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We use dynamic programming approach when
- a)We need an optimal solution
- b)The solution has optimal substructure
- c)The given problem can be reduced to the 3-SAT problem
- d)It's faster than Greedy
Correct answer is option 'B'. Can you explain this answer?
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We use dynamic programming approach whena)We need an optimal solutionb...
Option (D) is incorrect because Greedy algorithms are generally faster than Dynamic programming.
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We use dynamic programming approach whena)We need an optimal solutionb...
Dynamic Programming approach:
Dynamic programming is a technique for solving complex problems by breaking them down into simpler subproblems and solving each subproblem only once.
Optimal Substructure:
- Dynamic programming approach is used when the solution of a problem can be constructed by combining the solutions of its subproblems.
- This property is called Optimal Substructure.
- Optimal substructure is a property of a problem that allows a solution to be constructed from solutions of its subproblems.
Why do we need an optimal solution?
- In some problems, we need to find the best possible solution.
- In such cases, we need an optimal solution.
Why do we use dynamic programming approach?
- Dynamic programming approach is used to solve problems that have optimal substructure.
- If the problem can be divided into smaller subproblems that can be solved independently, then dynamic programming can be used to solve the problem.
- Dynamic programming is used when we want to find the optimal solution to a problem.
Why is dynamic programming faster than greedy?
- Greedy algorithms are often fast and easy to implement, but they do not always provide the optimal solution.
- Dynamic programming, on the other hand, guarantees to find the optimal solution.
- Dynamic programming is faster than greedy when the problem has overlapping subproblems.
Conclusion:
- Dynamic programming approach is used when the problem has optimal substructure.
- Dynamic programming guarantees to find the optimal solution.
- Dynamic programming is faster than greedy when the problem has overlapping subproblems.
Dynamic programming is a technique for solving complex problems by breaking them down into simpler subproblems and solving each subproblem only once.
Optimal Substructure:
- Dynamic programming approach is used when the solution of a problem can be constructed by combining the solutions of its subproblems.
- This property is called Optimal Substructure.
- Optimal substructure is a property of a problem that allows a solution to be constructed from solutions of its subproblems.
Why do we need an optimal solution?
- In some problems, we need to find the best possible solution.
- In such cases, we need an optimal solution.
Why do we use dynamic programming approach?
- Dynamic programming approach is used to solve problems that have optimal substructure.
- If the problem can be divided into smaller subproblems that can be solved independently, then dynamic programming can be used to solve the problem.
- Dynamic programming is used when we want to find the optimal solution to a problem.
Why is dynamic programming faster than greedy?
- Greedy algorithms are often fast and easy to implement, but they do not always provide the optimal solution.
- Dynamic programming, on the other hand, guarantees to find the optimal solution.
- Dynamic programming is faster than greedy when the problem has overlapping subproblems.
Conclusion:
- Dynamic programming approach is used when the problem has optimal substructure.
- Dynamic programming guarantees to find the optimal solution.
- Dynamic programming is faster than greedy when the problem has overlapping subproblems.
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We use dynamic programming approach whena)We need an optimal solutionb)The solution has optimal substructurec)The given problem can be reduced to the 3-SAT problemd)Its faster than GreedyCorrect answer is option 'B'. Can you explain this answer?
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