Complexity of an algorithm is the study of how long a program will take to run, depending on the size of its input & long of loops made inside the code
Specifically, the complexity of an algorithm is a measure of how long it takes to complete (give an answer) relative to increasing sizes of input data. Thus, complexity is not concerned with how long it took the algorithm to run using X amount of data. Rather, it is concerned with the relationship in runtime when using X amount of data, 2X amounts of data, 10X amounts of data, etc. While complexity usually refers to execution time, it can also be applied to other resource usage (for example, memory allocation). In all cases, complexity is concerned with the relationship between the rate of increase in resource consumption and the rate of increase of the size of the data set being worked on.
Complexity is closely related to the concepts of scalingand efficiency, but is NOT an exact equivalence to either.
The algorithm will have both a constant time complexity and a constant space complexity: O(1)
Time complexity and space complexity.
Dijkstra's original algorithm (published in 1959) has a time-complexity of O(N*N), where N is the number of nodes.
time complexity is 2^57..and space complexity is 2^(n+1).
o(nm)
The time complexity of the algorithm is superpolynomial.
The memory complexity of an algorithm refers to the amount of memory it requires to run. It is important to consider the memory complexity when evaluating the efficiency of an algorithm.
The time complexity of the algorithm is O(log n).
The algorithm will have both a constant time complexity and a constant space complexity: O(1)
The runtime complexity of the Union Find algorithm is O(log n) on average.
The space complexity of the Dijkstra algorithm is O(V), where V is the number of vertices in the graph.
The time complexity of an algorithm with a running time of nlogn is O(nlogn).
The time complexity of the Strassen algorithm for matrix multiplication is O(n2.81).
The time complexity of an algorithm with a factorial time complexity of O(n!) is O(n!).
Complexity of an algorithm is a measure of how long an algorithm would take to complete given
The time complexity of the backtrack algorithm is typically exponential, O(2n), where n is the size of the problem.
The time complexity of the backtracking algorithm is typically exponential, O(2n), where n is the size of the problem.