diff --git a/DSA/three_sum.py b/DSA/three_sum.py
new file mode 100644
index 0000000..2c26099
--- /dev/null
+++ b/DSA/three_sum.py
@@ -0,0 +1,26 @@
+# Three Sum dynamic problem solution
+def threeSum(self, nums: List[int]) -> List[List[int]]:
+        res = []
+        nums.sort()
+
+        for i, num1 in enumerate(nums):
+
+            if i > 0 and num1 == nums[i-1]:
+                continue
+
+            start, end = i+1, len(nums) - 1
+
+            while start < end:
+                sum = num1 + nums[start] + nums[end]
+
+                if sum > 0:
+                    end -= 1
+                elif sum < 0:
+                    start += 1
+                else:
+                    res.append([num1, nums[start], nums[end]])
+                    start += 1
+                    while nums[start] == nums[start-1] and start < end:
+                        start += 1
+
+        return res
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diff --git a/Search Algorithms/Exponential Search/README.md b/Search Algorithms/Exponential Search/README.md
new file mode 100644
index 0000000..70132b8
--- /dev/null
+++ b/Search Algorithms/Exponential Search/README.md	
@@ -0,0 +1,14 @@
+<h1>Exponential Search</h1>
+
+<h4>
+    Exponential search is also known as doubling or galloping search. This mechanism is used to find the range where the search key may present. If L and U are the upper and lower bound of the list, then L and U both are the power of 2. For the last section, the U is the last position of the list. For that reason, it is known as exponential.
+</h4>
+<br>
+<h3> Complexity of Exponential Search Technique </h3>
+<hr>
+
+<h4>
+Time Complexity: O(1) for the best case. O(log2 i) for average or worst case. Where i is the location where search key is present.
+<br>
+Space Complexity: O(1)
+</h4>
diff --git a/Search Algorithms/Exponential Search/exponential_search.py b/Search Algorithms/Exponential Search/exponential_search.py
new file mode 100644
index 0000000..f7cc0d7
--- /dev/null
+++ b/Search Algorithms/Exponential Search/exponential_search.py	
@@ -0,0 +1,27 @@
+# Exponential Search 
+def binary_search(arr, low, high, x):
+    while low <= high:
+        mid = low + (high - low)/2;
+
+        if arr[mid] == x:
+            return mid
+        elif arr[mid] < x:
+            low = mid + 1
+        else:
+            high = mid - 1
+
+    return -1
+
+def exponential_search(arr, low, high, x):
+    if arr[low] == x:
+        return low
+
+    i = (low + 1) * 2
+    while i <= high and arr[i] <= x:
+        i *= 2
+
+    return binary_search(arr, i / 2, min(i, high), x)
+
+# arr = range(0,500)
+# x = 42
+# print(exponential_search(arr, 0, len(arr), x))
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