From 41e41d804d105358a512e7a6aaa0feb1e3db4522 Mon Sep 17 00:00:00 2001
From: Ashita Prasad <ashitaprasad92@gmail.com>
Date: Thu, 13 Jun 2024 01:33:30 +0530
Subject: [PATCH] Update dynamic-programming.md

---
 contrib/ds-algorithms/dynamic-programming.md | 20 +++-----------------
 1 file changed, 3 insertions(+), 17 deletions(-)

diff --git a/contrib/ds-algorithms/dynamic-programming.md b/contrib/ds-algorithms/dynamic-programming.md
index 54c6fcb..0f16d5c 100644
--- a/contrib/ds-algorithms/dynamic-programming.md
+++ b/contrib/ds-algorithms/dynamic-programming.md
@@ -51,10 +51,6 @@ print(f"The {n}th Fibonacci number is: {fibonacci(n)}.")
 - **Time Complexity**: O(n) for both approaches
 - **Space Complexity**: O(n) for the top-down approach (due to memoization), O(1) for the bottom-up approach
 
-</br>
-<hr>
-</br>
-
 # 2. Longest Common Subsequence
 
 The longest common subsequence (LCS) problem is to find the longest subsequence common to two sequences. A subsequence is a sequence that appears in the same relative order but not necessarily contiguous.
@@ -112,10 +108,6 @@ print("Length of LCS is", longestCommonSubsequence(S1, S2, m, n))
 - **Time Complexity**: O(m * n) for both approaches, where m and n are the lengths of the input sequences
 - **Space Complexity**: O(m * n) for the memoization table
 
-</br>
-<hr>
-</br>
-
 # 3. 0-1 Knapsack Problem
 
 The 0-1 knapsack problem is a classic optimization problem where the goal is to maximize the total value of items selected while keeping the total weight within a specified limit.
@@ -146,6 +138,7 @@ capacity = 50
 n = len(weights)
 print("Maximum value that can be obtained:", knapsack(weights, values, capacity, n))
 ```
+
 ## 0-1 Knapsack Problem Code in Python (Bottom-up Approach)
 
 ```python
@@ -170,14 +163,11 @@ capacity = 50
 n = len(weights)
 print(knapSack(capacity, weights, values, n))
 ```
+
 ## Complexity Analysis
 - **Time Complexity**: O(n * W) for both approaches, where n is the number of items and W is the capacity of the knapsack
 - **Space Complexity**: O(n * W) for the memoization table
 
-</br>
-<hr>
-</br>
-
 # 4. Longest Increasing Subsequence
 
 The Longest Increasing Subsequence (LIS) is a task is to find the longest subsequence that is strictly increasing, meaning each element in the subsequence is greater than the one before it. This subsequence must maintain the order of elements as they appear in the original sequence but does not need to be contiguous. The goal is to identify the subsequence with the maximum possible length.
@@ -190,7 +180,6 @@ The Longest Increasing Subsequence (LIS) is a task is to find the longest subseq
 ## Longest Increasing Subsequence Code in Python (Top-Down Approach using Memoization)
 
 ```python
-
 import sys
 
 def f(idx, prev_idx, n, a, dp):
@@ -241,10 +230,7 @@ def lis(arr):
 arr = [10, 22, 9, 33, 21, 50, 41, 60]
 print("Length of lis is", lis(arr))
 ```
+
 ## Complexity Analysis
 - **Time Complexity**: O(n * n) for both approaches, where n is the length of the array.
 - **Space Complexity**: O(n * n) for the memoization table in Top-Down Approach, O(n) in Bottom-Up Approach.
-
-</br>
-<hr>
-</br>