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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.
multilist representation of graph
Elaborate the symbols of abstract data type length(a)-returns the number of characters in symbol a. capitalize(a)-returns the symbol generated from a by making its first cha
Definition: A set of data values & related operations that are accurately specified independent of any particular implementation. As the data values and operations are described
Explain an efficient way of storing a sparse matrix in memory. A matrix in which number of zero entries are much higher than the number of non zero entries is called sparse mat
Add -75to+25 in 2s complement
The objective analysis of an algorithm is to determine its efficiency. Efficiency is based on the resources which are used by the algorithm. For instance, CPU utilization (Ti
Since the stack is list of elements, the queue is also a list of elements. The stack & the queue differ just in the position where the elements may be added or deleted. Similar to
Q. The degree of a node is defined as the number of children it has. Shear show that in any binary tree, the total number of leaves is one more than the number of nodes of degree 2
representation of linear array
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