1. Consider the following polynomial with n terms, where ai is the ith coefficient:
P (x) = n−1∑ i=0
aix i = a0 + a1x+ a2x
2 + …+ an−1x n−1
Below is pseudocode of a naive algorithm that calculates P (x) for some input x:
1 Function pow(x, i): 2 // x is a number, i is an integer, returns xi
3 exp := 1 4 p := 1 5 while p ≤ i do 6 exp = exp ∗ x 7 p += 1
8 return exp
9 Function calculatePoly1(x, a, n): 10 // x is a number, a is a list of numbers, n is the length of a 11 sum := 0 12 i := 0 13 while i < n do 14 sum += a[i] * pow(x, i) 15 i += 1
16 return sum
(a) (4 points) Prove a tight bound for the runtime complexity of calculatePoly1 using sum- mations.
(b) (4 points) Give a tight bound for the space complexity of calculatePoly1. Explain your bound.
Assume that all integers and floats require the same, constant amount of memory.
Note: Unless otherwise specified, we ignore the space required to store the input argu- ments, since this will remain the same for all algorithms that solve the same problem.
(c) (10 points) Propose an algorithm calculatePoly2 with asymptotically better runtime compared to calculatePoly1 and provide its pseudocode.
Give tight bounds for your algorithm’s runtime and space complexity. Explain your bounds.
Your answer may not use a built-in exponentiation operator (like xˆn or x**n). You may use other basic operators like addition, multiplication, etc.
2. For each of the following questions, briefly explain your answer.
(a) (4 points) If an algorithm’s best case runtime is Θ(n), is it possible that it takes Θ(n2) time on some inputs?
(b) (4 points) If an algorithm’s best case runtime is Θ(n), is it possible that it takes Θ(n2) time on all inputs?
(c) (4 points) If an algorithm’s worst case runtime is O(n2), is it possible that it takes o(n2) time on some inputs?
(d) (4 points) If an algorithm’s worst case runtime is O(n2), is it possible that it takes o(n2) time on all inputs?
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