Algorithms

Question 1

What are Algorithms?

Answer 1

The fastest and most efficient set of steps to solve a problem.

Question 2

How do you determine the necessary algorithm to use for a problem?

Answer 2

By breaking down the problem into steps and understanding the problem.

Question 3

What are algorithms composed of?

Answer 3

A clear problem statement, expected input & expected output.

Question 4

How do you measure the efficiency of an algorithm

Answer 4

Through calculating the Time & Space complexity

Question 5

What is time complexity?

Answer 5

Simple the measure of how long an algorithm takes. Run or compute time.

Question 6

What is space complexity?

Answer 6

A measure of how much memory was used in order to compute an algorithm.

Question 7

What are the 3 cases to measure complexity against?

Answer 7

Best case, Average case, Worst case

Question 8

The best case to test on is the worst case. What does “n” represent in the worst case?

Answer 8

The amount, length, or form of measure for a given input in an algorithm.

Question 9

What is a linear algorithm?

Answer 9

An algorithm that executes steps that walks through a data structure/input in a sequential manner.

Question 10

What is a binary algorithm?

Answer 10

An algorithm that executes steps within half of the data structure & continues to narrow down by half until a value is found.

Question 11

What is “Big O”? (Upper bound)

Answer 11

A notation that represents the complexity of an Algorithm in the worst-case scenario

Question 12

The O in “Big O” represents what?

Answer 12

Order of magnitude of complexity

Question 13

The “(n)” is considered what?

Answer 13

The function of a size

Question 14

Constant Time O(1) represents what?

Answer 14

The runtime per operation is the same no matter how large “n” or input data is.

Question 15

O(log n) or O(ln n) represents what?

Answer 15

Logarithmic Runtime.

Question 16

What is a Logarithmic Runtime (Sublinear)?

Answer 16

A runtime that is the inverse of exponentiation. When number of operations is logarithmic to the sample size (data).

Question 17

What is a Linear Runtime? O(n)

Answer 17

An algorithm that sequentially (step by step) moves through the input size.

Question 18

What is Quadratic Runtime? O(n2)

Answer 18

When an algorithm’s number of operation scales by n ^ 2 (input size squared) Big O of n squared

Question 19

What is Cubic Runtime? O(n3)

Answer 19

When an algorithm’s operations time scales w/ the input size by n ^ 3

Question 20

What is Quasilinear runtime? O(n log n)

Answer 20

Runs (log n) amount of operations per n or per input amount | Big O of n x log n

Question 21

What makes you have a long runtime?

Answer 21

The higher the amount of operations executed per n or input data results in longer runtime.

Question 22

What is Polynomial Runtime? O(n^k)

Answer 22

Means a runtime where its function of a size is squared, cubic or an exponent of some number.

Question 23

Is a Polynomial Runtime considered efficient?

Answer 23

Yes

Question 24

What are 2 bad runtime types?

Answer 24

Exponential Runtimes O(x^n)

Factorial Runtimes O(n!)