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Merge pull request #14 from ollpu/master 

Improve grammar and language style in chapter 3
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@ -9,12 +9,12 @@ use sorting as a subroutine,
because it is often easier to process because it is often easier to process
data if the elements are in a sorted order. data if the elements are in a sorted order.
For example, the problem ''does the array contain For example, the problem ''does an array contain
two equal elements?'' is easy to solve using sorting. two equal elements?'' is easy to solve using sorting.
If the array contains two equal elements, If the array contains two equal elements,
they will be next to each other after sorting, they will be next to each other after sorting,
so it is easy to find them. so it is easy to find them.
Also the problem ''what is the most frequent element Also, the problem ''what is the most frequent element
in the array?'' can be solved similarly. in the array?'' can be solved similarly.
There are many algorithms for sorting, and they are There are many algorithms for sorting, and they are
@ -316,7 +316,7 @@ in the wrong order removes exactly one inversion
from the array. from the array.
Hence, if a sorting algorithm can only Hence, if a sorting algorithm can only
swap consecutive elements, each swap removes swap consecutive elements, each swap removes
at most one inversion and the time complexity at most one inversion, and the time complexity
of the algorithm is at least $O(n^2)$. of the algorithm is at least $O(n^2)$.
\subsubsection{$O(n \log n)$ algorithms} \subsubsection{$O(n \log n)$ algorithms}
@ -326,11 +326,11 @@ of the algorithm is at least $O(n^2)$.
It is possible to sort an array efficiently It is possible to sort an array efficiently
in $O(n \log n)$ time using algorithms in $O(n \log n)$ time using algorithms
that are not limited to swapping consecutive elements. that are not limited to swapping consecutive elements.
One such algorithm is \key{mergesort}\footnote{According to \cite{knu983}, One such algorithm is \key{merge sort}\footnote{According to \cite{knu983},
mergesort was invented by J. von Neumann in 1945.} merge sort was invented by J. von Neumann in 1945.},
that is based on recursion. which is based on recursion.
Mergesort sorts a subarray \texttt{t}$[a,b]$ as follows: Merge sort sorts the subarray \texttt{t}$[a,b]$ as follows:
\begin{enumerate} \begin{enumerate}
\item If $a=b$, do not do anything, because the subarray is already sorted. \item If $a=b$, do not do anything, because the subarray is already sorted.
@ -341,7 +341,7 @@ Mergesort sorts a subarray \texttt{t}$[a,b]$ as follows:
into a sorted subarray \texttt{t}$[a,b]$. into a sorted subarray \texttt{t}$[a,b]$.
\end{enumerate} \end{enumerate}
Mergesort is an efficient algorithm, because it Merge sort is an efficient algorithm, because it
halves the size of the subarray at each step. halves the size of the subarray at each step.
The recursion consists of $O(\log n)$ levels, The recursion consists of $O(\log n)$ levels,
and processing each level takes $O(n)$ time. and processing each level takes $O(n)$ time.
@ -519,8 +519,8 @@ ways to sort the array, a total of $n!$ ways.
For this reason, the height of the tree For this reason, the height of the tree
must be at least must be at least
\[ \log_2(n!) = \log_2(1)+\log_2(2)+\cdots+\log_2(n).\] \[ \log_2(n!) = \log_2(1)+\log_2(2)+\cdots+\log_2(n).\]
We get an lower bound for this sum We get a lower bound for this sum
by choosing last $n/2$ elements and by choosing the last $n/2$ elements and
changing the value of each element to $\log_2(n/2)$. changing the value of each element to $\log_2(n/2)$.
This yields an estimate This yields an estimate
\[ \log_2(n!) \ge (n/2) \cdot \log_2(n/2),\] \[ \log_2(n!) \ge (n/2) \cdot \log_2(n/2),\]
@ -541,7 +541,7 @@ An example of such an algorithm is
$O(n)$ time assuming that every element in the array $O(n)$ time assuming that every element in the array
is an integer between $0 \ldots c$ and $c=O(n)$. is an integer between $0 \ldots c$ and $c=O(n)$.
The algorithm creates a \emph{bookkeeping} array The algorithm creates a \emph{bookkeeping} array,
whose indices are elements in the original array. whose indices are elements in the original array.
The algorithm iterates through the original array The algorithm iterates through the original array
and calculates how many times each element and calculates how many times each element
@ -604,7 +604,7 @@ in the bookkeeping array is 2,
because the element 3 appears 2 times because the element 3 appears 2 times
in the original array (positions 2 and 6). in the original array (positions 2 and 6).
The construction of the bookkeeping array Construction of the bookkeeping array
takes $O(n)$ time. After this, the sorted array takes $O(n)$ time. After this, the sorted array
can be created in $O(n)$ time because can be created in $O(n)$ time because
the number of occurrences of each element can be retrieved the number of occurrences of each element can be retrieved
@ -614,7 +614,7 @@ sort is $O(n)$.
Counting sort is a very efficient algorithm Counting sort is a very efficient algorithm
but it can only be used when the constant $c$ but it can only be used when the constant $c$
is so small that the array elements can is small enough, so that the array elements can
be used as indices in the bookkeeping array. be used as indices in the bookkeeping array.
\section{Sorting in C++} \section{Sorting in C++}
@ -630,9 +630,9 @@ the function \texttt{sort} that can be easily used for
sorting arrays and other data structures. sorting arrays and other data structures.
There are many benefits in using a library function. There are many benefits in using a library function.
First, it saves time because there is no need to Firstly, it saves time because there is no need to
implement the function. implement the function.
In addition, the library implementation is Secondly, the library implementation is
certainly correct and efficient: it is not probable certainly correct and efficient: it is not probable
that a self-made sorting function would be better. that a self-made sorting function would be better.
@ -674,8 +674,8 @@ For example, the string ''monkey'' becomes ''ekmnoy''.
The function \texttt{sort} requires that The function \texttt{sort} requires that
a \key{comparison operator} is defined for the data type a \key{comparison operator} is defined for the data type
of the elements to be sorted. of the elements to be sorted.
During the sorting, this operator will be used When sorting, this operator will be used
whenever it is needed to find out the order of two elements. whenever it is necessary to find out the order of two elements.
Most C++ data types have a built-in comparison operator, Most C++ data types have a built-in comparison operator,
and elements of those types can be sorted automatically. and elements of those types can be sorted automatically.
@ -684,10 +684,10 @@ and strings are sorted in alphabetical order.
\index{pair@\texttt{pair}} \index{pair@\texttt{pair}}
Pairs (\texttt{pair}) are sorted primarily by their first Pairs (\texttt{pair}) are sorted primarily according to their
elements (\texttt{first}). first elements (\texttt{first}).
However, if the first elements of two pairs are equal, However, if the first elements of two pairs are equal,
they are sorted by their second elements (\texttt{second}): they are sorted according to their second elements (\texttt{second}):
\begin{lstlisting} \begin{lstlisting}
vector<pair<int,int>> v; vector<pair<int,int>> v;
v.push_back({1,5}); v.push_back({1,5});
@ -719,14 +719,14 @@ User-defined structs do not have a comparison
operator automatically. operator automatically.
The operator should be defined inside The operator should be defined inside
the struct as a function the struct as a function
\texttt{operator<} \texttt{operator<},
whose parameter is another element of the same type. whose parameter is another element of the same type.
The operator should return \texttt{true} The operator should return \texttt{true}
if the element is smaller than the parameter, if the element is smaller than the parameter,
and \texttt{false} otherwise. and \texttt{false} otherwise.
For example, the following struct \texttt{P} For example, the following struct \texttt{P}
contains the x and y coordinate of a point. contains the x and y coordinates of a point.
The comparison operator is defined so that The comparison operator is defined so that
the points are sorted primarily by the x coordinate the points are sorted primarily by the x coordinate
and secondarily by the y coordinate. and secondarily by the y coordinate.
@ -951,4 +951,4 @@ Note that unlike in the ordinary binary search,
here it is not allowed that consecutive values here it is not allowed that consecutive values
of the function are equal. of the function are equal.
In this case it would not be possible to know In this case it would not be possible to know
how to continue the search. how to continue the search.