CODEWITHSUNDEEP
pythonpython-3.xdata-structuresdynamic-programming
silenok sweet
silenok sweet

Reputation: 51

Is there any true general pattern to solve any dynamic programming problems?

I understand there is a general pattern to solve dynamic programming problems. Consists of:

  1. Identify Overlapping Subproblem by Breaking down the problem into smaller subproblems that are solved independently.

  2. Memoization by Creating a data structure to store the solutions to subproblems usually hash map

  3. Initialize Base Cases to Identify the base cases or trivial subproblems and initialize their solutions

  4. Fill in the Memoization Table by Iterate through the subproblems

  5. Finally, Return the Final Solution.

Once the memoization table is filled, the solution to the problem can often be found in the table's last entry or in a specific entry that represents the desired outcome.

However, it is applicable to simpler problems only such as the fibonacci problem in the below code.

def fibonacci(n, memo={}):
    if n in memo:
        return memo[n]
    if n <= 1:
        return n
    
    result = fibonacci(n - 1, memo) + fibonacci(n - 2, memo)
    memo[n] = result
    return result

# Example usage
n = 10
print(f"The {n}-th Fibonacci number is: {fibonacci(n)}")

Do we required to train ourself solving a lot of Dynamic programming problems to see the pattern that applicable to solve any Dynamic programming problem at any scale, any complexities? or there is a secret formula which can be replicated?

I try to find general pattern that replicable to solve any dynamic programming problem in Python

Upvotes: 5

Views: 349

Answers (1)

vhd
vhd

Reputation: 17

no. in general you should try to model the problem so that you can make the dynamic programming array from it. if you could do it (memoized or bottom up) then you have it.no. in general you should try to model the problem so that you can make the dynamic programming array from it. if you could do it (memoized or bottom up) then you have it.

Upvotes: 0

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