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Let n > 0 ( usually n is the size of the input ) and let f ( n ) and g ( n )

Let n >0(usually n is the size of the input) and let f (n) and g(n) be two
monotone and non-negative functions. We say that f (n)= O(g(n)) if there
exists a constant c and a constant n0 such that: f (n)<= cg(n),n > n0.
The exercise asks you to use the definition to prove the following:
Let f (n)=100n2+10n +1000. Then f (n)= O(n2).
Let f (n)=100n +0.001n log n. Then f (n)= O(n log n).
Let f (n)=50n log n +30n. Then f (n)= O(n3).
Feel free to use standard properties or inequalities for the logarithm without
proof.
Ex.3 Imagine you had an algorithm with runtime T (n) satisfying the recursion
T (n)= T (n/2)+1(and T (1)=1). You have probably seen the binary search
algorithm that exactly obeys that recursion. Prove that T (n)= O(log n). Then,
imagine another algorithm with Q(n)= Q(n/2)+ n (and Q(1)=1). Prove that
Q(n)= O(n).2
Ex.4 Describe an O(n log n)-time algorithm that, given a set S of n integers
and another integer x, determines whether or not there exist two elements in S
whose sum is exactly x.
Ex.5 Follow the O() definition & prove: Is 2n+2023= O(2n)? Is 22n = O(2n)?
Ex.6 Imagine you came up with an algorithm that depends on a parameter
k in [1, n] you can control. After calculations, the runtime ends up being
O(nk + n2
k ). What choice of k would you make for your algorithm?
Ex.7 This is from a common coding interview question: Imagine you are given a
list of n integers. Assume they are distinct if that helps you. Give an algorithm
that finds the smallest and the second smallest element in the list, using at most
n + log n comparisons, and explain why your algorithm needs at most n + log n
comparisons. (Hint: think again of the pairing process we saw in class, when
trying to find the max and the min simultaneously; where is the second smallest
element located? Here, thinking about tennis or chess tournaments may help
you.)
2For asymptotic notation exercises, if it helps you can think that n is a power of 2.
2

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