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bigoh8

Language/Type: C++ algorithm analysis big-oh
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Give a tight bound of the nearest runtime complexity class for each of the following code fragments in Big-Oh notation, in terms of the variable N. In other words, write the code's growth rate as N grows. Write a simple expression that gives only a power of N using a caret ^ character for exponentiation, such as O(N^2) to represent O(N2) or O(log N) to represent O(log2 N). Do not write an exact calculation of the runtime such as O(2N3 + 4N + 14). 

// a)
int sum = 0;
for (int i = 0; i < 1000; i++) {
    for (int j = 1; j < N * 2; j++) {
        sum++;
    }
    for (int k = 0; k < i; k++) {
        sum++;
    }
}
cout << sum << endl;
 		answer: 
// b)
vector<int> v;
for (int i = 0; i < N; i++) {
    v.insert(v.begin(), i);
}
while (!v.empty()) {
    v.erase(v.begin());
}
cout << "done!" << endl;
 		answer: 
// c)
queue<int> queue;
for (int i = 1; i <= N; i++) {
    queue.push(i * i);
}
map<int, int> map;
while (!queue.empty()) {
    int k = queue.front();
    queue.pop();
    map[k] = N * N;
}
cout << "done!" << endl;
 		answer: 
// d)
unordered_set<int> set;
for (int i = 0; i < N; i++) {
    set.insert(i);
}
stack<int> stack;
for (int i = 0; i < N * N; i++) {
    stack.push(i);
}
for (int i = 0; i < N; i++) {
    set.erase(i);
    stack.pop();
}
cout << "done!" << endl;
 		answer: 
// e)
vector<int> v;
for (int i = 1; i <= 1000000000; i++) {
    v.push_back(i);
}
v.clear();
cout << "done!" << endl;
 		answer:

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