commit a26ae88e866c32a62e3326e39c9e0add901efb0a (tree)
parent d10a7580f1bbc8cb491a468358e4a421d8f4c8a8
Author: Motiejus Jakštys <motiejus@uber.com>
Date: Mon, 26 Apr 2021 11:54:00 +0300
explain big-O notation better
Diffstat:
1 file changed, 21 insertions(+), 13 deletions(-)
diff --git a/IV/mj-msc.tex b/IV/mj-msc.tex
@@ -205,7 +205,7 @@ thus convenient to analyze for both small and large scale generalization.
\includegraphics[width=.2\textwidth]{salvis-250k}
\caption{Example scaled 1:250000.}
\end{subfigure}
- \caption{Down-scaled original river (1:50000 and 1:250000).}
+ \caption{Down-scaled original river.}
\label{fig:salvis-50-250}
\end{figure}
@@ -339,12 +339,12 @@ This section defines vocabulary and terms as defined in the rest of the paper.
\item[Vertex] is a point on a plane, can be expressed by a pair of $(x,y)$
coordinates.
- \item[Line Segment (or Segment)] joins two vertices by a straight line. A
- segment can be expressed by two coordinate pairs: $(x_1, y_1)$ and
- $(x_2, y_2)$. Line Segment and Segment are used interchangeably
+ \item[Line Segment] or \textsc{segment} joins two vertices by a straight
+ line. A segment can be expressed by two coordinate pairs: $(x_1, y_1)$
+ and $(x_2, y_2)$. Line Segment and Segment are used interchangeably
throughout the paper.
- \item[Line], or \textsc{linestring}, represents a single linear feature in
+ \item[Line] or \textsc{linestring}, represents a single linear feature in
the real world. For example, a river or a coastline.
Geometrically, A line is a series of connected line segments, or,
@@ -354,21 +354,29 @@ This section defines vocabulary and terms as defined in the rest of the paper.
\item[Bend] is a subset of a line that humans perceive as a curve. The
geometric definition is complex and is discussed in
- section~\onpage{sec:definition-of-a-bend}.
+ section~\ref{sec:definition-of-a-bend}.
\item[Baseline] is a line between bend's first and last vertex.
\item[Sum of inner angles] TBD.
\item[Algorithmic Complexity] also called \textsc{big o notation}, is a
- relative measure to explain how long will the algorithm run depending
- on it's input. For example, given $n$ objects and time complexity of
- $O(n)$, the time it takes to execute the algorithm is proportional to
- $n$. Conversely, if complexity is $O(n^2)$, then the time it takes to
- execute the algorithm is quadratic. $O$ notation was first suggested by
+ relative measure to explain how long will the algorithm runs depending
+ on it's input. It is widely used in computing science when discussing
+ the efficiency of a given algorithm.
+
+ For example, given $n$ objects and time complexity of $O(log(n))$, the
+ time it takes to execute the algorithm is logarithmic to $n$.
+ Conversely, if complexity is $O(n^2)$, then the time it takes to
+ execute the algorithm is quadratic depending on the input. Importantly,
+ if the input size doubles, the time it takes to run the algorithm
+ quadruples.
+
+ $O$ notation was first suggested by
Bachmann\cite{bachmann1894analytische} and
- Laundau\cite{landau2000handbuch} in late XIX'th century, and adopted
- for computer science by Donald Knuth\cite{knuth1976big} in 1970s.
+ Landau\cite{landau2000handbuch} in late XIX'th century, and clarified
+ and popularized for computing science by Donald
+ Knuth\cite{knuth1976big} in the 1970s.
\end{description}