What would happen to the time complexity (Big-oh) of the methods in an array implementation of the stack if the top of the stack were at position 0? Explain.
What would happen to the time complexity (Big-oh) of the methods in an array implementation of...
What would happen to the time complexity (Big-oh) of the methods (push, pop) in an array implementation of the stack if the top of the stack were at position 0? Explain.
When using an array to implement a list ADT, what is the time complexity (Big-Oh) for finding an element in the list with N elements? (C++)
2. Consider a circular array based Queue as we have discussed in the lectures (class definition given below for reference) public class CircArrayQueue<E> implements Queue<E private EI Q private int front-0 indicates front of queue l indicates position after end of queue private int end-0: public CircArrayQueue( public int getSize (.. public boolean isEmpty ( public void enqueue (E e)... public E dequeue ) throws EmptyQueueException... Il constructor We are interested in implementing a Stack class based on the above...
1.If a list is implemented as a singly linked stack, give the big-O worst-case time complexity of the following operations (as usual use the smallest standard big-O category that works: a) push_front, b) push_back, c) lookup, d) read the i'th member 2.Repeat question 3 for a dynamic array (for example, as in the C++ vector class)
Please provide the big oh notation for running time and space complexity for the following methods (splay, findSplay, putSplay, eraseSplay: void SplayTreeMap::splay(Node* x) { if(x!=NULL){ while (x -> parent != NULL ) { Node *p = x -> parent; Node *g = p -> parent; if (g == NULL) zig(x); else if (g -> left == p && p -> left == x) zigZig(x); else if (g -> right == p && p -> right == x) zigZig(x); else...
Using C++ please explain What is the Big-O time complexity of the following code: for (int i=0; i<N; i+=2) { ... constant time operations... Select one: o a. O(n^2) O b. O(log n) c. O(n) O d. 0(1) What is the Big-O time complexity of the following code: for(int i=1; i<N; i*=2) { ... constant time operations... Select one: O O a. O(n^2) b. 0(1) c. O(n) d. O(log n) O What is the Big-O time complexity of the following...
In Java. What would the methods of this class look like? StackADT.java public interface StackADT<T> { /** Adds one element to the top of this stack. * @param element element to be pushed onto stack */ public void push (T element); /** Removes and returns the top element from this stack. * @return T element removed from the top of the stack */ public T pop(); /** Returns without removing the top element of this stack. * @return T...
1. [5 marks Show the following hold using the definition of Big Oh: a) 2 mark 1729 is O(1) b) 3 marks 2n2-4n -3 is O(n2) 2. [3 marks] Using the definition of Big-Oh, prove that 2n2(n 1) is not O(n2) 3. 6 marks Let f(n),g(n), h(n) be complexity functions. Using the definition of Big-Oh, prove the following two claims a) 3 marks Let k be a positive real constant and f(n) is O(g(n)), then k f(n) is O(g(n)) b)...
Review the Stack implementation with Vector, and implement/answer the following methods. Stack One of the principles of good programming is to reuse existing code whenever practical. If you can reuse existing code, you don't need to spend the time to rewrite it. Code used previously has also been debugged, and will likely contain fewer errors. One of the easiest ways to create a container is to leverage an existing data type to build a new abstraction. In this lesson we...
Show your work Count the number of operations and the big-O time complexity in the worst-case and best-case for the following code int small for ( i n t i = 0 ; i < n ; i ++) { i f ( a [ i ] < a [ 0 ] ) { small = a [ i ] ; } } Show Work Calculate the Big-O time complexity for the following code and explain your answer by showing...