Pretty much a sliding window or single-pass min-tracking problem.
Clarify the problem constraints (e.g., single transaction, return 0 if no profit) and then explain a one-pass solution that tracks the minimum price seen so far and the maximum profit. Walk through a small example to demonstrate correctness and analyze time and space complexity.
Pro tip: Mention that this is a classic 'best time to buy and sell stock' problem and that the one-pass approach is optimal; also note that you can extend it to handle multiple transactions if needed, showing awareness of variations.
Confirm that only one buy-sell transaction is allowed, that the buy must occur before the sell, and that if no profit is possible, return 0. Discuss edge cases like empty array, single element, or strictly decreasing prices.
Briefly mention that a brute-force solution would check all pairs of buy and sell days, which is O(n^2) time. This sets the stage for optimization.
Explain that you can iterate through the array once, keeping track of the minimum price seen so far and the maximum profit. For each price, update the minimum and then compute the potential profit if sold today, updating the maximum profit if larger.
Use a small array like [7,1,5,3,6,4] to demonstrate how the algorithm works step by step, showing how min price and max profit are updated.
State that the time complexity is O(n) and space complexity is O(1). Optionally, mention how the solution could be adapted for multiple transactions or other constraints.
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