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Complexity is the rate at which the needed storage or consumed time rise as a function of the problem size. The absolute growth based on the machine utilized to execute the program, the compiler utilized to construct the program, and several other factors. We would like to have a way of defining the inherent complexity of a program (or piece of a program), independent of machine/compiler considerations. It means that we have to not attempt to describe the absolute time or storage needed. We have to instead concentrate on a "proportionality" approach, expressing the complexity in terms of its relationship to some known function. This kind of analysis is known as asymptotic analysis. It might be noted that we are dealing with complexity of an algorithm not that of a problem. For instance, the simple problem could have high order of time complexity & vice-versa.
Assume a complete binary tree T with n nodes where each node has an item (value). Label the nodes of the complete binary tree T from top to bottom & from left to right 0, 1, ..., n
Q. Write down an algorithm to convert an infix expression into the postfix expression. Ans. Algo rithm to convert infix expression to post fix expression is given as
Q. The two Binary Trees are said to be similar if they are both empty or if they are both non- empty and left and right sub trees are similar. Write down an algorithm to determine
nested for loop for (i = 0; i for (j = 0; j sequence of statements } } Here, we observe that, the outer loop executes n times. Every time the outer loop execute
memory address of any element of lower left triangular sparse matrix
Q. Consider the specification written below of a graph G V(G ) = {1,2,3,4} E(G ) = {(1,2), (1,3), (3,3), (3,4), (4,1)} (i) Draw the undirected graph. (
Data array A has data series from 1,000,000 to 1 with step size 1, which is in perfect decreasing order. Data array B has data series from 1 to 1,000,000, which is in random order.
tree is graph or not
For preorder traversal, in the worst case, the stack will rise to size n/2, where n refer to number of nodes in the tree. Another method of traversing binary tree non-recursively t
Q. What is the smallest value of n such that an algorithm whose running time is 100n2 runs faster than an algorithm whose running time is 2n on the same machine. A n
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