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Problem solution · C++

Maximum Balanced Subsequence Sum

Maximum Balanced Subsequence Sum: a C++ solution using sorting and greedy selection. Learn the idea, check the complexity, and read the full code, with credit to walkccc LeetCode Solutions.

Technique
Sorting and greedy selection
Source
walkccc LeetCode Solutions
Length
61 lines
Start with the idea.

Try the problem first. If you get stuck, read the approach below, then write your own solution. The full code is at the bottom.

Approach

Sorting and greedy selection

For Maximum Balanced Subsequence Sum, the implementation first exposes a useful order, then scans that order while making locally justified choices.

  1. Choose the key that reveals the greedy or grouping structure.
  2. Sort the relevant records by that key.
  3. Scan in order, maintaining the invariant that makes each local choice safe.

Code notes

  • 61 lines of C++ from the credited upstream file 2926.cpp.
  • The implementation visibly relies on sequence storage.
  • 4 loop blocks detected.

Complexity

Sorting is typically the dominant term unless the subsequent scan uses a more expensive nested operation.

Check the problem constraints before deciding whether this complexity will pass.

Source

Code and credit

This code comes from walkccc LeetCode Solutions by P.-Y. Chen (walkccc) and is used under the MIT licence.

Open problem ↗Original source ↗
Full codeMaximum Balanced Subsequence Sum · C++C++
Use this to learn the idea, then write your own version.
class FenwickTree { public:  FenwickTree(int n) : vals(n + 1) {}   // Updates the maximum sum of subsequence ending in (i - 1) with `val`.  void maximize(int i, long val) {    while (i < vals.size()) {      vals[i] = max(vals[i], val);      i += lowbit(i);    }  }   // Returns the maximum sum of subsequence ending in (i - 1).  long get(int i) const {    long res = 0;    while (i > 0) {      res = max(res, vals[i]);      i -= lowbit(i);    }    return res;  }  private:  vector<long> vals;   static int lowbit(int i) {    return i & -i;  }}; class Solution { public:  long long maxBalancedSubsequenceSum(vector<int>& nums) {    // Let's define maxSum[i] := subsequence with the maximum sum ending in i    // By observation:    //     nums[i] - nums[j] >= i - j    //  => nums[i] - i >= nums[j] - j    //  So, if nums[i] - i >= nums[j] - j, where i > j,    //  maxSum[i] = max(maxSum[i], maxSum[j] + nums[i])    long ans = LONG_MIN;    FenwickTree tree(nums.size());     for (const auto& [_, i] : getPairs(nums)) {      const long subseqSum = tree.get(i) + nums[i];      tree.maximize(i + 1, subseqSum);      ans = max(ans, subseqSum);    }     return ans;  }  private:  vector<pair<int, int>> getPairs(const vector<int>& nums) {    vector<pair<int, int>> pairs;    for (int i = 0; i < nums.size(); ++i)      pairs.emplace_back(nums[i] - i, i);    ranges::sort(pairs);    return pairs;  }};

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