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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.
N = number of rows of the graph D[i[j] = C[i][j] For k from 1 to n Do for i = 1 to n Do for j = 1 to n D[i[j]= minimum( d ij (k-1) ,d ik (k-1) +d kj (k-1)
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