Algorithmic Efficiency

Question 1

Explain Big O

Answer 1

Describes the upper bound of the growth rate of an algorithm,
often used for worst case analysis.

Question 2

Explain Big Omega

Answer 2

Describes the lowerbound; the minimum rsources an algorithm requires,
Often used for best-case analysis.

Question 3

Explain Big Theta

Answer 3

Describes the tight bound; both upper and lower,
Often used as the average / expected case.

Question 4

What are the four issues when choosing an algorithm ?

Answer 4

Data size,
Memory usage,
Programming time,
Solution Quality.