TheAlgorithms · yanglbme · May 11, 2019 · May 10, 2019 · May 10, 2019 · May 11, 2019
@@ -116,6 +116,18 @@ __Properties__
 * Average case performance	O(log n)
 * Worst case space complexity	O(1) 
 
+### Jump
+![alt-text][jump-image]
+
+From [Wikipedia][jump-wiki]: Jump search or block search refers to a search algorithm for ordered lists. It works by first checking all items Lkm, where {\displaystyle k\in \mathbb {N} } k\in \mathbb {N} and m is the block size, until an item is found that is larger than the search key. To find the exact position of the search key in the list a linear search is performed on the sublist L[(k-1)m, km].
+
+__Properties__
+* Worst case performance  O(n)
+* Best case performance O(√n)
+* Average case performance  O(√n)
+* Worst case space complexity O(1) 
+
+
 ----------------------------------------------------------------------------------------------------------------------
 
 ## Ciphers
@@ -177,5 +189,8 @@ The Luhn algorithm or Luhn formula, also known as the "modulus 10" or "mod 10" a
 [binary-wiki]: https://en.wikipedia.org/wiki/Binary_search_algorithm
 [binary-image]: https://upload.wikimedia.org/wikipedia/commons/f/f7/Binary_search_into_array.png
 
+[jump-wiki]: https://en.wikipedia.org/wiki/Jump_search
+[jump-image]: https://i1.wp.com/theoryofprogramming.com/wp-content/uploads/2016/11/jump-search-1.jpg
+
 
 [caesar]: https://upload.wikimedia.org/wikipedia/commons/4/4a/Caesar_cipher_left_shift_of_3.svg
@@ -0,0 +1,35 @@
+/* The Jump Search algorithm allows to combine a linear search with a speed optimization.
+  * This means that instead of going 1 by 1, we will increase the step of √n and increase that 
+  * step of √n which make the step getting bigger and bigger.
+  * The asymptotic analysis of Jump Search is o(√n). Like the binary search, it needs to be sorted.
+  * The advantage against binary search is that Jump Search traversed back only once.
+ */
+
+const jumpSearch = (arr, value) => {
+    const length = arr.length;
+    let step = Math.floor(Math.sqrt(length));
+    let lowerBound = 0;
+    while (arr[Math.min(step, length) - 1] < value) {
+        lowerBound = step;
+        step += step;
+        if (lowerBound >= length) {
+            return -1;
+        }
+    }
+
+    const upperBound = Math.min(step, length);
+    while (arr[lowerBound] < value) {
+        lowerBound++;
+        if (lowerBound === upperBound) {
+            return -1;
+        }
+    }
+    if (arr[lowerBound] === value) {
+        return lowerBound;
+    }
+    return -1;
+}
+const arr = [0,0,4,7,10,23,34,40,55,68,77,90]
+jumpSearch(arr,4);
+jumpSearch(arr,34);
+jumpSearch(arr,77);