big o

Author: Wakidur Rahaman

src/big-O-complexities/big-O-complexities-01.js

@@ -12,6 +12,9 @@
  * O(n2) is quadratic time,
  *
  * O(n3) is cubic time.
+ *
+ * logarithmic time complexity
+ * is printing elements that are a power of 2 between 2 and n
 
 
  */
@@ -45,3 +48,13 @@ function printCubicTime(array) {
 }
 var cubicTimeArray = ['a', 'b', 'c', 'd', 'e', 'f'];
 console.log(printCubicTime(cubicTimeArray));
+/**
+ * The efficiency of logarithmic time complexities is apparent with large inputs such as a million items.
+ * Although n is a million, exampleLogarithmic will print only 19 items because log2(1,000,000) = 19.9315686.
+ */
+function printLogarithmic(n) {
+    for (var i = 2; i <= n; i = i * 2) {
+        console.log(i);
+    }
+}
+printLogarithmic(1000000);

src/big-O-complexities/big-O-complexities-01.ts

@@ -12,6 +12,9 @@
  * O(n2) is quadratic time,
  *
  * O(n3) is cubic time.
+ *
+ * logarithmic time complexity
+ * is printing elements that are a power of 2 between 2 and n
 
 
  */
@@ -52,3 +55,16 @@ function printCubicTime(array: string[]) {
 }
 const cubicTimeArray = ['a', 'b', 'c', 'd', 'e', 'f'];
 console.log(printCubicTime(cubicTimeArray));
+
+/**
+ * The efficiency of logarithmic time complexities is apparent with large inputs such as a million items.
+ * Although n is a million, exampleLogarithmic will print only 19 items because log2(1,000,000) = 19.9315686.
+ */
+
+function printLogarithmic(n: number) {
+  for (let i = 2; i <= n; i = i * 2) {
+    console.log(i);
+  }
+}
+
+printLogarithmic(1000000);