Very easy and short
\n\n# Intuition Behind the Approach:\n\nBy prioritizing children with the highest initial happiness, the algorithm ensures you capture the maximum benefit early
\n\n# Intuition Behind the Approach:\n\nBy prioritizing children with the highest initial happiness, the algorithm ensures you capture the maximum benefit early on. The -i adjustment compensates for the happiness decrease of unselected children, reflecting the dynamic nature of happiness values throughout the selection process.\n\n# Complexity\n- Time complexity:\n$$O(n*log(n))$$ \n\n- Space complexity:\n$$O(1)$$ \n\n# Code\n```\nfunc maximumHappinessSum(happiness []int, k int) int64 {\n\n    sort.Slice(happiness, func(i, j  int) bool {\n        return happiness[i] > happiness[j]\n    })\n\n    ans := int64(0)\n    for i:=0; i<k; i++ {\n        ans += max(int64(0),int64(happiness[i] - i))\n    }\n\n    return ans\n}\n```
['Sorting', 'Go']