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Generally, Computational complexity of algorithms are referred to through space complexity (space needed for running program) and time complexity (time needed for running the program). In computer of science, the concept of runtime complexity has been studied vigorously. Sufficient research is being carried out to determine more efficient algorithms for present problems. We studied several asymptotic notations, to define the time complexity and space complexity of algorithms, say the big-O, Omega & Theta notations. These asymptotic orders of time & space complexity define how best or worst an algorithm is for an adequately large input.
We studied regarding the process of calculation of runtime complexity of several algorithms. The exact analysis of insertion sort was discussed to define the best case, worst case & average case scenario.
RGB Model The RGB model is based on the assumption that any desired shade of colour can be obtained by mixing the correct amounts of red, green, and blue light. The exact hues
Merging two sequence using CREW merge
What will be depth do , of complete binary tree of n nodes, where nodes are labelled from 1 to n with root as node and last leaf node as node n
Give the example of bubble sort algorithm For example List: - 7 4 5 3 1. 7 and 4 are compared 2. Since 4 3. The content of 7 is now stored in the variable which was h
Define Big Omega notation Big Omega notation (?) : The lower bound for the function 'f' is given by the big omega notation (?). Considering 'g' to be a function from the non-n
/* the program accepts two polynomials as a input & prints the resultant polynomial because of the addition of input polynomials*/ #include void main() { int poly1[6][
Multidimensional array: Multidimensional arrays can be defined as "arrays of arrays". For example, a bidimensional array can be imagined as a bidimensional table made of elements,
Method to measure address of any element of a matrix stored in memory. Let us consider 2 dimensional array a of size m*n further consider that the lower bound for the row index
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
What are circular queues? Circular queue: Static queues have a very large drawback that once the queue is FULL, even though we erase few elements from the "front" and relieve
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