Table of Contents
- 1 Where Dynamic Programming is used in real life?
- 2 Can Dynamic Programming solve all problems?
- 3 What types of problems are solved using the dynamic programing strategy?
- 4 Which of the following problems should be solved by dynamic programming?
- 5 Which of the following problems is solved by using branch and bound method?
Where Dynamic Programming is used in real life?
Dynamic programming is heavily used in computer networks, routing, graph problems, computer vision, artificial intelligence, machine learning etc.
Can Dynamic Programming solve all problems?
Typically, all the problems that require maximizing or minimize certain quantities or counting problems that say to count the arrangements under certain conditions or certain probability problems can be solved by using Dynamic Programming.
Which application can be solved by using dynamic programming?
Dynamic programming is used to solve the multistage optimization problem in which dynamic means reference to time and programming means planning or tabulation.
What types of problems are solved using the dynamic programing strategy?
Dynamic programming solves optimization problems by combining solutions to subproblems. This sounds familiar: divide and conquer also combines solutions to subproblems, but applies when the subproblems are disjoint. For example, here is the recursion tree for merge sort on an array A[1.. 8].
Which of the following problems should be solved by dynamic programming?
Explanation: the longest common subsequence problem has both, optimal substructure and overlapping subproblems. hence, dynamic programming should be used the solve this problem.
Which of the following problems Cannot be solved using recursion?
2. Which of the following problems can’t be solved using recursion? Explanation: Problems without base case leads to infinite recursion call. In general, we will assume a base case to avoid infinite recursion call.
Which of the following problems is solved by using branch and bound method?
Branch and bound is an algorithm design paradigm which is generally used for solving combinatorial optimization problems. Greedy Algorithm for Fractional Knapsack. DP solution for 0/1 Knapsack. Backtracking Solution for 0/1 Knapsack.