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Nilesh Raut
Nilesh Raut

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LeetCode1425: Solving Constrained Subsequence Sum with DP and Heap Approach

Hey there, fellow coding enthusiasts! Today, we're diving into an intriguing LeetCode problem, Constrained Subsequence Sum. This problem is not only a great way to sharpen your dynamic programming and heap skills but also a valuable addition to your coding repertoire. Let's get started.

LeetCode1425. Constrained Subsequence Sum -DP : NileshBlog.Tech

Master Leetcode 1425 and conquer Constrained Subsequence Sum. Learn efficient algorithms to solve this challenging problem.



LeetCode1425. Constrained Subsequence Sum is a medium-level problem that asks us to find the maximum sum of a non-empty subsequence of a given array, subject to the constraint that no two elements in the subsequence should be adjacent. We'll explore two approaches to solve this problem: Dynamic Programming (DP) and a clever heap-based method.


Before we jump into the code, make sure you have a good grasp of dynamic programming and the basics of heaps. Understanding how heaps (or priority queues) work will be especially crucial for the second approach.

The DP Approach

Dynamic Programming is a powerful tool for solving problems like this. We'll create an array to keep track of the maximum sum ending at each element. This way, we can make sure we don't use adjacent elements in our subsequence.

Let's walk through the process step by step:

  1. Create a DP array of the same size as the input array.
  2. Initialize the first two elements of the DP array with the maximum of the input array's first element and zero.
  3. Loop through the input array, starting from the third element.
  4. At each step, calculate the maximum of two possibilities: a. The current element plus the DP value two steps back. b. The DP value from the previous step.
  5. Update the DP array accordingly.
  6. The maximum value in the DP array will be our answer.

Let's visualize this with a simple example.


Input: [10, 2, 3, 5]

  1. Initialize DP: [10, 10, 0, 0]
  2. Start looping from the third element (3).
  3. DP[2] + 3 = 10 + 3 = 13
  4. DP[1] = 10
  5. Update DP: [10, 10, 13, 0]
  6. Maximum value in DP: 13

So, the maximum sum is 13.

The Heap Approach

For this method, we'll use a heap to efficiently keep track of the maximum elements that can be added to the subsequence without violating the constraint. The heap will help us ensure that the elements are in descending order of value.

Here's how we do it:

  1. Initialize a heap and add the first element of the input array.
  2. Loop through the input array, starting from the second element.
  3. At each step, pop elements from the heap until you find an element that doesn't violate the constraint (i.e., not adjacent).
  4. Add the popped element and the current element to get the new maximum, then push it back into the heap.
  5. The top element of the heap will be our answer.

This approach is more efficient than the DP method, especially for large arrays.

Wrapping It Up

In this LeetCode problem, we've explored two distinct approaches: Dynamic Programming and a clever heap-based solution. Understanding both of these methods is a valuable addition to your coding toolkit. Depending on the problem constraints and the size of the input, you can choose the most suitable method to achieve optimal results.

So, the next time you come across a problem like Constrained Subsequence Sum (LeetCode 1425), you'll have a couple of powerful techniques at your disposal to crack it. Happy coding!

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