Algorithmic Complexity
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 anArrayList; -
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.
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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 anArrayList; -
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.
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