That means, roughly speaking, that for any input of size "x", the algorithm will take no longer than xn for some constant "n".
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Performance measurement is concerned with obtaining the space and time requirement of a particular algorithm thus quantities depend on the and absence used as well as on computer on which the algorithm is run..........
Dijkstra's original algorithm (published in 1959) has a time-complexity of O(N*N), where N is the number of nodes.
The algorithm will have both a constant time complexity and a constant space complexity: O(1)
o(nm)
Finding a time complexity for an algorithm is better than measuring the actual running time for a few reasons: # Time complexity is unaffected by outside factors; running time is determined as much by other running processes as by algorithm efficiency. # Time complexity describes how an algorithm will scale; running time can only describe how one particular set of inputs will cause the algorithm to perform. Note that there are downsides to time complexity measurements: # Users/clients do not care about how efficient your algorithm is, only how fast it seems to run. # Time complexity is ambiguous; two different O(n2) sort algorithms can have vastly different run times for the same data. # Time complexity ignores any constant-time parts of an algorithm. A O(n) algorithm could, in theory, have a constant ten second section, which isn't normally shown in big-o notation.