# 2.6. Analysis of Array and Vector Operators¶

As we know, vectors use contiguous storage locations in an underlying (typically larger) array. Both array and vector elements can be accessed and traversed with the help of iterators, and they can also be accessed randomly using indexes.

However, unlike basic arrays, vectors have a dynamic size meaning that whenever a new element is inserted or deleted, their size changes automatically. A new element can be inserted into or deleted from any part of a vector, and automatic reallocation for other existing items in the vector is applied. Nevertheless, computing time for insertion and deletion might differ depending on the location of the item, and how many items need to be reallocated. For example, the last item in a vector is typically removed at a constant time, because no resizing of the vector is typically needed for this operation, while an item is removed or inserted into the beginning or the middle of a vector at a linear time because all of the remaining items to the right of that element must be shifted.

Two common operations for both arrays and vectors are indexing and assigning to an index position that already exists. Both of these operations take the same amount of time no matter how large the array or vector is. When an operation like this is independent of the size of the array or vector they are $$O(1)$$.

Although not possible with basic arrays, a common programming technique is growing a vector. As we have seen, one way to create a longer vector is to use the push_back() method. The push_back() method is typically $$O(1)$$, provided there is adequate capacity in the underlying array.

First we’ll use push_back() method. Listing 3 shows the code for making our vector.

Listing 3

#include <iostream>
#include <vector>
using namespace std;

void test1(int num){
vector <int> vect;
vect.reserve(num);
for (int i = 0; i < num; i++){
vect.push_back(i);
}
}

int main() {
test1(1000);
}


To capture the time it takes for each of our functions to execute we will use C++’s ctime module. The ctime module is designed to allow C++ developers to make cross-platform timing measurements by running functions in a consistent environment and using timing mechanisms that are as similar as possible across operating systems.

To use ctime you create two clock objects. The first clock object marks the current start time; the second clock object marks the current time after the function runs a set number of times (the end time). To get the total runtime, you subtract the end time from the start time to get the elapsed time. The following session shows how long it takes to run each of our test functions 10,000 times within a for loop.

In the experiment above the statement that we are timing is the function call to test1. From the experiment, we see the amount of time taken by the push_back operation.

We can improve the runtime a bit further by setting an adequate reserve for the vector in advance. Doing this will keep us from having to move the entire vector to an adequately sized space in memory.

Now that we have seen how performance can be measured concretely you can look at Table 2 to see the Big-O efficiency of all the basic vector operations. When pop_back() is called, the element at the end of the vector is removed and it typically takes $$O(1)$$ but when erase() is called on the first element in the vector or anywhere in the middle it is $$O(n)$$. The reason for this lies in how C++ chooses to implement vectors. When an item is taken from the front of the vector, in C++ implementation, all the other elements in the vector are shifted one position closer to the beginning. This may seem silly to you now, but if you look at Table 2 you will see that this implementation also allows the index operation to be $$O(1)$$. This is a tradeoff that the C++ implementers thought was a good one.

Table 2: Big-O Efficiency of C++ Vector Operators

Operation

Big-O Efficiency

index []

O(1)

index assignment =

O(1)

push_back()

typically O(1)

pop_back()

O(1)

erase(i)

O(n)

insert(i, item)

O(n)

find(srt, stp, item)

O(log n) or O(n)

reserve()

O(n)

begin()

O(1)

end()

O(1)

size()

O(1)

The push_back() operation is $$O(1)$$ unless there is inadequate capacity, in which case the entire vector is moved to a larger contiguous underlying array, which is $$O(n)$$.

Note that the vector class provides a find command which can determine whether a given item is in the vector. It is $$O(log n)$$ if the vector is sorted and is $$O(n)$$ otherwise. We will explain why this is in Chapter 3.

As a way of demonstrating the difference in performance between push_back and insert, let’s do another experiment using the ctime module. Our goal is to be able to verify the performance of the pop_back() operation on a vector of a known size when the program pops from the end of the vector using pop_back(), and again when the program pops from the beginning of the vector using erase(). We will also want to measure this time for vectors of different sizes. What we would expect to see is that the time required to pop from the end of the vector will stay constant even as the vector grows in size, while the time to pop from the beginning of the vector will continue to increase as the vector grows.

Listing 4 shows one attempt to measure the difference between the pop_back() and erase().

There are a couple of things to notice about Listing 4. This approach allows us to time just the single pop_back() statement and get the most accurate measure of the time for that single operation. Because the timer repeats 10,000 times it is also important to point out that the vector is decreasing in size by 1 each time through the loop.

Listing 4

Q-4: Drag the operation(s) on the left to their corresponding Big(O) Review operations and thier Big(O)
• begin(), end(), size(), index [], index assignment = ,push_back(), pop_back()
• O(1)
• reserve(), erase(i), insert(i, item),find(srt, stp, item)
• O(n)
• find(srt, stp, item)
• O(log n)