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Complexity: How do the resource needs of a program or algorithm scale (the growth of resource requirements as a function of input). In other words, what happens with the performance of an algorithm, as the size of the difficulty being solved gets larger & larger? For instance, the time & memory requirement of an algorithm that computes the sum of 1000 numbers is larger than the algorithm that computes the sum of 2 numbers.
Time Complexity: The maximum time needed through a Turing machine to execute on any input of length n.
Space Complexity: The amount of storage space needed by an algorithm varies along the size of the problem being solved out. Normally the space complexity is expressed as an order of magnitude of the size of the problem, for example (n2) means that if the size of the problem (n) doubles then the working storage (memory) needs will become four times.
HEAP A heap is described to be a binary tree with a key in every node, such that 1-All the leaves of the tree are on 2 adjacent levels. 2- All leaves on the lowest leve
Two broad classes of collision resolution techniques are A) open addressing and B) chaining
I want to example for midsquare method
Explain an efficient and effective way of storing two symmetric matrices of the same order in the memory. A n-square matrix array will be symmetric if a[j][k]=a[k][j] for all j
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Illustrates the program segment for Quick sort. It uses recursion. Program 1: Quick Sort Quicksort(A,m,n) int A[ ],m,n { int i, j, k; if m { i=m; j=n+1; k
1. Give both a high-level algorithm and an implementation (\bubble diagram") of a Turing machine for the language in Exercise 3.8 (b) on page 160. Use the ' notation to show the co
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