Add README.md for Dynamic Programming Algorithms

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+# Dynamic Programming Algorithms
+
+This directory contains various implementations of dynamic programming algorithms. Dynamic programming is a method for solving complex problems by breaking them down into simpler subproblems and storing the results to avoid redundant computations.
+
+## Algorithms Included
+
+- **Coin Change Problem** (`coin_change.rs`)
+  - Determines the minimum number of coins needed to make a given amount.
+  - Time Complexity: O(n * m), where `n` is the amount and `m` is the number of coin types.
+
+- **Edit Distance** (`edit_distance.rs`)
+  - Calculates the minimum number of operations required to convert one string into another.
+  - Time Complexity: O(m * n), where `m` and `n` are the lengths of the two strings.
+
+- **Egg Dropping Problem** (`egg_dropping.rs`)
+  - Determines the minimum number of attempts needed to find the highest floor from which an egg can be dropped without breaking.
+  - Time Complexity: O(k * n), where `k` is the number of eggs and `n` is the number of floors.
+
+- **Fibonacci Sequence** (`fibonacci.rs`)
+  - Computes the `n`th Fibonacci number.
+  - Time Complexity: O(n)
+
+- **Knapsack Problem** (`knapsack.rs`)
+  - Solves the problem of selecting items with given weights and values to maximize total value without exceeding the weight capacity.
+  - Time Complexity: O(n * W), where `n` is the number of items and `W` is the weight capacity.
+
+- **Longest Common Subsequence** (`longest_common_subsequence.rs`)
+  - Finds the longest subsequence present in both of the given sequences.
+  - Time Complexity: O(m * n), where `m` and `n` are the lengths of the two sequences.
+
+- **Longest Increasing Subsequence** (`longest_increasing_subsequence.rs`)
+  - Finds the length of the longest subsequence of a given sequence such that all elements of the subsequence are sorted in increasing order.
+  - Time Complexity: O(n^2) with dynamic programming approach.
+
+- **Maximum Subarray** (`maximum_subarray.rs`)
+  - Finds the contiguous subarray within a one-dimensional array of numbers which has the largest sum.
+  - Time Complexity: O(n)
+
+- **Rod Cutting Problem** (`rod_cutting.rs`)
+  - Determines the maximum revenue obtainable by cutting up a rod and selling the pieces.
+  - Time Complexity: O(n^2)
+
+## References
+- [Introduction to Algorithms by Cormen et al.](https://books.google.co.in/books?id=RSMuEAAAQBAJ&lpg=PR13&ots=a3i4WT9DSM&dq=%5BIntroduction%20to%20Algorithms%20by%20Cormen%20et%20al.%5D&lr&pg=PR13#v=onepage&q=%5BIntroduction%20to%20Algorithms%20by%20Cormen%20et%20al.%5D&f=false)
+- [Dynamic Programming - Wikipedia](https://en.wikipedia.org/wiki/Dynamic_programming)
+
+