What is the Difference Between Divide and Conquer and Dynamic Programming

The main difference between divide and conquer and dynamic programming is that the divide and conquer combines the solutions of the sub-problems to obtain the solution of the main problem while dynamic programming uses the result of the sub-problems to find the optimum solution of the main problem.

1. Is dynamic programming more efficient than divide and conquer?
2. What are the advantages of dynamic programming method over Divide and conquer method?
3. What is the difference between Memoization and dynamic programming?
4. What is dynamic programming example?
5. Is Fibonacci dynamic programming?
6. What is optimal substructure in dynamic programming?
7. What are the disadvantages of dynamic programming?
8. What types of problems are solved using the dynamic programing strategy?
9. What is greedy method in algorithm?
10. What is the concept of dynamic programming?
11. Why is it called dynamic programming?
12. Is Dynamic Programming bottom-up or top-down?

Is dynamic programming more efficient than divide and conquer?

Divide-&-conquer works best when all subproblems are independent. So, pick partition that makes algorithm most efficient & simply combine solutions to solve entire problem. Dynamic programming is needed when subproblems are dependent; we don't know where to partition the problem.

What are the advantages of dynamic programming method over Divide and conquer method?

Combine the solutions to the sub problems into the solution for the original problem.

• They call themselves recursively one or more times to deal with closely related sub problems.
• D&C does more work on the sub-problems and hence has more time consumption.
• In D&C the sub problems are independent of each other.

What is the difference between Memoization and dynamic programming?

Both Memoization and Dynamic Programming solves individual subproblem only once. Memoization uses recursion and works top-down, whereas Dynamic programming moves in opposite direction solving the problem bottom-up.

What is dynamic programming example?

Dynamic Programming is mainly an optimization over plain recursion. ... For example, if we write simple recursive solution for Fibonacci Numbers, we get exponential time complexity and if we optimize it by storing solutions of subproblems, time complexity reduces to linear.

Is Fibonacci dynamic programming?

What is Dynamic Programming: Dynamic programming is a technique to solve the recursive problems in more efficient manner. In dynamic programming we store the solution of these sub-problems so that we do not have to solve them again, this is called Memoization. ...

What is optimal substructure in dynamic programming?

In computer science, a problem is said to have optimal substructure if an optimal solution can be constructed from optimal solutions of its subproblems. This property is used to determine the usefulness of dynamic programming and greedy algorithms for a problem. ... This is an example of optimal substructure.

What are the disadvantages of dynamic programming?

Disadvantages of Dynamic Programming over recursion

Many times, output value gets stored and never gets utilized in the next subproblems while execution. It leads to unnecessary memory utilization. In DP, functions are called recursively. Stack memory keeps increasing.

What types of problems are solved using the dynamic programing strategy?

Two main properties of a problem suggest that the given problem can be solved using Dynamic Programming. These properties are overlapping sub-problems and optimal substructure.

What is greedy method in algorithm?

Greedy is an algorithmic paradigm that builds up a solution piece by piece, always choosing the next piece that offers the most obvious and immediate benefit. So the problems where choosing locally optimal also leads to global solution are best fit for Greedy. For example consider the Fractional Knapsack Problem.

What is the concept of dynamic programming?

Dynamic Programming (DP) is an algorithmic technique for solving an optimization problem by breaking it down into simpler subproblems and utilizing the fact that the optimal solution to the overall problem depends upon the optimal solution to its subproblems.

Why is it called dynamic programming?

The word dynamic was chosen by Bellman to capture the time-varying aspect of the problems, and because it sounded impressive. The word programming referred to the use of the method to find an optimal program, in the sense of a military schedule for training or logistics.

Is Dynamic Programming bottom-up or top-down?

Dynamic programming problems can be solved using either bottom-up or top-down approaches. Generally, the bottom-up approach uses the tabulation technique, while the top-down approach uses the recursion (with memorization) technique.