Big O notation

Question 1

What does the big O notaion measures ?

Answer 1

The increase in time complexity when increasing the n of an algorythm

Question 2

What is the Mantissa problem

Answer 2

Its when memory cant store all the decimals of a number causing rounding errors

Question 3

How is the performance of an algorythm measured ?

Answer 3

Not by running time. But by the number of basic operations needed to solve the problem.
Typically the number of data points ( n ). The time can be used to measure big 8 bu the increase in time from increase in n

Question 4

What are the 4 algorithymic Tecniques

Answer 4

• Dynamic Programming
• Divide and Conquer
• Greedy Algo
• Recursion

Question 5

Explain what is dynamic programming

Answer 5

Uses a table ( catche ) to store intermidiate results

Question 6

Divide and Conquer

Answer 6

Divide instabce of a problem into two or more instances of the same type. Solve smaller intances recursively. Solution = SUM/COMBINING smaller solutions

Question 7

Greedy Algorythms

Answer 7

being greedy at the beggining:

• first solve for the best IMMIDIATE one ( without caring for optimization )
• Hope that choosing a local optimum at each step, it will result at a global optimum