Intro to Algorithms and DataStructure

1. Data Structures and
Algorithms
By Prasanna Jatla
Sr Software Engineer
American Express

2. Basic Data Structures
• Overview of Arrays
• Overview of Linked Lists
• Overview of Stacks and Queues

3. What is
Recursion
• Recursionis a programming technique where a function calls itself directly or
indirectly.
• The function keeps calling itselfuntil a base case is reached, which stops the
recursion.
• Each recursive call typically creates a newactivation recordon the call stack.
• Recursionallows us to break down complex problems into smaller, self-similar
subproblems.
Advantages ofRecursion:
• Elegant solutions: Recursioncan provide elegantandconcise solutions to
problems that might be cumbersome with iterative approaches (using loops).
• Self-similar problems: Recursionshines when dealing with problems that
exhibitself-similarity, where the solution involves solving smaller versions ofthe
originalproblem.
• Improved readability: In some cases, recursive code canbe more readable
and easier tounderstandcompared to iterative solutions, especiallyfor
problems with inherentself-similarity.

4. Real-world
Applications
of Recursion
• Factorials: Calculating factorials of numbers is a
classic example of recursion.
• Tree traversals: Recursion is commonly used to
traverse tree data structures (like binary trees) in
a depth-firstmanner.
• File system navigation: Recursioncan be used
to navigate throughdirectory structureswithin a
file system.
• Maze solving algorithms: Several maze-solving
algorithms, like backtracking,utilize recursionto
explore differentpaths within the maze.
• Divide and conquer algorithms: Many divide-
and-conqueralgorithms, like merge sort and
quick sort, rely on recursionto break down
problems into smaller subproblems.

5. Analyzing time complexity
• Definition of AsymptoticNotation
• Importancein AnalyzingAlgorithm
Efficiency
• Definition of Big O Notation
• Analyzetime complexity of below code.

6. Dynamic Programming
• Core Idea: Breaks down a complex problem into smaller, overlapping
subproblems.
• Solves subproblems: Computes the optimal solutions for each
subproblem and stores them for reuse.
• Bottom-up approach: Builds solutions for larger subproblems using
previously solved smaller ones.
• Suitable for: Optimization problems with overlapping subproblems
(e.g., shortest path, edit distance).

7. Solving some Interview coding problems
• Generate all subsets , given an array of numbers
• K sum problem
• Check number of uni-value sub nodes in a tree.

8. Greedy Algorithms
• Core Idea: Makes the locally optimal choice at
each step with the hope of achieving a globally
optimal solution.
• Heuristic approach: Employs a set of rules to
make seemingly optimal choices at each step.
• Simpler implementation: Often easier to
understand and implement compared to
Dynamic Programming.
• No guarantee of global optimality: The locally
optimal choice at each step might not lead to the
overall best solution.

9. Q & A