Memoized Dynamic Programming algorithm

In summary, the memoized dynamic programming algorithm checks to see if the value of $n$ is already in the memoized dynamic programming algorithm, in case it is faster to just use the value from the memoized dynamic programming algorithm rather than trying to look it up from the original input.
  • #1
evinda
Gold Member
MHB
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Hello! (Wave)

I am looking at the following example of Memoized Dynamic Programming algorithm.

Code:
memo={}
Fib(n):
  if n in memo return memo[n]
  if n<=2 F[n]=1
  else F[n]=F[n-1]+F[n-2]
  memo[n]=F[n]
  return F[n]

Could you explain me why we check if $n$ is in memo although memo is a set that contains the values of $F[n]$ and not of $n$? (Thinking)
 
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  • #2
memo is not a set. It would not be very useful to have a set that contains various Fibonacci numbers, since there would be no way to look up a Fibonacci number by its position in the Fibonacci sequence (remember sets have no notion of order; if I give you a set that contains the first 100 Fibonacci numbers and then ask you to give me the 71st Fibonacci number, the set doesn't help a whole lot).

Here memo is an associative array (also known as a map or a dictionary) which associates any integer $n$ to the corresponding Fibonacci number $F_n$, if it has been computed so far (if not, then $n$ is not inside the array and memo[n] is undefined).
 
Last edited:
  • #3
Bacterius said:
memo is not a set. It would not be very useful to have a set that contains various Fibonacci numbers, since there would be no way to look up a Fibonacci number by its position in the Fibonacci sequence (remember sets have no notion of order; if I give you a set that contains the first 100 Fibonacci numbers and then ask you to give me the 71st Fibonacci number, the set doesn't help a whole lot).

Here memo is an associative array (also known as a map or a dictionary) which associates any integer $n$ to the corresponding Fibonacci number $F_n$, if it has been computed so far (if not, then $n$ is not inside the array and memo[n] is undefined).

I see... Thank you! (Smirk)
 

Related to Memoized Dynamic Programming algorithm

What is a Memoized Dynamic Programming algorithm?

A Memoized Dynamic Programming algorithm is a problem-solving technique that uses a combination of memoization and dynamic programming to optimize the performance of a recursive algorithm. It stores the results of subproblems in a data structure, allowing for faster retrieval and avoiding unnecessary recomputation.

How does a Memoized Dynamic Programming algorithm work?

A Memoized Dynamic Programming algorithm works by breaking down a complex problem into smaller subproblems, solving each subproblem only once, and storing the results in a data structure. This allows for faster retrieval of results when solving larger subproblems, as the algorithm can look up the results in the data structure instead of recomputing them.

What is the difference between memoization and dynamic programming?

Memoization and dynamic programming are both techniques used to optimize the performance of recursive algorithms. The main difference is that memoization stores the results of subproblems in a data structure, while dynamic programming uses a bottom-up approach to solve smaller subproblems before solving larger ones. Memoization is more suitable for problems with overlapping subproblems, while dynamic programming is better for problems with optimal substructure.

What types of problems are best solved using a Memoized Dynamic Programming algorithm?

Memoized Dynamic Programming algorithms are best suited for problems with overlapping subproblems, meaning that the same subproblems are being solved multiple times. Examples of such problems include the Fibonacci sequence, the longest common subsequence, and the knapsack problem.

What are the benefits of using a Memoized Dynamic Programming algorithm?

The main benefit of using a Memoized Dynamic Programming algorithm is that it can drastically improve the performance of a recursive algorithm by avoiding unnecessary recomputation. This can lead to faster execution times and more efficient use of resources. Additionally, the use of a data structure to store results can make the algorithm easier to understand and debug.

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