Chapter 1 : What is an algorithm?

Effectiveness

means that you can perform each operation precisely to solve the problem.

variable n

The in an equation that describes the number of steps in an algorithm.

Definiteness

means that the steps are clear, concise, and unambiguous.

finite process

He (Donald Knuths) describes an algorithm as a definite, effective, and that receives input and produces output based on this input.

Finiteness

means that the algorithm stops after a finite number of steps.

amount of memory a program

The requires to store the data set.

Time complexity

is the maximum number of steps an algorithm takes to complete as n gets larger.

Temporary space

is the amount of memory your algorithm needs for intermediary processing, for example, if your algorithm needs to temporarily copy a list to transfer data.

big O notation

The for exponential complexity is O (c** n), where c is a constant.

worst possible scenario

An algorithms best- case complexity is how it performs with ideal input, and an algorithms worst- case complexity is how it performs in the for it.

Linear Time

: The next most efficient type of algorithm is one that runs in .

Exponential scaling

is the reason why it is so important to create long passwords.

order of magnitude

A(n) is a class in a classification system where each class is many times greater or smaller than the one before.

brute force algorithm

A(n) is a type of algorithm that tests every possible option.

Constant Time

: The most efficient order of magnitude is called constant time complexity.

amount of memory an algorithm

The needs for intermediary processing, for example, if your algorithm needs to temporarily copy a list to transfer data.

amount of time it

The takes a computer to execute an algorithm written in a programming language.

Big O notation

is a mathematical notation that describes how an algorithms time or space requirements (you will learn about space requirements later) increase as the size of n increases.

linear time

An algorithm runs in when it grows at the same rate as the problems size.

big O notation

Computer scientists use to create an order- of- magnitude function from T (n)

Constant Time

The most efficient order of magnitude is called constant time complexity

Logarithmic Time

Logarithmic time is the second most efficient time complexity

Linear Time

The next most efficient type of algorithm is one that runs in linear time

Log-Linear Time

An algorithm that runs in log-linear time grows as a combination (multiplication) of logarithmic and linear time complexities

Quadratic Time

After log-linear, the next most efficient time complexity is quadratic time

Cubic Time

After quadratic comes cubic time complexity