week 3

Question 1

Limitations of experiments with algorithms (running times)

Answer 1

.costly to implement algorithms
. dealing with many algorithms time consuming to find best one
. to compare algorithms needs same hardware and software environment

Question 2

linear growth rate ( runtime of algorithm increases linearly with an increase in input size)

Answer 2

linear growth rate is independent of hardware as it describes the complexity of the algorithm itself not hardware its executed on

Question 3

Hardware can affect running time by a …

Answer 3

CONSTANT FACTOR

Question 4

WE CAN SAY A function f(n) Is O(g(n)) if there are positive constants c and n₀ where f(n) <= c . g(n) for n >= n₀

Answer 4

WE CAN SAY A function f(n) Is O(g(n)) if there are positive constants c and n₀ where f(n) <= c . g(n) for n >= n₀

Question 5

Big O notation (
�

Answer 5

And we know Big-O is for upper bound, means the maximum time an algorithm may take.

Answer 6

Omega is lower bound, quite understood, the minimum time an algorithm may take.