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What are the Dynamic arrays
Dynamic arrays are convenient for programmers since they can never be too small-whenever more space is needed in a dynamic array, it can simply be expanded. One drawback of dynamic arrays is that implementing language support for them is more work for compiler or interpreter writer. A potentially more serious drawback is that expansion procedure is expensive, so there are circumstances when using a dynamic array can be dangerous. For instance, if an application should respond in real time to events in its environment, and a dynamic array should be expanded when application is in the midst of a response, then the response may be delayed too long, causing problems.
Write down the algorithm of quick sort. An algorithm for quick sort: void quicksort ( int a[ ], int lower, int upper ) { int i ; if ( upper > lower ) { i = split ( a,
create aset of ten numbers.then you must divide it into two sets numbers which are set of odd numbers and set of even numbers.
Polynomials like 5x 4 + 2x 3 + 7x 2 + 10x - 8 can be represented by using arrays. Arithmetic operations such as addition & multiplication of polynomials are com
(1) (i) Add the special form let to the metacircular interpreter Hint: remember let is just syntactic sugar for a lambda expression and so a rewrite to the lambda form is all t
Data type An implementation of an abstract data type on a computer. Therefore, for instance, Boolean ADT is implemented as the Boolean type in Java, and bool type in C++;
Q. A Binary tree comprises 9 nodes. The preorder and inorder traversals of the tree yield the given sequence of nodes: Inorder : E A C K F H D
Example of Back Face Detection Method To illustrate the method, we shall start with the tetrahedron (pyramid) PQRS of Figure with vertices P (1, 1, 2), Q (3, 2, 3), R (1,
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
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What do you mean by complexity of an algorithm? The complexity of an algorithm M is the function f(n) which gives the running time and/or storage space need of the algorithm i
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