1626184080
Die O-Notation EINFACH ERKLÄRT! (Landau Notation)
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In diesem Video erkläre ich dir, was die O-Notation bzw. die Landau Notation ist. Diese wird dazu verwendet um die Komplexität von Algorithmen zu beschreiben. Ich werde dir in diesem Video erklären welche Arten von Komplexität es gibt, was eine Komplexitätsklasse ist und welche Komplexitätsklassen du häufig antreffen wirst. Außerdem zeige ich dir verschiedene Beispiele.
Zum Video über den Bubblesort Algorithmus: https://www.youtube.com/watch?v=jMId4dq6p44
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____ Weiterführende Videos_____________________
Was ist eine Bitmap?: https://www.youtube.com/watch?v=QL7CIZYw4D4
Bild zu ASCII in C#: https://www.youtube.com/watch?v=v6LVre9_Exo
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Lerne C# in einer Stunde: https://www.youtube.com/watch?v=tRfZMfkJ-yg
Die komplette Python Playlist: https://www.youtube.com/watch?v=oxXAb8IikHM&list=PL_pqkvxZ6ho3u8PJAsUU-rOAQ74D0TqZB
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Klartext! Das wichtigste Video für Programmier-Einsteiger (Motivation): https://www.youtube.com/watch?v=43RKY5bJK3M
Hier zeigen wir dir, wie man Spiele programmieren kann: https://www.youtube.com/watch?v=LPe6o55B8Ko
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1626184080
Die O-Notation EINFACH ERKLÄRT! (Landau Notation)
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🔹 Kostenlos die Programmier-Einsteiger-Serie sichern:
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In diesem Video erkläre ich dir, was die O-Notation bzw. die Landau Notation ist. Diese wird dazu verwendet um die Komplexität von Algorithmen zu beschreiben. Ich werde dir in diesem Video erklären welche Arten von Komplexität es gibt, was eine Komplexitätsklasse ist und welche Komplexitätsklassen du häufig antreffen wirst. Außerdem zeige ich dir verschiedene Beispiele.
Zum Video über den Bubblesort Algorithmus: https://www.youtube.com/watch?v=jMId4dq6p44
____ Über diesen Kanal_____________________
Möchtest du Schritt für Schritt und leichtverständlich das Programmieren lernen? Auf unserem Kanal findest du Hunderte von Videos zu den Programmiersprachen C#, Python und Java.
Jetzt kostenlos abonnieren: https://www.youtube.com/channel/UCVdfgrCLfJQfO5EgPlzaYAQ?sub_confirmation=1
____ Weiterführende Videos_____________________
Was ist eine Bitmap?: https://www.youtube.com/watch?v=QL7CIZYw4D4
Bild zu ASCII in C#: https://www.youtube.com/watch?v=v6LVre9_Exo
____ Komplett neu in der Materie? Dann starte hier!__
✘ Werbung: Kostenlose Programmier-Einsteiger-Serie - https://programmieren-starten.de/pev-lp1/?utm_source=youtube&utm_medium=share&utm_term=youtube-video&utm_content=link-in-videobeschreibung&utm_campaign=pev
Lerne C# in einer Stunde: https://www.youtube.com/watch?v=tRfZMfkJ-yg
Die komplette Python Playlist: https://www.youtube.com/watch?v=oxXAb8IikHM&list=PL_pqkvxZ6ho3u8PJAsUU-rOAQ74D0TqZB
Die komplette Java Playlist: https://www.youtube.com/watch?v=dcPM1NrgIdA&list=PL_pqkvxZ6ho1foDU2vEutcBXwD13SsjuZ
Klartext! Das wichtigste Video für Programmier-Einsteiger (Motivation): https://www.youtube.com/watch?v=43RKY5bJK3M
Hier zeigen wir dir, wie man Spiele programmieren kann: https://www.youtube.com/watch?v=LPe6o55B8Ko
____ Folge uns auch auf Instagram_____________________
Wenn dir unsere Youtube Inhalte gefallen, dann solltest du uns definitiv auch auf Instagram folgen. Dort posten wir auf fast täglicher Basis interessante Fakten, Übungsaufgaben, Quizze, Impulse und vieles mehr.
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#developer
1598508720
What Is Big O Notation?
As programmers, we often find ourselves asking the same two questions over and over again:
- How much time does this algorithm need to complete?
- How much space does this algorithm need for computing?
To put it in other words, in computer programming, there are often multiple ways to solve a problem, so
- How do we know which solution is the right one?
- How do we compare one algorithm against another?
The big picture is that we are trying to compare how quickly the runtime of algorithms grows with respect to the size of their input. We think of the runtime of an algorithm as a function of the size of the input, where the output is how much work is required to run the algorithm.
To answer those questions, we come up with a concept called Big O notation.
- Big O describes how the time is taken, or memory is used, by a program scales with the amount of data it has to work on
- Big O notation gives us an upper bound of the complexity in the worst case, helping us to quantify performance as the input size becomes arbitrarily large
- In short, Big O notation helps us to measure the scalability of our code
Time and Space Complexity
When talking about Big O Notation it’s important that we understand the concepts of time and space complexity, mainly because_ Big O Notation_ is a way to indicate complexities.
Complexity is an approximate measurement of how efficient (or how fast) an algorithm is and it’s associated with every algorithm we develop. This is something all developers have to be aware of. There are 2 kinds of complexities: time complexity and space complexity. Time and space complexities are approximations of how much time and space an algorithm will take to process certain inputs respectively.
Typically, there are three tiers to solve for (best case scenario, average-case scenario, and worst-case scenario) which are known as asymptotic notations. These notations allow us to answer questions such as: Does the algorithm suddenly become incredibly slow when the input size grows? Does it mostly maintain its fast run time performance as the input size increases?
#performance #development #big o complexity #big o notation #big data
1625640780
Time Complexity & Big O notation | DSA-One Course - All you Need in One place
Hey guys, In this video, we’ll be talking about Time complexity and Big O notation. This is the first video of our DSA-One Course. We’ll also learn how to find the time complexity of Recursive problems.
Practice here: https://www.interviewbit.com/courses/programming/topics/time-complexity/
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time complexity,time complexity of algorithms,time complexity analysis,complexity,time complexity tutorial,time and space complexity,time complexity explained,examples of time complexity,time,space complexity,time complexity in hindi,time complexity examples,analysis of time complexity,time complexity calculation,how to calculate time complexity,time and space complexity in hindi,time complexity of algorithms in hindi,what is time complexity in data structure,all time complexity
#big o #big o notation #time complexity
1597227180
An Overview of Big-O Notation
How the efficiency between same purpose algorithms are judged
When you first started programming, the primary concern was figuring out an algorithm (or function, when put into practice) that would accomplish the task at hand. As your skills progressed, you started working on larger projects and studying concepts that would prepare you for a career in software engineering. One of the first concepts you would inevitably come across is asymptotic notation, or what is colloquially known as Big O Notation.
In short, Big O Notation describes how long it takes a function to execute (runtime) as the size of an input becomes arbitrarily large. Big O can be represented mathematically as O(n), where “O” is growth rate (or order) of the function, and “n” is the size of the input. Translated into English, the runtime grows on the order of the input, or in the case of say O(n²), the order of the square of the size of the input.
This is a very important concept that will come up not only in your technical interviews, but also during your career when implementing solutions to handle large datasets. In this post I’ll give a brief overview on Big O analysis, simplifications, and calculations.
Big O Analysis
When using Big O to analyze an algorithm (asymptotic analysis), it should be noted that it primarily concerns itself with the worst and average case scenarios. For example, the runtime of an algorithm which sequentially searches a data set for a value that happens to be last, would be the worst case scenario.
Figuring out the worst case scenario is safe and thus never underestimated, though sometimes it may be overly pessimistic. Ultimately whether you analyze for worst or average case will depend on the use of your algorithm. For the typical problem the average case of an algorithm may be suitable, for cryptographic problems it’s usually best to seek out the worst case.
Heuristics for Calculating Big O
When calculating Big O, there are a couple of shortcuts that can help you
expedite the process:
- Arithmetic, assignment, accessing an element in a array/object (by index/key) are all constant time, O(1)
- In a loop, the runtime is the loop itself multiplied by whatever is in the loop
Keep these in mind for when you calculate the Big O for an algorithm I’ll provide towards the end.
#programming #big-o-notation #algorithms #algorithms
1594945860
Big O Notation and Time Complexity
Big O notation is a simplified analysis of an algorithm’s efficiency. Big O notation gives us an algorithm’s complexity in terms of input size, N. It gives us a way to abstract the efficiency of our algorithm or code from the machines/computers they run on. We don’t care how powerful our machine is, but rather, the basic steps of the code. We can use big O to analyze both time and space. I will go over how we can use Big O to measure time complexity using Ruby for examples.
Types of measurement
There are a couple of ways to look at an algorithm’s efficiency. We can examine worst-case, best-case, and average-case. When we examine big O notation, we typically look at the worst-case. This isn’t to say the other cases aren’t as important.
General rules
- Ignore constants
5n ->O(n)
Big O notation ignores constants. For example, if you have a function that has a running time of 5n, we say that this function runs on the order of the big O of N. This is because as N gets large, the 5 no longer matters.
2. In the same way that N grows, certain terms “dominate” others
Here’s a list:
O(1) < O(logn) < O(n) < O(nlogn) < O(n²) < O(2^n) < O(n!)
We ignore low-order terms when they are dominated by high order terms.
Constant Time: O(1)
This basic statement computes x and does not depend on the input size in any way. This is independent of input size, N. We say this is a “Big O of one” or constant time.
#time-complexity #algorithms #big-o-notation #flatiron-school #algorithms