I’m not able to open that link directly. Could you please share the problem statement here (paste the text), or at least describe:

- Input format and constraints
- What exactly to output (LCS length, number of LCS, count modulo something, number of distinct LCS, etc.)
- Any special requirements (time/memory limits, multiple test cases, reconstruction needed, large inputs, etc.)
- A sample input and output

If you’d like, I can provide a general solution template for common DPLCS-type problems. Here are a few possibilities and how I’d approach them:

1) Longest Common Subsequence length (classic)
- DP: dp[i][j] = LCS length of first i chars of A and first j chars of B
- Recurrence:
- if A[i-1] == B[j-1], dp[i][j] = dp[i-1][j-1] + 1
- else dp[i][j] = max(dp[i-1][j], dp[i][j-1])
- Optimization: O(nm) time, O(min(n,m)) memory with rolling arrays
- Simple reconstruction can be added if needed

2) Number of LCS (counting how many LCS sequences)
- You can extend the DP to count ways, being careful with duplicates:
- If A[i-1] == B[j-1], dpLen[i][j] = dpLen[i-1][j-1] + 1
count[i][j] = count[i-1][j-1]
- Else:
- if dpLen[i-1][j] > dpLen[i][j-1], take from top
- if <, take from left
- if equal, sum counts from top and left (and subtract from top-left if dpLen[i-1][j-1] == dpLen[i][j])
- This gets tricky to avoid counting duplicates; I can provide a robust version if that’s your goal.

3) Number of distinct LCS (unique sequences)
- Much trickier; typically requires careful handling to avoid duplicates, sometimes with sets or more advanced DP-with-next-array optimizations.
- I can tailor this if you confirm it’s the “distinct” variant.

4) Large alphabets or special constraints
- If strings are huge, we might need optimizations like bitset-based LCS (for binary alphabet or small alphabets) or patience/suffix-automaton tricks for variants.

If you share the problem text, I’ll tailor a precise solution (with code in C++, Python, or Java) to match DPLCS2’s exact requirements.