The algorithm will have both a constant time complexity and a constant space complexity: O(1)
The simplest and slowest searching method; the only possible method when the data is unsorted and/or only sequential access is possible (eq. processing a tape file). I think he's looking for time complexity which I believe is just n
time complexity is 2^57..and space complexity is 2^(n+1).
Dijkstra's original algorithm (published in 1959) has a time-complexity of O(N*N), where N is the number of nodes.
Time complexity and space complexity.
The algorithm will have both a constant time complexity and a constant space complexity: O(1)
The simplest and slowest searching method; the only possible method when the data is unsorted and/or only sequential access is possible (eq. processing a tape file). I think he's looking for time complexity which I believe is just n
time complexity is 2^57..and space complexity is 2^(n+1).
If you're strictly using a sequential search, then the order of the array's content will make no difference. Whether it's in low-high order, high-low order, or randomized, the time complexity for a sequential search will remain O(n).
Dijkstra's original algorithm (published in 1959) has a time-complexity of O(N*N), where N is the number of nodes.
Time complexity and space complexity.
o(nm)
Time complexity is a function which value depend on the input and algorithm of a program and give us idea about how long it would take to execute the program
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.
O 2^(n)
The usual definition of an algorithm's time complexity is called Big O Notation. If an algorithm has a value of O(1), it is a fixed time algorithm, the best possible type of algorithm for speed. As you approach O(∞) (a.k.a. infinite loop), the algorithm takes progressively longer to complete (an algorithm of O(∞) would never complete).
Time complexity and space complexity. More specifically, how well an algorithm will scale when given larger inputs.