From 181a129f4555d03fc646ee87d5f0e4e861b3448f Mon Sep 17 00:00:00 2001
From: jeehay <sophiepark528@gmail.com>
Date: Mon, 6 Jan 2025 21:09:22 +0900
Subject: [PATCH] Add best-time-to-buy-and-sell-stock solution

---
 best-time-to-buy-and-sell-stock/Jeehay28.js | 41 +++++++++++++++++++++
 1 file changed, 41 insertions(+)
 create mode 100644 best-time-to-buy-and-sell-stock/Jeehay28.js

diff --git a/best-time-to-buy-and-sell-stock/Jeehay28.js b/best-time-to-buy-and-sell-stock/Jeehay28.js
new file mode 100644
index 000000000..1964dd1e5
--- /dev/null
+++ b/best-time-to-buy-and-sell-stock/Jeehay28.js
@@ -0,0 +1,41 @@
+/**
+ * @param {number[]} prices
+ * @return {number}
+ */
+
+// TC : O(n)
+// SC : O(1)
+
+var maxProfit = function (prices) {
+  if (prices.length === 1) {
+    return 0;
+  }
+
+  // Two variables (profitMax and priceMin) are used to store the maximum profit and minimum price seen, which require O(1) space.
+  let profitMax = 0;
+  let priceMin = prices[0];
+
+  for (const price of prices) {
+    const profit = price - priceMin;
+    profitMax = Math.max(profit, profitMax);
+    priceMin = Math.min(price, priceMin);
+  }
+
+  return profitMax;
+};
+
+// Why Constants Are Ignored in Big-O
+// In Big-O notation, O(2) is simplified to O(1) because constants are irrelevant in asymptotic analysis.
+// Big-O focuses on how resource usage scales with input size, not fixed values.
+
+// Using 2 variables: O(1)
+// Using 10 variables: O(1)
+// Using 100 variables: O(1)
+
+// What Space Complexity Looks Like for Larger Growth
+// O(n): Memory grows linearly with the input size (e.g., storing an array of n elements).
+// O(n^2): Memory grows quadratically (e.g., a 2D matrix with n*n elements).
+// 𝑂(log 𝑛): Memory grows logarithmically (e.g., recursive calls in binary search).
+// O(1): Fixed memory usage, regardless of input size (e.g., using a fixed number of variables).
+
+