Chapter 1 : What is an algorithm?

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

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

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

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

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

The ________ requires to store the data set.

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

________ 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.

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

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.

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

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

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

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

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

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

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

________ 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.

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

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

The most efficient order of magnitude is called constant time complexity

Logarithmic time is the second most efficient time complexity

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

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

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

After quadratic comes cubic time complexity