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## Data Structure Questions and Answers-Fibonacci using Dynamic Programming

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Question 1 |

The following sequence is a fibonacci sequence:

0, 1, 1, 2, 3, 5, 8, 13, 21, .....

Which technique can be used to get the nth fibonacci term?

Recursion | |

Dynamic programming | |

A single for loop | |

All of the mentioned |

**Visual arts Questions answers**

Question 1 Explanation:

Each of the above mentioned methods can be used to find the nth fibonacci term.

Question 2 |

Consider the recursive implementation to find the nth fibonacci number:

int fibo(int n) if n <= 1 return n return .....

Which line would make the implementation complete?

fibo(n) + fibo(n) | |

fibo(n) + fibo(n - 1) | |

fibo(n - 1) + fibo(n + 1) | |

fibo(n - 1) + fibo(n - 2) |

**KBC Questions answers**

Question 2 Explanation:

Consider the first five terms of the fibonacci sequence: 0, 1, 1, 2, 3. The 6th term can be found by adding the two previous terms, i.e. fibo(6) = fibo(5) + fibo(4) = 3 + 2 = 5. Therefore, the nth term of a fibonacci sequence would be given by:

fibo(n) = fibo(n-1) + fibo(n-2).

Question 3 |

What is the time complexity of the recursive implementation used to find the nth fibonacci term?

O(1) | |

O(n ^{2}) | |

O(n!) | |

Exponential |

**GK Questions answers**

Question 3 Explanation:

The recurrence relation is given by fibo(n) = fibo(n - 1) + fibo(n - 2). So, the time complexity is given by:

T(n) = T(n - 1) + T(n - 2)

Approximately,

T(n) = 2 * T(n - 1)

= 4 * T(n - 2)

= 8 * T(n - 3)

:

:

:

= 2^{k} * T(n - k)

This recurrence will stop when n - k = 0

i.e. n = k

Therefore, T(n) = 2^{n} * O(0) = 2^{n}

Hence, it takes exponential time.

It can also be proved by drawing the recursion tree and counting the number of leaves.

Question 4 |

Suppose we find the 8th term using the recursive implementation. The arguments passed to the function calls will be as follows:

fibonacci(8) fibonacci(7) + fibonacci(6) fibonacci(6) + fibonacci(5) + fibonacci(5) + fibonacci(4) fibonacci(5) + fibonacci(4) + fibonacci(4) + fibonacci(3) + fibonacci(4) + fibonacci(3) + fibonacci(3) + fibonacci(2) : : :

Which property is shown by the above function calls?

Memoization | |

Optimal substructure | |

Overlapping subproblems | |

Greedy |

**HRM Questions answers**

Question 4 Explanation:

From the function calls, we can see that fibonacci(4) is calculated twice and fibonacci(3) is calculated thrice. Thus, the same subproblem is solved many times and hence the function calls show the overlapping subproblems property.

Question 5 |

What is the output of the following program?

#include<stdio.h> int fibo(int n) { if(n<=1) return n; return fibo(n-1) + fibo(n-2); } int main() { int r = fibo(50000); printf("%d", r); return 0; }

1253556389 | |

5635632456 | |

Garbage value | |

Runtime error |

**KBC Questions answers**

Question 5 Explanation:

The value of n is 50000. The function is recursive and it's time complexity is exponential. So, the function will be called almost 2

^{50000}times. Now, even though NO variables are stored by the function, the space required to store the addresses of these function calls will be enormous. Stack memory is utilized to store these addresses and only a particular amount of stack memory can be used by any program. So, after a certain function call, no more stack space will be available and it will lead to stack overflow causing runtime error.

There are 5 questions to complete.