What Is An Algorithm? | What Exactly Is Algorithm? | Algorithm Basics Explained | Simplilearn

00:13:17
https://www.youtube.com/watch?v=cuhLSGGV-1k

Résumé

TLDRThe video explains what an algorithm is, its characteristics, how to write one, and how they are analyzed. An algorithm is a step-by-step method or set of rules for performing calculations or solving problems in computer science. Key characteristics include clarity, having well-defined inputs and outputs, feasibility, finiteness, and language independence. Writing an algorithm does not follow specific rules but should handle conditional and iterative statements effectively. Algorithms are analyzed through priori (theoretical) and posteriori (practical) methods to assess time and space complexity. Time complexity deals with how runtime scales with input size, using Big O notation, while space complexity involves memory usage. Benefits of algorithms include easy understanding and breaking down tasks, though they can be time-consuming and complex to demonstrate with branching and loops. The video also contrasts algorithms with programming, noting that while algorithms can be run by humans, programs are executed through compilers, and algorithms are language neutral while programming requires a specific language.

A retenir

  • 🤖 Algorithms are essential for problem-solving in computer science.
  • 🔍 They have distinct characteristics like clarity and finiteness.
  • ✍️ Writing an algorithm involves conditional and iterative logic.
  • 🕒 Time complexity and space complexity are key analysis areas.
  • 🚫 Algorithms are language-independent and act as a logical plan.
  • 📈 Big O notation is used for expressing an algorithm's complexity.
  • 💡 Algorithms can be hypothetical, whereas programming is executable.
  • 🔄 They help in structured problem-solving with clear steps.
  • 🛠 Algorithms can simplify complex problems into manageable parts.
  • 👥 Humans can understand algorithms, unlike machine-focused code.

Chronologie

  • 00:00:00 - 00:05:00

    In this session, the agenda includes understanding what an algorithm is, its characteristics, how to write one, its analysis and complexities, the advantages and disadvantages of algorithms, and the differences between algorithms and programming. The session begins with a simple explanation of what an algorithm is, using the example of making tea. An algorithm consists of a series of steps to achieve a desired result. It's stressed that algorithms in computer science are a set of rules to perform specific calculations or problem-solving tasks, highlighting the step-by-step approach required for execution.

  • 00:05:00 - 00:13:17

    Writing an algorithm involves creating a step-by-step solution without being tied to any specific programming language and should incorporate conditional and iterative statements like if-else or loops. The process begins with defining the problem domain and crafting the algorithm accordingly. An example provided is determining the largest number among three variables, using conditional if-else statements. The session then explains algorithm analysis through 'priori' and 'post' analysis, evaluating time and space complexity using Big O notation. It discusses the counting of steps in time complexity and space allocation in space complexity. The tutorial ends with the pros and cons of algorithms and contrasts algorithms with programming, emphasizing algorithms as a design or logical series of steps, while programming involves implementation and execution.

Carte mentale

Mind Map

Questions fréquemment posées

  • What is an algorithm?

    An algorithm is a collection of rules or instructions for solving problems or performing calculations.

  • What are the main characteristics of an algorithm?

    Main characteristics include being clear and unambiguous, having well-defined inputs and outputs, feasibility, finiteness, and language independence.

  • How do you write an algorithm?

    Writing an algorithm involves defining a sequence of steps that lead to a solution. It should handle conditional and iterative statements effectively.

  • What is algorithm analysis?

    Algorithm analysis involves theoretical (priori) and practical (posterior) evaluations to understand time and space requirements.

  • How is time complexity measured?

    Time complexity is often measured using Big O notation, indicating how the runtime of an algorithm scales with input size.

  • What is space complexity?

    Space complexity refers to how much memory an algorithm requires, including program space, constant storage, variable storage, and so on.

  • What are the pros and cons of algorithms?

    Pros include being easy to understand and breaking problems into smaller steps. Cons include being time-consuming to write and challenging for demonstrating branching and loops.

  • How do algorithms differ from programming?

    Algorithms are a logical plan or solution design while programming involves executing this plan in a programming language.

  • Can humans execute algorithms?

    Yes, humans can understand and execute an algorithm because it's designed using human-friendly logic.

  • Are algorithms dependent on programming languages?

    No, algorithms are not dependent on specific programming languages.

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  • 00:00:08
    hey everyone welcome to simply learn's
  • 00:00:10
    youtube channel in this session we will
  • 00:00:12
    discuss what is an algorithm but before
  • 00:00:15
    we begin let me tell you guys that we
  • 00:00:17
    have daily updates on multiple
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    technologies if you're a tech geek in a
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    update from simply done
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    now without further ado let's get
  • 00:00:32
    started with the agenda for today's
  • 00:00:34
    session
  • 00:00:37
    first we will get started with what is
  • 00:00:39
    an algorithm
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    followed by that we will look into the
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    characteristics of algorithm
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    next we will learn how to write an
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    algorithm
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    moving ahead we will look at algorithm
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    analysis and algorithm complexities
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    after that we will discuss the
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    advantages and disadvantages of
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    algorithms and finally we will see some
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    differences between algorithms and
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    programming
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    i hope i made myself clear with the
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    agenda now let's get started with the
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    first point that is what exactly is an
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    algorithm
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    how does algorithm work in real life
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    so we'll have a use case here
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    on my screen we have a lady and she is
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    shreya shreya wants to make some tea and
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    for that she is following some steps
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    so the first step is she'll take a pan
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    and fill it with water and place it on a
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    gas stove
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    followed by that the second step is
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    she'll make sure that the water comes to
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    a nice boil and then she adds some tea
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    leaves and sugar followed by step 2 we
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    have step 3 where she'll allow the leaf
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    to be fully expanded and she'll add some
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    milk after that
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    next we have the stefo where she will
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    wait for the tea to come for a good
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    decent boil
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    next we have step 5 where she'll turn
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    off the gas and she'll take the pan off
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    from the stove and the tea is ready to
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    get filtered and lastly we have the step
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    6 where she is all set with the tea
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    in computer science an algorithm is a
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    method or set of rules that we must be
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    following to perform some specific
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    calculations or other problem solving
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    operations
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    as a result an algorithm is a collection
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    of rules or instructions that govern how
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    a work needs to be conducted step by
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    step to achieve the desired results to
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    accomplish a task
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    we first provide some input and then
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    follow a sequence of steps to acquire
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    the required outcome of the input so
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    that is the fundamental definition of an
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    algorithm and we also had a use case and
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    now we will move ahead and understand
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    the characteristics of algorithms
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    so basically we have six characteristics
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    of algorithm so the first one is it
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    should be clear and unambiguous the
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    second one is that it has to be well
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    defined inputs and the third one is that
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    it has to be having well-defined outputs
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    as well the fourth property of an
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    algorithm is its feasibility followed by
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    that
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    finiteness and finally we have language
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    independence
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    now we will look into each one of the
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    characteristics in detail so first we
  • 00:03:16
    have clear and unambiguous so this
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    statement is the one which states that
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    the algorithm should be straightforward
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    simple and easily readable
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    each one of its steps should be distinct
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    in every way and lead it to a single
  • 00:03:30
    conclusion
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    so the next one is it should be having
  • 00:03:34
    well-defined inputs so this means that
  • 00:03:37
    an algorithm should indicate what output
  • 00:03:40
    can be expected as well as the input
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    should be properly well-defined now we
  • 00:03:45
    have the third one similarly we should
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    also have well-defined outputs so the
  • 00:03:50
    well-defined output characteristics of
  • 00:03:51
    an algorithm means that the algorithm
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    must clearly indicate what output can be
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    expected as well as the output should
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    also be well defined followed by the
  • 00:04:02
    third characteristic we have the fourth
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    characteristic that is the finiteness of
  • 00:04:07
    algorithm so the finiteness of algorithm
  • 00:04:09
    says that the algorithm must be finite
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    and it must not result in infinite loops
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    or similar situations
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    so followed by the fourth one we have
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    the fifth characteristic that defines
  • 00:04:21
    about the feasibility of an algorithm
  • 00:04:24
    so feasibility of an algorithm states
  • 00:04:27
    that the algorithm must be really simple
  • 00:04:29
    generic and practical
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    it must be able to be executed with
  • 00:04:33
    resources available
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    now the last characteristic of the
  • 00:04:37
    algorithm is being independent of the
  • 00:04:40
    language
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    so it means that the algorithm should be
  • 00:04:44
    defined in such a way that it is
  • 00:04:46
    completely independent of the language
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    it must be simple instructions that can
  • 00:04:50
    be implemented using any programming
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    language
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    so these were the six major
  • 00:04:55
    characteristics of algorithm now let's
  • 00:04:57
    move ahead and understand how to write
  • 00:05:00
    an algorithm
  • 00:05:01
    writing an algorithm does not have any
  • 00:05:03
    hard and fast rules
  • 00:05:05
    next algorithms are never created to
  • 00:05:08
    support a specific programming language
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    as we all know algorithm should be
  • 00:05:13
    capable enough to support the
  • 00:05:14
    conditional statements such as if if
  • 00:05:17
    else switch etc
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    an algorithm should be capable enough to
  • 00:05:20
    execute some of the conditional
  • 00:05:22
    statements such as if if else switch etc
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    and also it must be capable to execute
  • 00:05:28
    the iterative statements such as for
  • 00:05:30
    loop while loop do while loop etc
  • 00:05:34
    we usually create algorithms in a
  • 00:05:36
    step-by-step manner however this isn't
  • 00:05:38
    always the case
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    after the problem domain has been well
  • 00:05:42
    defined algorithm writing is a procedure
  • 00:05:45
    that is carried out
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    we build an algorithm to find a solution
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    to a problem a problem can be resolved
  • 00:05:51
    in a variety of ways let's look at an
  • 00:05:53
    example of how to write an algorithm
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    consider the following scenario we need
  • 00:05:58
    to create an algorithm to determine the
  • 00:06:00
    largest number among the three given
  • 00:06:02
    numbers x y and z as a result the
  • 00:06:06
    initial solution to the problem can be
  • 00:06:08
    written in this manner so we begin with
  • 00:06:11
    reading all the three numbers and in the
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    second step we will have a condition
  • 00:06:15
    that says if x is greater than y we will
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    proceed to step four else we will
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    proceed to step 5.
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    next in step 4 we establish a condition
  • 00:06:24
    that if x is greater than z then x is
  • 00:06:27
    the largest of the three numbers
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    otherwise z is the largest
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    if y is greater than z z will be the
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    greatest number otherwise y will be the
  • 00:06:37
    largest number amongst all the three in
  • 00:06:40
    step five
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    and the procedure will be terminated
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    let's have a look at what the next
  • 00:06:45
    solution has to offer in this algorithm
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    we begin by reading all the three
  • 00:06:50
    variables then compare all the three
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    numbers in the third step to determine
  • 00:06:54
    the largest number which is if x is
  • 00:06:57
    greater than y then there is another if
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    else statement that states that if x is
  • 00:07:02
    greater than y then x is largest
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    otherwise z is the largest
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    then this if else statement which says
  • 00:07:11
    that if y is greater than z then y is
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    greater otherwise z is the greatest of
  • 00:07:17
    the three numbers so this is how we have
  • 00:07:20
    multiple solutions to the same problem
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    so after discussing these solutions we
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    will move ahead and understand algorithm
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    analysis
  • 00:07:28
    the algorithm may be studied in two
  • 00:07:30
    levels first before it is made and then
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    after it is created the two analysis of
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    an algorithm are as follows the first
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    one is priori analysis and the second
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    one is the post analysis so first we
  • 00:07:43
    will talk about the priori analysis
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    priori analysis refers to the
  • 00:07:48
    theoretical analysis of an algorithm
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    performed before its implementation
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    before implementing the algorithm other
  • 00:07:54
    parameters such as processor speed might
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    be considered
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    which does not affect the implementation
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    component next we will talk about post
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    analysis
  • 00:08:05
    a practical analysis of an algorithm is
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    called as posterior analysis and it
  • 00:08:10
    utilizes any computer language to build
  • 00:08:12
    the algorithm the purpose of this
  • 00:08:15
    analysis is to determine how much time
  • 00:08:17
    and space the algorithm takes to operate
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    now after being understood the algorithm
  • 00:08:23
    analysis we will be able to comprehend
  • 00:08:25
    the algorithm complexity two methods can
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    be used to assess the algorithm
  • 00:08:30
    complexity the first one is time
  • 00:08:32
    complexity which states that the amount
  • 00:08:35
    of time required to finish an
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    algorithm's execution is known as its
  • 00:08:39
    time complexity
  • 00:08:41
    followed by time complexity the other
  • 00:08:43
    one is the space complexity which is
  • 00:08:46
    calculated by the quantity of space
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    required to solve a problem and produce
  • 00:08:50
    an output of space complexity of an
  • 00:08:52
    algorithm
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    let's take a closer look at these
  • 00:08:55
    complexities
  • 00:08:57
    now we will begin with time complexity
  • 00:08:59
    of an algorithm the big o notation is
  • 00:09:02
    used to express an algorithm's time
  • 00:09:04
    complexity the asymptomatic notation is
  • 00:09:07
    used to depict temporary complexity of
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    big o notation
  • 00:09:12
    the time complexity is mainly determined
  • 00:09:14
    by counting the number of steps required
  • 00:09:17
    to complete the task
  • 00:09:19
    let's take a look at an example to
  • 00:09:21
    understand this time complexity in a
  • 00:09:23
    much better way
  • 00:09:24
    if we want to find out a multiplication
  • 00:09:26
    of n numbers we can do it by executing a
  • 00:09:29
    loop from 1 to n then calculating
  • 00:09:33
    multiplication when the loop ends then
  • 00:09:35
    multiplication holds the n numbers
  • 00:09:38
    multiplication and last we return the
  • 00:09:40
    calculated multiplication
  • 00:09:42
    the loop statements time complexity will
  • 00:09:45
    be at least n and when the number of n
  • 00:09:47
    increases the time complexity will also
  • 00:09:51
    increase
  • 00:09:52
    while the code's complexity that is
  • 00:09:54
    returns multiplication will remain
  • 00:09:56
    constant because its value is unaffected
  • 00:09:59
    by the value of n and it will obtain the
  • 00:10:02
    result in every single step
  • 00:10:05
    next we will discuss about space
  • 00:10:07
    complexity which is expressed with big o
  • 00:10:10
    notation just as time complexity
  • 00:10:15
    these four items are stored in space
  • 00:10:18
    complexity it initially saves program
  • 00:10:21
    instructions then stores constant values
  • 00:10:24
    then stores variable values and finally
  • 00:10:27
    it keeps track of function cells jumping
  • 00:10:29
    statements and so on the sum of
  • 00:10:32
    auxiliary space and input size is used
  • 00:10:35
    to calculate space complexity the extra
  • 00:10:38
    space or temporary space used by an
  • 00:10:40
    algorithm is referred to an auxiliary
  • 00:10:42
    space after learning about algorithm
  • 00:10:44
    complexity we will look at some of the
  • 00:10:46
    algorithms pros and cons so let's start
  • 00:10:50
    with the advantages of algorithms
  • 00:10:52
    firstly algorithms are easy to
  • 00:10:54
    understand
  • 00:10:55
    next algorithms are step-by-step
  • 00:10:58
    representation of a solution and lastly
  • 00:11:01
    the problem is broken down into smaller
  • 00:11:03
    bits or steps in an algorithm making it
  • 00:11:06
    easier for the programmer now an
  • 00:11:09
    algorithm also have various drawbacks
  • 00:11:12
    one of which being it takes long time to
  • 00:11:15
    write hence it is time consuming
  • 00:11:17
    second con of the algorithm is
  • 00:11:20
    algorithms make it harder to demonstrate
  • 00:11:22
    branching and looping statements
  • 00:11:25
    so followed by the advantages and
  • 00:11:27
    disadvantages of algorithms we now enter
  • 00:11:30
    into the final segment where we discuss
  • 00:11:32
    the differences between algorithms and
  • 00:11:34
    programming so the first difference is
  • 00:11:36
    that
  • 00:11:37
    an algorithm is a type of programming on
  • 00:11:40
    the other hand a program is more
  • 00:11:42
    directly associated with the computer
  • 00:11:44
    performing one or more tasks but an
  • 00:11:47
    algorithm is more of a notation a
  • 00:11:49
    technique for solving a problem
  • 00:11:52
    now let us discuss the second difference
  • 00:11:55
    an algorithm can be decoded or run by a
  • 00:11:58
    human whereas the program is only run or
  • 00:12:02
    decoded by a set of compilers of the
  • 00:12:04
    computer
  • 00:12:06
    now the third difference
  • 00:12:08
    basically an algorithm is a design of
  • 00:12:10
    the solution to the problem
  • 00:12:13
    here we can only analyze the solution
  • 00:12:16
    but we can never run it
  • 00:12:18
    but whereas on the other hand
  • 00:12:19
    programming is completely a different
  • 00:12:22
    ballgame here you can directly implement
  • 00:12:24
    a certain phase or an entire program and
  • 00:12:28
    test the programs and get an output
  • 00:12:30
    so the fourth and the last difference
  • 00:12:32
    between an algorithm and programming is
  • 00:12:35
    an algorithm is written using natural
  • 00:12:37
    human vocabulary
  • 00:12:38
    whereas programs are written using any
  • 00:12:41
    programming language such as c c plus
  • 00:12:43
    plus java python etc
  • 00:12:46
    now with that we have come to an end of
  • 00:12:47
    this tutorial on what is an algorithm if
  • 00:12:50
    you have any queries regarding the
  • 00:12:52
    topics covered in this session then
  • 00:12:53
    please feel free to let us know in the
  • 00:12:55
    comment section below and our team of
  • 00:12:56
    experts will be happy to resolve all
  • 00:12:58
    your queries until next time thank you
  • 00:13:00
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Tags
  • algorithm
  • programming
  • time complexity
  • space complexity
  • Big O notation
  • algorithm analysis
  • algorithm characteristics