Commit 24563cc0 authored by Michael Keller's avatar Michael Keller
Browse files

thesis: add LP in LP Method

parent 6f378044
......@@ -37,7 +37,7 @@ on large fields and requires no conditions of $R$.
\begin{tikzpicture}[->,>=stealth',auto,node distance=3cm,thick,main node/.style={rectangle,draw,font=\sffamily\Large\bfseries}]
\begin{tikzpicture}[->,>=stealth',auto,node distance=3cm,thick,main node/.style={rectangle,draw,font=\sffamily\Large}]
\node[main node] (1) {U};
\node[main node] (2) [right of=1] {V};
\chapter{The Linear Programming Method}
\section{Problem Setup}
The core idea with this method is to replace
the simple swap between two pixels in the simulated
annealing approach with a multi-way swap. Picture
the following scenario of three non-neighboring pixels:
\begin{tabular}{@{}cccc@{}} \toprule
Pixel & Currently Contains & Preferred & Strongly Disliked\\ \midrule
W & $a$ & $c$ & $b$ \\
U & $b$ & $a$ & $c$ \\
V & $c$ & $b$ & $a$ \\
Using only swaps involving two pixels will always
lead to a pixel containing a crop it strongly dislikes
based on it's neighborhood. Instead, swapping between
three pixels as seen below leads to every pixel containing
their preferred crop:
\begin{tikzpicture}[->,>=stealth',auto,node distance=2cm,thick,main node/.style={rectangle,draw,font=\sffamily\Large}]
\node[main node] at (-3, 0) (1) {U};
\node (hidden) [right of=1] {};
\node[main node] (2) [right of=hidden] {V};
\node[main node] (3) [above of=hidden] {W};
\path[every node/.style={font=\sffamily\small}]
(1) edge[left] node [above] {$b$} (2)
(2) edge[left] node [above] {$c$} (3)
(3) edge[right] node [above] {$a$} (1);
However, the question is how we find such a multi-way swap.
If we allowed swapping between neighboring pixels this would
indeed be tricky. If instead we restrict ourselves to only
swapping between non-neighboring pixels however, then the score
function changes only linearly and we can use linear programming
to find the optimal swap.
Concretely, we setup the following linear program. Let $P$
be a set on non-neighboring pixels. Every pixel
gets $C$ variables that denote how much of each crop
is planted within the pixel:
x &= \begin{bmatrix}
x_{1, 1} \\
\vdots \\
x_{1, C} \\
\vdots \\
x_{n, C}
& &
x_{i, j} = \text{Pixel } p_i \text{'s amount of crop } j
And the function we are trying to maximize shows
for every variable in $x$ how good it would be
based on the pixels neighborhood to plant that crop
c &= \begin{bmatrix}
c_{1, 1} \\
\vdots \\
c_{1, C} \\
\vdots \\
c_{n, C}
& &
c_{i, j} = \text{Score change for planting } 1 \text{ of crop } j \text{ in pixel } i
Then we are trying to maximize the function:
\text{maximize } c^T x
subject to the constraints that:
\item We don't allow less than $0$ or more than $1$ of each crop to
be planted in a pixel
\item Every Pixel must contain a total amount of crops equaling $1$
\item The amount of each crop we are redistributing must stay the same
or formally, a solution $x^*$ must satisfy:
0 \leq x^*_{i, j} &\leq 1\\
\forall p \in P:\quad \sum_{c \in \Cps} x^*_{p, c} &= 1\\
\forall c \in \Cps:\quad \sum_{p \in P} x^*_{p, c} &= \sum_{p \in P} x_{p, c}
Now that we have an understanding of the
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......@@ -19,6 +19,8 @@
\contentsline {subsection}{\numberline {7.2.1}The standard method}{17}{subsection.7.2.1}%
\contentsline {subsection}{\numberline {7.2.2}The advanced method}{18}{subsection.7.2.2}%
\contentsline {chapter}{\chapternumberline {8}The Linear Programming Method}{21}{chapter.8}%
\contentsline {section}{\numberline {8.1}Problem Setup}{21}{section.8.1}%
\contentsline {section}{\numberline {8.2}Method}{22}{section.8.2}%
\contentsline {chapter}{\chapternumberline {9}Gradient Descent}{23}{chapter.9}%
\contentsline {chapter}{\chapternumberline {10}Conclusion}{25}{chapter.10}%
\contentsline {appendix}{\chapternumberline {A}Calculations Appendix}{27}{appendix.A}%
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