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This approach involves sorting engineers by their efficiency in descending order, ensuring that each team's minimum efficiency is dominated by its highest-efficiency member being considered rather than doing a costly check of all members.
After this, we use a min-heap to keep track of the top 'k' speeds. The strategy is to iterate through each engineer (sorted by efficiency), compute potential team performance by inserting their speed into the speed sum (if advantageous), and then multiplying by the current engineer's efficiency (the smallest efficiency thus far in the sorted order).
Time Complexity: O(n log n) due to sorting and heap operations.
Space Complexity: O(k) for maintaining the min-heap.
1const maxPerformance = (n, speed, efficiency, k) => {
2 let engineers = [];
3 for (let i = 0
JavaScript handles array manipulations by making use of sorting and direct array operations to manage the equivalent of a min-heap. As employing traditional binary heaps is less straightforward, this implementation still retains the core regarding managing the active best group of engineers selected by their speeds.
Instead of using a heap, an alternative approach employs dynamic programming. For each engineer sorted by efficiency, we determine the maximum performance we can achieve using a binary search to find an optimal combination in an accumulated list of maximum speeds and efficiencies.
This involves recursively filling up a DP table with potential performance values, achieving optimization through binary search.
Time Complexity: O(n^2 log n) in a worst-case naive implementation.
Space Complexity: O(n) for the DP table.
This involves using JavaScript's array handling within a broader DP framework. Binary search properties are integrated into lists advanced speed categorizations to evaluate best combined performance possibilities.