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Fundamental Data Structures in Java

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Algorithmic Complexity

In the Collection framework, there are many different data structures, and each of them has its algorithmic complexity.

Algorithmic complexity is denoted using big O notation (e.g., O(n), O(n^2)), where "O" stands for "big O" and indicates an upper bound on the growth of the running time as a function of the input size.

Here are the main types of algorithmic complexity:

  • O(1) (constant time): time complexity does not depend on the size of the input data. For example, accessing an element in an array by index;

  • O(log n) (logarithmic time): time complexity grows logarithmically with the size of the input data. Example: binary search in a sorted array;

  • O(n) (linear time): time complexity grows linearly with the size of the input data. Example: iterating through all elements in an ArrayList;

  • O(n^2) (quadratic time): time complexity is proportional to the square of the size of the input data. Example: bubble sort.

These are basic categories, and there are many other types of algorithmic complexity, such as O(n log n), O(2^n), O(n!), and others, characterizing more complex algorithms. Choosing an efficient algorithm, considering its complexity, is a crucial aspect of software development.

question mark

Which statements accurately describe the meaning of common big O notations in algorithmic complexity

Select all correct answers

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Hur kan vi förbättra det?

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Avsnitt 1. Kapitel 5

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Algorithmic Complexity

In the Collection framework, there are many different data structures, and each of them has its algorithmic complexity.

Algorithmic complexity is denoted using big O notation (e.g., O(n), O(n^2)), where "O" stands for "big O" and indicates an upper bound on the growth of the running time as a function of the input size.

Here are the main types of algorithmic complexity:

  • O(1) (constant time): time complexity does not depend on the size of the input data. For example, accessing an element in an array by index;

  • O(log n) (logarithmic time): time complexity grows logarithmically with the size of the input data. Example: binary search in a sorted array;

  • O(n) (linear time): time complexity grows linearly with the size of the input data. Example: iterating through all elements in an ArrayList;

  • O(n^2) (quadratic time): time complexity is proportional to the square of the size of the input data. Example: bubble sort.

These are basic categories, and there are many other types of algorithmic complexity, such as O(n log n), O(2^n), O(n!), and others, characterizing more complex algorithms. Choosing an efficient algorithm, considering its complexity, is a crucial aspect of software development.

question mark

Which statements accurately describe the meaning of common big O notations in algorithmic complexity

Select all correct answers

Var allt tydligt?

Hur kan vi förbättra det?

Tack för dina kommentarer!

Avsnitt 1. Kapitel 5
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