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Hint

1

Effectiveness

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

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2

variable n

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

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3

Definiteness

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

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4

finite process

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

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5

Finiteness

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

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6

amount of memory a program

The ________ requires to store the data set.

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7

Time complexity

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

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8

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.

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9

big O notation

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

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10

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.

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11

Linear Time

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

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12

Exponential scaling

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

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13

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.

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14

brute force algorithm

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

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15

Constant Time

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

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16

amount of memory an algorithm

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

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17

amount of time it

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

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18

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.

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19

linear time

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

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20

big O notation

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

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21

Constant Time

The most efficient order of magnitude is called constant time complexity

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22

Logarithmic Time

Logarithmic time is the second most efficient time complexity

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23

Linear Time

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

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24

Log-Linear Time

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

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25

Quadratic Time

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

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26

Cubic Time

After quadratic comes cubic time complexity

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