Explain what it means for an algorithm to exhibit optimal substructure and contain overlapping subproblems. Show that your Dynamic Programming algorithm exhibits both properties, and is therefore much faster than your recursion-based algorithm.

A. Construct a RECURSIVE solution for following iterative solution to find a value in a circular-linked list B. Analyze the runtime complexity (correctly explain the scenario, show how much work would be done, and represent the work using asymptotic notations, i.e. big O),  of the given iterative solution and  your recursive solution for the best case scenario and the worse case scenario. C. prove the correctness of your recursive solution by induction /**@param value - a value to search for@return true if the value is in the list and set the current reference to itotherwise return false and not updating the current reference*/public boolean find(T value){  if(this.cur == null)    return false; //get out, nothing is in here    Node<T> tmp = this.cur; //start at the current position  if(tmp.data == value)    return true; // found it at the starting location   tmp = tmp.next; //adv. to next node, if there is one  while(tmp != cur) {  if(tmp.data == value){    this.cur = tmp;…

Explain the halting condition for the recursive binary search and why all recursive algorithms need one.

Give an account of the stopping condition for the recursive binary search, and elaborate on why such a condition is required of all recursive algorithms.