Rat in a Maze Problem in DSA Javascript - Time & Space Complexity

Time Complexity: Rat in a Maze Problem

O(2^(2n))

Understanding Time Complexity

We want to understand how the time needed to solve the Rat in a Maze problem changes as the maze size grows.

Specifically, how does the number of steps the algorithm takes grow when the maze gets bigger?

Scenario Under Consideration

Analyze the time complexity of the following code snippet.

function solveMaze(maze, x, y, solution) {
  const n = maze.length;
  if (x === n - 1 && y === n - 1) {
    solution[x][y] = 1;
    return true;
  }
  if (x >= 0 && y >= 0 && x < n && y < n && maze[x][y] === 1) {
    solution[x][y] = 1;
    if (solveMaze(maze, x + 1, y, solution)) return true;
    if (solveMaze(maze, x, y + 1, solution)) return true;
    solution[x][y] = 0;
    return false;
  }
  return false;
}
    

This code tries to find a path from the top-left to the bottom-right of a maze using backtracking.

Identify Repeating Operations

Identify the loops, recursion, array traversals that repeat.

• Primary operation: Recursive calls exploring possible paths in the maze.
• How many times: Potentially, each cell is visited multiple times as the algorithm tries different paths.

How Execution Grows With Input

As the maze size (n x n) grows, the number of possible paths grows very fast because the rat can move right or down at each step.

Input Size (n)	Approx. Operations
2	Few (around 2-4 paths)
4	Many (dozens of paths)
8	Very many (thousands of paths)

Pattern observation: The number of paths grows exponentially as the maze size increases.

Final Time Complexity

Time Complexity: O(2^(2n))

This means the time grows exponentially with n, making the problem very slow for large mazes.

Common Mistake

[X] Wrong: "The algorithm only checks one path, so it runs quickly."

[OK] Correct: The algorithm tries many paths by exploring all possible moves, so it can take a very long time for bigger mazes.

Interview Connect

Understanding this problem helps you think about how recursive backtracking explores many options and why some problems can be slow without shortcuts.

Self-Check

"What if we added memoization to remember visited cells? How would the time complexity change?"

Practice

1. What is the main goal of the Rat in a Maze problem in algorithms?

easy

A. Calculate the shortest distance between two points in a graph

B. Count the number of walls in the maze

C. Find a path from the start to the destination in a maze

D. Sort the maze cells in ascending order

Solution

1. Step 1: Understand the problem objective

   The Rat in a Maze problem requires finding a path from the start cell to the destination cell in a maze grid.

2. Step 2: Identify the correct goal

   The goal is not counting walls, sorting cells, or shortest distance in a graph, but specifically finding a valid path through the maze.

3. Final Answer:

   Find a path from the start to the destination in a maze -> Option C

4. Quick Check:

   Goal = Find path [OK]

Hint: Focus on pathfinding from start to end in the maze [OK]

Common Mistakes:

• Confusing pathfinding with counting or sorting
• Thinking it's about shortest path always
• Assuming maze cells need sorting

2. Which of the following is the correct way to check if a cell (x, y) is safe to move in a maze represented by a 2D array maze in JavaScript?

easy

A. if (x >= 0 && y >= 0 && x <= maze.length && y <= maze[0].length && maze[x][y] === 0)

B. if (x >= 0 && y >= 0 && x < maze.length && y < maze[0].length && maze[x][y] === 1)

C. if (x > 0 && y > 0 && x <= maze.length && y <= maze[0].length && maze[x][y] === 1)

D. if (x > 0 && y > 0 && x < maze.length && y < maze[0].length && maze[x][y] === 0)

Solution

1. Step 1: Check boundary conditions correctly

   Valid indices in JavaScript arrays start at 0 and go up to length - 1, so conditions must use >= 0 and < length.

2. Step 2: Check cell value for path

   In the Rat in a Maze problem, 1 usually means path is open, so maze[x][y] === 1 is correct.

3. Final Answer:

   if (x >= 0 && y >= 0 && x < maze.length && y < maze[0].length && maze[x][y] === 1) -> Option B

4. Quick Check:

   Boundary + path check = if (x >= 0 && y >= 0 && x < maze.length && y < maze[0].length && maze[x][y] === 1) [OK]

Hint: Use >= 0 and < length for array bounds, check for 1 as path [OK]

Common Mistakes:

• Using > 0 excludes index 0
• Using <= length causes out-of-bound errors
• Checking for 0 instead of 1 as path

3. What will be the output of the following JavaScript code snippet for the Rat in a Maze problem?

const maze = [
  [1, 0, 0, 0],
  [1, 1, 0, 1],
  [0, 1, 0, 0],
  [1, 1, 1, 1]
];

function solveMaze(x, y, maze, path) {
  if (x === maze.length - 1 && y === maze[0].length - 1) {
    path.push([x, y]);
    return true;
  }
  if (x >= 0 && y >= 0 && x < maze.length && y < maze[0].length && maze[x][y] === 1) {
    path.push([x, y]);
    maze[x][y] = 0;
    if (solveMaze(x + 1, y, maze, path)) return true;
    if (solveMaze(x, y + 1, maze, path)) return true;
    path.pop();
    return false;
  }
  return false;
}

const path = [];
solveMaze(0, 0, maze, path);
console.log(path);

medium

A. [[0,0],[0,1],[1,1],[2,1],[3,1],[3,2],[3,3]]

B. []

C. [[0,0],[1,0],[1,1],[1,2],[2,2],[3,2],[3,3]]

D. [[0,0],[1,0],[1,1],[2,1],[3,1],[3,2],[3,3]]

Solution

1. Step 1: Trace the recursive path from (0,0)

   Starting at (0,0), the function tries moving down (x+1,y) first, then right (x,y+1). It marks visited cells as 0 to avoid revisiting.

2. Step 2: Follow the path found

   The path found is (0,0) -> (1,0) -> (1,1) -> (2,1) -> (3,1) -> (3,2) -> (3,3), which is a valid path to the destination.

3. Final Answer:

   [[0,0],[1,0],[1,1],[2,1],[3,1],[3,2],[3,3]] -> Option D

4. Quick Check:

   Path traced matches [[0,0],[1,0],[1,1],[2,1],[3,1],[3,2],[3,3]] [OK]

Hint: Follow down moves first, then right moves recursively [OK]

Common Mistakes:

• Assuming right moves tried before down
• Not marking visited cells causing infinite loops
• Misreading maze cell values

4. Identify the bug in the following JavaScript code snippet for the Rat in a Maze problem and select the fix.

function solveMaze(x, y, maze, path) {
  if (x === maze.length - 1 && y === maze[0].length - 1) {
    path.push([x, y]);
    return true;
  }
  if (x >= 0 && y >= 0 && x < maze.length && y < maze[0].length && maze[x][y] === 1) {
    path.push([x, y]);
    if (solveMaze(x + 1, y, maze, path)) return true;
    if (solveMaze(x, y + 1, maze, path)) return true;
    path.pop();
    return false;
  }
  return false;
}

medium

A. Mark visited cells as 0 after pushing to path to avoid revisiting

B. Remove path.pop() to keep all visited cells in path

C. Change base case to check if x > maze.length and y > maze[0].length

D. Add a check to move diagonally (x+1, y+1) in recursion

Solution

1. Step 1: Identify missing visited marking

   The code does not mark visited cells as 0, so it may revisit the same cell causing infinite recursion.

2. Step 2: Fix by marking visited cells

   After pushing a cell to path, mark maze[x][y] = 0 to avoid revisiting and infinite loops.

3. Final Answer:

   Mark visited cells as 0 after pushing to path to avoid revisiting -> Option A

4. Quick Check:

   Visited marking fixes infinite recursion [OK]

Hint: Mark visited cells to prevent infinite loops [OK]

Common Mistakes:

• Forgetting to mark visited cells
• Removing path.pop() causing wrong paths
• Changing base case incorrectly

5. Given a maze represented by a 2D array where 1 means open path and 0 means blocked, how would you modify the Rat in a Maze algorithm to find all possible paths from start (0,0) to destination (n-1,n-1)?

hard

A. Use backtracking to explore all directions and store each complete path in a list

B. Stop recursion after finding the first path and return it immediately

C. Use a queue to perform breadth-first search and return the shortest path only

D. Mark all visited cells permanently to avoid revisiting any cell

Solution

1. Step 1: Understand the requirement to find all paths

   Finding all paths means exploring every possible route from start to destination without stopping at the first success.

2. Step 2: Use backtracking to explore all paths

   Backtracking allows exploring all directions, storing each valid path, and backtracking to try other routes.

3. Final Answer:

   Use backtracking to explore all directions and store each complete path in a list -> Option A

4. Quick Check:

   All paths require full backtracking exploration [OK]

Hint: Backtrack fully and save each path to find all solutions [OK]

Common Mistakes:

• Stopping after first path found
• Using BFS only for shortest path
• Marking visited cells permanently blocking other paths