There's more than one algorithms, the best one is the following:
F(n) = 1/sqrt(5) * (q1^n + q2^n)
where q1=(1+sqrt(5))/2, q2=(1-sqrt(5))/2
The algorithm will have both a constant time complexity and a constant space complexity: O(1)
time complexity is 2^57..and space complexity is 2^(n+1).
Time complexity and space complexity.
"Running Time" is essentially a synonym of "Time Complexity", although the latter is the more technical term. "Running Time" is confusing, since it sounds like it could mean "the time something takes to run", whereas Time Complexity unambiguously refers to the relationship between the time and the size of the input.
Complexity is a measure of how long an algorithm is expected to take and/or how much space is required to complete the task. It is not specific to C++ -- the language is immaterial -- it only applies to algorithms. Complexity is often expressed in big O notation, where O(1) is constant time (the best that can be expected of any algorithm).
The space complexity of the Dijkstra algorithm is O(V), where V is the number of vertices in the graph.
The algorithm will have both a constant time complexity and a constant space complexity: O(1)
time complexity is 2^57..and space complexity is 2^(n+1).
The space complexity of the quicksort algorithm is O(log n) in the best and average cases, and O(n) in the worst case.
Time complexity and space complexity.
The complexity of the algorithm refers to how much time and space it needs to solve a problem. When dealing with a problem that has an exponential space requirement, the algorithm's complexity will also be exponential, meaning it will take a lot of time and memory to solve the problem.
The space complexity of the breadth-first search algorithm is O(V), where V is the number of vertices in the graph being traversed.
The space complexity of the quick sort algorithm is O(log n) in the best and average cases, and O(n) in the worst case.
The space complexity of the Quick Sort algorithm is O(log n) in the best and average cases, and O(n) in the worst case.
The constant extra space complexity of an algorithm refers to the amount of additional memory it requires to run, regardless of the input size. It is a measure of how much extra space the algorithm needs beyond the input data.
The space complexity of the Breadth-First Search (BFS) algorithm is O(V), where V is the number of vertices in the graph being traversed.
The space complexity of the Breadth-First Search (BFS) algorithm is O(V), where V is the number of vertices in the graph being traversed.