Big O notation

Question 1

Big O Constant Time (random access in array)

Answer 1

O(1) Constant Time

Question 2

Big O Linear Time (Delete value linked List)

Answer 2

O(n) Linear Time (Looping Arrays, Lists)

Question 3

What is Big O Notation

Answer 3

1) The efficiency of your Algorithm

2) Used to describe run-time characteristics of our data structure and algorithms

Question 4

What is linear time

Answer 4

Where the number of elements in the data structure increased and the operation takes longer (looping, etc)

Question 5

What is faster O(n) or O(2^n)

Answer 5

O(n)

Question 6

Big O What is Logarithmic (search algorithms)

Answer 6

O(Log n) Logarithmic

Question 7

Big O What is Quasilinear ( Sorting algorithms)

Answer 7

O(n log n) Quasilinear

Question 8

Big O What is Quadratic (Nested Loops )

Answer 8

O(n powerOf 2) Quadratic

Question 9

Exponential (Recursion, Fibonacci series) Gets slower the more that is calculated

Answer 9

O(2 powerOf n) Recursion,

Question 10

Operations that takes the same amount of time regardless of the number of elements

Answer 10

O(1)

Question 11

Iteration through a collection (for loop, while loop)

Answer 11

O(n)

Question 12

Looping through the same collection x2 (nested)

Answer 12

O(n powerOf 2)

Question 13

Fibonacci series - recursion (branches squared depth)

Answer 13

O(2 powerOf n)

Question 14

Search sorted array (binary tree)

Answer 14

O(log n)

Question 15

simplify O(n powerOf 2 + n)

Answer 15

O(n powerOf) Drop non-dominated terms

Question 16

simplify O(3n)

Answer 16

O(n) Drop constants

Question 17

Describe two Loops

Answer 17

O(n+m)

Question 18

Nested for Loop two different arrays

Answer 18

O(n*m)

Question 19

Nest for Loop with same array

Answer 19

O(n powerOf 2)

Question 20

Big O

Constant

Answer 20

O(1)

Question 21

Big O

Logarithmic

Answer 21

O(log n)

Question 22

Big O

Linear

Answer 22

O(n)

Question 23

Big O

Log Linear

Answer 23

O(n log n)

Question 24

Big O

Quadratic

Answer 24

O(n^2)

O(n powerOf 2)

Question 25

Big O

Cubic

Answer 25

O(n^3)

Question 26

Big O

Exponential

Answer 26

O(2^n)

Question 27

Big O simplify

O(1) + (O(n/2)) = O (1/2 *n) + O(10)

print 1st[0] # O(1)

midpoint = len(1st)/2 # O(1/2 * n)

for val in list[:midpoint]:
print (val)

for x in range(10) # O(10)
print(‘hello world’)

Answer 27

O(1) + (O(n/2)) = O (1/2 *n) + O(10)

# O(1 + n/2 + 10)
# AS IT GROWS lARGER 1 AND 10 BECOME INSUGNIFICATE 
#  o(n)

Question 28

Big O

n*n (two for loops)

Answer 28

n squared

O(n^2)

dangers for large inputs

Question 29

Big O

Only do an action once

Answer 29

O(1)

Question 30

Big O - Python

Lists

Answer 30

Indexing and Assigning

O(1)

Question 31

Big O - Python

Dictionary

Answer 31

O(1)