Steps to solve a Dynamic Programming Problem

Last Updated : 23 Dec, 2024

Steps to solve a Dynamic programming problem:

Identify if it is a Dynamic programming problem.
Decide a state expression with the Least parameters.
Formulate state and transition relationship.
Apply tabulation or memorization.

Step 1: How to classify a problem as a Dynamic Programming Problem?

Typically, all the problems that require maximizing or minimizing certain quantities or counting problems that say to count the arrangements under certain conditions or certain probability problems can be solved by using Dynamic Programming.
All dynamic programming problems satisfy the overlapping subproblems property and most of the classic Dynamic programming problems also satisfy the optimal substructure property. Once we observe these properties in a given problem be sure that it can be solved using Dynamic Programming.

Step 2: Deciding the state

Dynamic Programming problems are all about the state and its transition. This is the most basic step which must be done very carefully because the state transition depends on the choice of state definition you make.

State:

A state can be defined as the set of parameters that can uniquely identify a certain position or standing in the given problem. This set of parameters should be as small as possible to reduce state space.

Example:
Let’s take the classic Knapsack problem, where we need to maximize profit by selecting items within a weight limit. Here, we define our state using two parameters: index and weight (dp[index][weight]). Think of it like this: dp[3][10] would tell us “what’s the maximum profit we can make by choosing from the first 4 items (index 0 to 3) when our bag can hold 10 units of weight?” These two parameters (index and weight) work together to uniquely identify each subproblem we need to solve.

Just like GPS coordinates need both latitude and longitude to pinpoint a location, our knapsack solution needs both the item range and remaining capacity to determine the optimal profit at each step.

So, our next step will be to find a relation between previous states to reach the current state.

Step 3: Formulating a relation among the states

This part is the hardest part of solving a Dynamic Programming problem and requires a lot of intuition, observation, and practice.

Example:

Given 3 numbers {1, 3, 5}, The task is to tell the total number of ways we can form a number n using the sum of the given three numbers. (allowing repetitions and different arrangements).

The total number of ways to form 6 is: 8
1 + 1 + 1 + 1 + 1 + 1
1 + 1 + 1 + 3
1 + 1 + 3 + 1
1 + 3 + 1 + 1
3 + 1 + 1 + 1
3 + 3
1 + 5
5 + 1

The steps to solve the given problem will be:

We decide a state for the given problem.
We will take a parameter n to decide the state as it uniquely identifies any subproblem.
DP state will look like state(n), state(n) means the total number of arrangements to form n by using {1, 3, 5} as elements. Derive a transition relation between any two states.
Now, we need to compute state(n).

How to Compute the state?

As we can only use 1, 3, or 5 to form a given number n. Let us assume that we know the result for n = 1, 2, 3, 4, 5, 6
Let us say we know the result for:
state (n = 1), state (n = 2), state (n = 3) ……… state (n = 6)
Now, we wish to know the result of the state (n = 7). See, we can only add 1, 3, and 5. Now we can get a sum total of 7 in the following 3 ways:

1) Adding 1 to all possible combinations of state (n = 6)
Eg : [(1 + 1 + 1 + 1 + 1 + 1) + 1]
[(1 + 1 + 1 + 3) + 1]
[(1 + 1 + 3 + 1) + 1]
[(1 + 3 + 1 + 1) + 1]
[(3 + 1 + 1 + 1) + 1]
[(3 + 3) + 1]
[(1 + 5) + 1]
[(5 + 1) + 1]

2) Adding 3 to all possible combinations of state (n = 4);
[(1 + 1 + 1 + 1) + 3]
[(1 + 3) + 3]
[(3 + 1) + 3]

3) Adding 5 to all possible combinations of state(n = 2)
[(1 + 1) + 5]

(Note how it sufficient to add only on the right-side – all the add-from-left-side cases are covered, either in the same state, or another, e.g. [1 + (1 + 1 + 1 + 3)] is not needed in state (n=6) because it’s covered by state (n = 4) [(1 + 1 + 1 + 1) + 3])

Now, think carefully and satisfy yourself that the above three cases are covering all possible ways to form a sum total of 7;
Therefore, we can say that result for
state(7) = state (6) + state (4) + state (2)
OR
state(7) = state (7-1) + state (7-3) + state (7-5)
In general,
state(n) = state(n-1) + state(n-3) + state(n-5)

Below is the implementation of the above approach:

C++

// C++ program to express // n as sum of 1, 3, 5. #include <bits/stdc++.h> using namespace std;  // Returns the number of  // arrangements to form 'n'  int countWays(int n) {          // base case    if (n < 0)        return 0;    if (n == 0)         return 1;       return countWays(n-1) + countWays(n-3) + countWays(n-5); }      int main() {     int n = 7;     cout << countWays(n); }

Java

// Java program to express // n as sum of 1, 3, 5.  class GfG {      // Returns the number of      // arrangements to form 'n'      static int countWays(int n) {                   // base case         if (n < 0)              return 0;         if (n == 0)               return 1;            return countWays(n - 1) + countWays(n - 3) + countWays(n - 5);     }          public static void main(String[] args) {         int n = 7;         System.out.println(countWays(n));     } }

Python

# Python program to express # n as sum of 1, 3, 5.  # Returns the number of  # arrangements to form 'n'  def countWays(n):          # base case     if n < 0:         return 0     if n == 0:         return 1      return countWays(n - 1) + countWays(n - 3) + countWays(n - 5)  if __name__ == "__main__":     n = 7     print(countWays(n))

// C# program to express // n as sum of 1, 3, 5.  using System;  class GfG {      // Returns the number of      // arrangements to form 'n'      static int countWays(int n) {                  // base case         if (n < 0)              return 0;         if (n == 0)               return 1;            return countWays(n - 1) + countWays(n - 3) + countWays(n - 5);     }          static void Main(string[] args) {         int n = 7;         Console.WriteLine(countWays(n));     } }

JavaScript

// JavaScript program to express // n as sum of 1, 3, 5.  // Returns the number of  // arrangements to form 'n'  function countWays(n) {      // base case     if (n < 0)         return 0;     if (n === 0)         return 1;      return countWays(n - 1) + countWays(n - 3) + countWays(n - 5); }  let n = 7; console.log(countWays(n));

Output

Time Complexity: O(3^n), As at every stage we need to take three decisions and the height of the tree will be of the order of n.
Auxiliary Space: O(n), The extra space is used due to the recursion call stack.

The above code seems exponential as it is calculating the same state again and again. So, we just need to add memoization.

Step 4: Adding memoization or tabulation for the state

This is the easiest part of a dynamic programming solution. We just need to store the state answer so that the next time that state is required, we can directly use it from our memory.

Using Top-Down DP (Memoization)

We break down the problem into smaller subproblems, where each subproblem corresponds to finding the number of ways to form a sum for a smaller value of ‘n’. By utilizing previously computed results, we can avoid redundant calculations and build up the solution for larger values of ‘n’.

C++

// C++ program to express // n as sum of 1, 3, 5. #include <bits/stdc++.h> using namespace std;  int countRecur(int n, vector<int> &memo) {          // base case     if (n < 0)        return 0;     if (n == 0)         return 1;              // If value is memoized     if (memo[n] != -1) {         return memo[n];     }          // Memoize the state      memo[n] = countRecur(n-1, memo) +      countRecur(n-3, memo) + countRecur(n-5, memo);            return memo[n]; }  // Returns the number of  // arrangements to form 'n'  int countWays(int n) {           vector<int> memo(n+1, -1);     return countRecur(n, memo); }      int main() {     int n = 7;     cout << countWays(n); }

Java

// Java program to express // n as sum of 1, 3, 5.  import java.util.Arrays;  class GfG {      static int countRecur(int n, int[] memo) {                  // base case         if (n < 0)              return 0;         if (n == 0)               return 1;                    // If value is memoized         if (memo[n] != -1) {             return memo[n];         }                  // Memoize the state          memo[n] = countRecur(n - 1, memo) +                    countRecur(n - 3, memo) +                    countRecur(n - 5, memo);              return memo[n];     }      // Returns the number of      // arrangements to form 'n'      static int countWays(int n) {         int[] memo = new int[n + 1];         Arrays.fill(memo, -1);         return countRecur(n, memo);     }      public static void main(String[] args) {         int n = 7;         System.out.println(countWays(n));     } }

Python

# Python program to express # n as sum of 1, 3, 5.  def countRecur(n, memo):          # base case     if n < 0:         return 0     if n == 0:         return 1      # If value is memoized     if memo[n] != -1:         return memo[n]      # Memoize the state      memo[n] = countRecur(n - 1, memo) + \               countRecur(n - 3, memo) + \               countRecur(n - 5, memo)          return memo[n]  # Returns the number of  # arrangements to form 'n'  def countWays(n):     memo = [-1] * (n + 1)     return countRecur(n, memo)  if __name__ == "__main__":     n = 7     print(countWays(n))

// C# program to express // n as sum of 1, 3, 5.  using System;  class GfG {      static int countRecur(int n, int[] memo) {                  // base case         if (n < 0)              return 0;         if (n == 0)               return 1;                    // If value is memoized         if (memo[n] != -1) {             return memo[n];         }                  // Memoize the state          memo[n] = countRecur(n - 1, memo) +                    countRecur(n - 3, memo) +                    countRecur(n - 5, memo);              return memo[n];     }      // Returns the number of      // arrangements to form 'n'      static int countWays(int n) {         int[] memo = new int[n + 1];         for (int i = 0; i <= n; i++) {             memo[i] = -1;         }         return countRecur(n, memo);     }      static void Main(string[] args) {         int n = 7;         Console.WriteLine(countWays(n));     } }

JavaScript

// JavaScript program to express // n as sum of 1, 3, 5.  function countRecur(n, memo) {      // base case     if (n < 0)         return 0;     if (n === 0)         return 1;      // If value is memoized     if (memo[n] !== -1) {         return memo[n];     }      // Memoize the state      memo[n] = countRecur(n - 1, memo) +                countRecur(n - 3, memo) +                countRecur(n - 5, memo);      return memo[n]; }  // Returns the number of  // arrangements to form 'n'  function countWays(n) {     let memo = Array(n + 1).fill(-1);     return countRecur(n, memo); }  let n = 7; console.log(countWays(n));

Output

Time Complexity: O(n), As at every stage we need to take three decisions and the height of the tree will be of the order of n.
Auxiliary Space: O(n + n), The extra space is used due to the recursion call stack and memo array of size n+1 is used to store the results of subproblems.

Using Bottom-Up DP (Tabulation)

We define a DP array where each element dp[i] represents the number of ways to form the sum ‘i’. Starting with the base case dp[0] = 1 (since there is exactly one way to form a sum of 0 – using no numbers), we iteratively calculate the number of ways to form each value from 1 to n.

C++

// C++ program to express // n as sum of 1, 3, 5. #include <bits/stdc++.h> using namespace std;  // Returns the number of // arrangements to form 'n' int countWays(int n) {      vector<int> dp(n + 1);      dp[0] = 1;      for (int i = 1; i <= n; i++) {         dp[i] = 0;          if (i - 1 >= 0)             dp[i] += dp[i - 1];         if (i - 3 >= 0)             dp[i] += dp[i - 3];         if (i - 5 >= 0)             dp[i] += dp[i - 5];     }      return dp[n]; }  int main() {     int n = 7;     cout << countWays(n); }

Java

// Java program to express // n as sum of 1, 3, 5.  class GfG {      // Returns the number of      // arrangements to form 'n'      static int countWays(int n) {         int[] dp = new int[n + 1];         dp[0] = 1;          for (int i = 1; i <= n; i++) {             dp[i] = 0;              if (i - 1 >= 0) dp[i] += dp[i - 1];             if (i - 3 >= 0) dp[i] += dp[i - 3];             if (i - 5 >= 0) dp[i] += dp[i - 5];         }          return dp[n];     }      public static void main(String[] args) {         int n = 7;         System.out.println(countWays(n));     } }

Python

# Python program to express # n as sum of 1, 3, 5.  # Returns the number of  # arrangements to form 'n'  def countWays(n):     dp = [0] * (n + 1)     dp[0] = 1      for i in range(1, n + 1):         dp[i] = 0          if i - 1 >= 0:             dp[i] += dp[i - 1]         if i - 3 >= 0:             dp[i] += dp[i - 3]         if i - 5 >= 0:             dp[i] += dp[i - 5]      return dp[n]  if __name__ == "__main__":     n = 7     print(countWays(n))

// C# program to express // n as sum of 1, 3, 5.  using System;  class GfG {      // Returns the number of      // arrangements to form 'n'      static int countWays(int n) {         int[] dp = new int[n + 1];         dp[0] = 1;          for (int i = 1; i <= n; i++) {             dp[i] = 0;              if (i - 1 >= 0) dp[i] += dp[i - 1];             if (i - 3 >= 0) dp[i] += dp[i - 3];             if (i - 5 >= 0) dp[i] += dp[i - 5];         }          return dp[n];     }      static void Main(string[] args) {         int n = 7;         Console.WriteLine(countWays(n));     } }

JavaScript

// JavaScript program to express // n as sum of 1, 3, 5.  // Returns the number of  // arrangements to form 'n'  function countWays(n) {     let dp = new Array(n + 1).fill(0);     dp[0] = 1;      for (let i = 1; i <= n; i++) {         dp[i] = 0;          if (i - 1 >= 0) dp[i] += dp[i - 1];         if (i - 3 >= 0) dp[i] += dp[i - 3];         if (i - 5 >= 0) dp[i] += dp[i - 5];     }      return dp[n]; }  let n = 7; console.log(countWays(n));

Output

Time Complexity: O(n), As we just need to make 3n function calls and there will be no repetitive calculations as we are returning previously calculated results.
Auxiliary Space: O(n), dp array of size n+1 is used to store the results of subproblems.

Please Refer to Tabulation vs Memoization to understand the difference between memoization and tabulation.

Dynamic Programming comes with lots of practice. One must try solving various classic DP problems that can be found here. You may check the below problems first and try solving them using the above-described steps:-

S. No.	Problem	Practice link
1	Min Cost Path	solve
2	Subset Sum Problem	solve
3	Coin Change	solve
4	Edit Distance	solve
5	Cutting a Rod	solve

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Steps to solve a Dynamic Programming Problem