......@@ -115,6 +115,7 @@
\input{indroduction.tex}
\input{related-work.tex}
\input{problem-statement.tex}
\input{hardness.tex}
\input{conclusion.tex}
\appendix
......
......@@ -4,6 +4,12 @@
\contentsline {chapter}{\chapternumberline {1}Introduction}{1}{chapter.1}%
\contentsline {chapter}{\chapternumberline {2}Related Work}{3}{chapter.2}%
\contentsline {chapter}{\chapternumberline {3}Problem Statement}{5}{chapter.3}%
\contentsline {chapter}{\chapternumberline {4}Conclusion}{7}{chapter.4}%
\contentsline {appendix}{\chapternumberline {A}Calculations Appendix}{9}{appendix.A}%
\contentsline {chapter}{Bibliography}{11}{appendix*.2}%
\contentsline {chapter}{\chapternumberline {4}Computational Hardness}{7}{chapter.4}%
\contentsline {section}{\numberline {4.1}The decision version of pixel farming}{7}{section.4.1}%
\contentsline {section}{\numberline {4.2}Pixel Farming is in NP}{7}{section.4.2}%
\contentsline {section}{\numberline {4.3}Pixel Farming finds Hamiltonian Paths}{8}{section.4.3}%
\contentsline {paragraph}{Hamiltonian Paths}{8}{section*.2}%
\contentsline {paragraph}{Reduction}{8}{section*.3}%
\contentsline {chapter}{\chapternumberline {5}Conclusion}{9}{chapter.5}%
\contentsline {appendix}{\chapternumberline {A}Calculations Appendix}{11}{appendix.A}%
\contentsline {chapter}{Bibliography}{13}{appendix*.4}%
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