Search results
The Big-O notation: the running time of an algorithm as a function of the size of its input. worst case estimate. asymptotic behavior. O(n2) means that the running time of the algorithm on an input of size n is limited by the quadratic function of n.
Designing better algorithms. Analyzing the asymptotic running time of algorithms is a useful way of thinking about algorithms that often leads to nonobvious improvements. Understanding. An analysis can tell us what parts of an algorithm are crucial for what kinds of inputs, and why.
Finally, the e ciency or performance of an algorithm relates to the resources required by it, such as how quickly it will run, or how much computer memory it will use. This will 6
For simplicity, we compute the running time of an algorithm as a function of the length of the string that represents the input. In worst-case analysis, we consider the longest running time of all inputs of a particular length (that is all we care about in this class)
4 Lecture 13: Dijkstra’s Algorithm. Running Time • Count operations on changeable priority queue Q, assuming it contains n items: Operation Time
Time complexity. Use of time complexity makes it easy to estimate the running time of a program. Performing an accurate calculation of a program’s operation time is a very labour-intensive process (it depends on the compiler and the type of computer or speed of the processor).
Outline of this Lecture. Spanning trees and minimum spanning trees. The minimum spanning tree (MST) problem. The generic algorithm for MST problem. Prim’s algorithm for the MST problem. The algorithm. Correctness. Implementation + Running Time. Spanning Trees.
