In this approach, we traverse the prices array while keeping track of the minimum price seen so far and calculate the maximum profit we could achieve if we sold on that day. The maximum profit is updated accordingly through each iteration.
This approach makes a single pass through the array (O(n) time complexity) and uses constant space (O(1) space complexity).
Time Complexity: O(n), where n is the number of days.
Space Complexity: O(1).
1function maxProfit(prices) {
2 let minPrice = Infinity;
3 let maxProfit = 0;
4 for (let price of prices) {
5 if (price < minPrice) {
6 minPrice = price;
7 } else if (price - minPrice > maxProfit) {
8 maxProfit = price - minPrice;
9 }
10 }
11 return maxProfit;
12}
13
14const prices = [7, 1, 5, 3, 6, 4];
15console.log("Max Profit:", maxProfit(prices));The JavaScript approach here uses similar logic, iterating over the prices array and updating the minimum price and maximum potential profit for each day's price examined.
This approach considers all possible pairs of buy and sell days, calculating the profit for each combination. It is straightforward but inefficient due to its O(n^2) time complexity, which is impractical for large inputs.
Time Complexity: O(n^2), where n is the number of days.
Space Complexity: O(1).
1def maxProfit(prices):
2 max_profit = 0
3 for i in range(len(prices)):
4 for j in range(i + 1, len(prices)):
5 profit = prices[j] - prices[i]
6 if profit > max_profit:
7 max_profit = profit
8 return max_profit
9
10prices = [7, 1, 5, 3, 6, 4]
11print("Max Profit (Brute Force):", maxProfit(prices))This Python brute force approach manually examines each potential sell price following every buy price, updating max_profit with the highest difference captured.