adding time complexity for divide and conquer

Author: rasbt

ipython_nbs/essentials/divide-and-conquer-algorithm-intro.ipynb

@@ -374,6 +374,19 @@
     "print(lst5[::-1], '->', majority_ele_dac(lst=lst5[::-1]))"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In algorithms such as binary search that we saw at the beginning of this notebook, we recursively break down our problem into smaller subproblems. Thus, we have a recurrence problem with time complexity\n",
+    "\n",
+    "$T(n) = T(\\frac{2}{n}) + O(1) \\rightarrow T(n) = O(\\log n).$\n",
+    "\n",
+    "In this example, finding the majority element, we break our problem down into only 2 subproblems. Thus, the complexity of our algorithm is\n",
+    "\n",
+    "$T(n) = 2T (\\frac{2}{n} + O(n) \\rightarrow T(n) = O(n \\log n).$"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},