Best Time to Buy and Sell Stock

Problem

You are given an array prices where prices[i] is the price of a given stock on the ith day.

You want to maximize your profit by choosing a single day to buy one stock and choosing a different day in the future to sell that stock.

Return the maximum profit you can achieve from this transaction. If you cannot achieve any profit, return 0.

Example 1:

Input: prices = [7,1,5,3,6,4]
Output: 5
Explanation: Buy on day 2 (price = 1) and sell on day 5 (price = 6), profit = 6-1 = 5.
Note that buying on day 2 and selling on day 1 is not allowed because you must buy before you sell.

Example 2:

Input: prices = [7,6,4,3,1]
Output: 0
Explanation: In this case, no transactions are done and the max profit = 0.

Constraints:

  • 1 <= prices.length <= 105
  • 0 <= prices[i] <= 104

Solution

I’m not really sure how this one is considered dynamic programming.

Anyway.

Keep track of the minimum price we’ve seen and the most profit we could have. Go through the array and check if we see a lower price. Update max profit as applicable.

class Solution {
    public int maxProfit(int[] prices) {
        var minPrice = prices[0];
        var maxProfit = 0;

        for (var i : prices) {
            minPrice = Math.min(i, minPrice);
            maxProfit = Math.max(i - minPrice, maxProfit);
        }

        return maxProfit;
    }
}

Recent posts from blogs that I like

Paintings of the Franco-Prussian War: 2 The Siege of Paris

As winter grew colder, Parisians started to starve. A city known for its food and restaurants had to scavenge meals based on horse, dog, cat and even rat.

via The Eclectic Light Company

Impromptu disaster recovery

via fasterthanlime

Notes on implementing Attention

Some notes on implementing attention blocks in pure Python + Numpy. The focus here is on the exact implementation in code, explaining all the shapes throughout the process. The motivation for why attention works is not covered here - there are plenty of excellent online resources explaining it. Seve...

via Eli Bendersky