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What is memoization in Dynamic Programming?
  • a)
    A technique to store and reuse previously computed results
  • b)
    A technique to reduce the space complexity of the algorithm
  • c)
    A technique to solve problems using a bottom-up approach
  • d)
    A technique to optimize recursive algorithms
Correct answer is option 'A'. Can you explain this answer?
Most Upvoted Answer
What is memoization in Dynamic Programming?a)A technique to store and ...
Memoization is a technique used in Dynamic Programming to store the results of expensive function calls and reuse them when the same inputs occur again. It helps avoid redundant calculations and improves the overall performance of the algorithm.
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What is memoization in Dynamic Programming?a)A technique to store and ...


What is memoization in Dynamic Programming?




Memoization is a technique used in dynamic programming to store and reuse previously computed results. It involves storing the results of expensive function calls and returning the cached result when the same inputs occur again. This helps in reducing the time complexity of the algorithm by avoiding redundant computations.

Key Points:
- Memoization is particularly useful in recursive algorithms where the same subproblems are solved multiple times.
- By storing the results of subproblems in a data structure like a dictionary or an array, we can retrieve them in constant time when needed again.
- This technique helps in improving the efficiency of algorithms and reducing the overall time complexity.
- Memoization is commonly used in problems like Fibonacci series, factorial calculation, and finding optimal solutions in dynamic programming.

By implementing memoization, we can optimize the performance of recursive algorithms and make them more efficient. This technique is a key concept in dynamic programming and is widely used in various problem-solving scenarios to enhance the efficiency of algorithms.
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What is memoization in Dynamic Programming?a)A technique to store and reuse previously computed resultsb)A technique to reduce the space complexity of the algorithmc)A technique to solve problems using a bottom-up approachd)A technique to optimize recursive algorithmsCorrect answer is option 'A'. Can you explain this answer?
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