Minimum Initial Energy to Finish Tasks
Approach\n Describe your approach to solving the problem. \nSort the Tasks by "Difference":\nWe should prioritize tasks that have a larger difference between mi
# Approach\n<!-- Describe your approach to solving the problem. -->\nSort the Tasks by "Difference":\nWe should prioritize tasks that have a larger difference between minimumi and actuali. This way, we maximize the leftover energy by starting with tasks that are "expensive" in terms of their starting requirement but "cheap" in terms of actual energy consumption.\nSort the tasks by the difference: minimumi - actuali in descending order. This ensures we handle the more restrictive tasks (in terms of energy to start) earlier, leaving more room for the easier tasks later.\n\nSimulate the Execution:\nOnce the tasks are sorted, we calculate the minimum initial energy needed by iterating over the sorted list. For each task, we ensure that we have enough energy to meet its minimumi requirement before subtracting the actuali used to complete the task.\n\n# Complexity\n- Time complexity:\n<!-- Add your time complexity here, e.g. $$O(n)$$ -->\nSorting: Sorting the tasks takes O(nlogn), where n is the number of tasks.\n\nTask iteration: The iteration over the sorted tasks takes O(n). Thus, the overall time complexity is O(nlogn).\n\n- Space complexity:\n<!-- Add your space complexity here, e.g. $$O(n)$$ -->\nThe space complexity is O(1) for storing the energy values and constants, excluding the input space.\n\n# Code\n```python []\nclass Solution:\n    def minimumEffort(self, tasks):\n        # Sort tasks by the difference (minimum - actual), in descending order\n        tasks.sort(key=lambda x: x[1] - x[0], reverse=True)\n        \n        total_energy = 0\n        current_energy = 0\n        \n        for actual, minimum in tasks:\n            # If current energy is less than the minimum required energy for the task\n            if current_energy < minimum:\n                # Add the difference to total_energy and current_energy\n                total_energy += (minimum - current_energy)\n                current_energy = minimum\n            \n            # After performing the task, reduce current energy by the actual cost\n            current_energy -= actual\n        \n        return total_energy\n\n```
['Python']