Unique Paths in Grid DP in DSA Rust - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the problem statement of the Unique Paths in Grid DP?

Find the number of unique ways to move from the top-left corner to the bottom-right corner of a grid, moving only down or right.

beginner

What does DP stand for and why is it used in Unique Paths?

DP stands for Dynamic Programming. It is used to store results of subproblems (paths to each cell) to avoid repeated calculations and improve efficiency.

beginner

In Unique Paths DP, how do you calculate the number of ways to reach a cell (i, j)?

Number of ways to reach (i, j) = ways to reach (i-1, j) + ways to reach (i, j-1), because you can come from the top or left cell only.

beginner

What is the base case for the Unique Paths DP grid?

The first row and first column cells have only 1 way to reach because you can only move right or down respectively from the start.

beginner

Why is Unique Paths problem a good example to learn DP?

It clearly shows how to break a problem into smaller overlapping subproblems and use memoization or tabulation to optimize.

In Unique Paths, which moves are allowed to reach the destination?

A Only right and down

B Only left and up

C Right, down, left, and up

D Only diagonal moves

The problem restricts moves to only right and down to simplify path counting.

What is the number of unique paths in a 1x1 grid?

A 0

B 1

C 2

D Undefined

Starting and ending at the same cell means there is exactly 1 path.

How do you initialize the first row in the DP table for Unique Paths?

A All ones

B All zeros

C Increasing numbers from 1

D Random values

Only one way to move right along the first row, so all cells are 1.

If dp[i][j] = dp[i-1][j] + dp[i][j-1], what does dp[i][j] represent?

A Number of moves made

B Number of obstacles

C Distance from start

D Number of unique paths to cell (i, j)

dp[i][j] stores the count of unique paths to reach cell (i, j).

What is the time complexity of the DP solution for Unique Paths in an m x n grid?

A O(m + n)

B O(1)

C O(m * n)

D O(m^n)

We fill an m by n table once, so time complexity is O(m * n).

Explain how dynamic programming helps solve the Unique Paths problem efficiently.

Think about how you count paths to each cell using already known counts.

Describe the steps to build the DP table for Unique Paths in a grid.

Start with edges, then fill inside cells using neighbors.

Practice

1. What does the problem "Unique Paths in Grid DP" ask you to find?
Unique Paths means:

easy

A. The number of ways to move from the top-left to bottom-right corner of a grid using only right and down moves

B. The shortest path length from top-left to bottom-right in a grid

C. The maximum sum path in a grid from top-left to bottom-right

D. The number of obstacles in the grid

Solution

1. Step 1: Understand the problem goal

   The problem asks for counting all possible paths from the top-left corner to the bottom-right corner of a grid.

2. Step 2: Identify allowed moves

   Only moves to the right or down are allowed, so paths are sequences of these moves.

3. Final Answer:

   The number of ways to move from the top-left to bottom-right corner of a grid using only right and down moves -> Option A

4. Quick Check:

   Unique Paths = Count of right/down move sequences [OK]

Hint: Count paths with only right and down moves [OK]

Common Mistakes:

• Thinking about shortest path length instead of count
• Including diagonal or left moves
• Confusing with maximum sum path

2. Which of the following Rust code snippets correctly initializes a 2D vector dp of size m x n with all values set to 1?

easy

A. let dp = vec![vec![0; m]; n];

B. let dp = vec![vec![1; m]; n];

C. let dp = vec![vec![0; n]; m];

D. let dp = vec![vec![1; n]; m];

Solution

1. Step 1: Understand vector dimensions

   We want a vector with m rows and n columns, so outer vector length is m, inner vector length is n.

2. Step 2: Check initialization values

   All values should be 1, so inner vector is vec![1; n], repeated m times.

3. Final Answer:

   let dp = vec![vec![1; n]; m]; -> Option D

4. Quick Check:

   Outer length = m, inner length = n, all 1s [OK]

Hint: Outer vector size = rows (m), inner vector size = cols (n) [OK]

Common Mistakes:

• Swapping m and n in vector sizes
• Initializing with zeros instead of ones
• Using incorrect vector syntax

3. What is the output of the following Rust code for m = 3 and n = 3?

fn unique_paths(m: usize, n: usize) -> usize {
    let mut dp = vec![vec![1; n]; m];
    for i in 1..m {
        for j in 1..n {
            dp[i][j] = dp[i-1][j] + dp[i][j-1];
        }
    }
    dp[m-1][n-1]
}

fn main() {
    println!("{}", unique_paths(3, 3));
}

medium

A. 9

B. 6

C. 3

D. 1

Solution

1. Step 1: Initialize dp with 1s

   dp is a 3x3 grid with all 1s initially.

2. Step 2: Fill dp using formula dp[i][j] = dp[i-1][j] + dp[i][j-1]

   Calculate dp values row by row: - dp[1][1] = dp[0][1] + dp[1][0] = 1 + 1 = 2 - dp[1][2] = dp[0][2] + dp[1][1] = 1 + 2 = 3 - dp[2][1] = dp[1][1] + dp[2][0] = 2 + 1 = 3 - dp[2][2] = dp[1][2] + dp[2][1] = 3 + 3 = 6

3. Final Answer:

   6 -> Option B

4. Quick Check:

   Unique paths in 3x3 grid = 6 [OK]

Hint: Use dp formula and fill row-wise [OK]

Common Mistakes:

• Not initializing first row and column with 1
• Adding dp[i][j-1] and dp[i-1][j] incorrectly
• Returning wrong dp cell

4. Identify the bug in this Rust function for unique paths and select the fix:

fn unique_paths(m: usize, n: usize) -> usize {
    let mut dp = vec![vec![0; n]; m];
    for i in 0..m {
        for j in 0..n {
            if i == 0 || j == 0 {
                dp[i][j] = 1;
            } else {
                dp[i][j] = dp[i-1][j] + dp[i][j-1];
            }
        }
    }
    dp[m][n]
}

medium

A. Change return to dp[m-1][n-1]

B. Initialize dp with ones instead of zeros

C. Change loops to start from 1 instead of 0

D. Remove the if condition inside loops

Solution

1. Step 1: Check dp indexing at return

   dp indices go from 0 to m-1 and 0 to n-1, so dp[m][n] is out of bounds and causes error.

2. Step 2: Fix return statement

   Return dp[m-1][n-1] to get bottom-right cell value.

3. Final Answer:

   Change return to dp[m-1][n-1] -> Option A

4. Quick Check:

   Return dp[m-1][n-1] to avoid index error [OK]

Hint: Return dp[m-1][n-1], not dp[m][n] [OK]

Common Mistakes:

• Returning dp[m][n] causing runtime panic
• Not initializing first row and column
• Starting loops incorrectly

5. Given a grid with obstacles represented by 1s and empty spaces by 0s, how would you modify the Unique Paths DP solution to count paths avoiding obstacles?
Choose the correct approach:

hard

A. Use recursion without memoization to skip obstacles

B. Ignore obstacles and use normal unique paths formula; obstacles don't affect path count

C. Initialize dp with zeros; set dp[0][0] = 1 if no obstacle; for each cell, if obstacle, dp[i][j] = 0 else dp[i][j] = dp[i-1][j] + dp[i][j-1]

D. Initialize dp with ones everywhere; subtract paths passing through obstacles at the end

Solution

1. Step 1: Initialize dp considering obstacles

   Set dp cells to 0 if obstacle present; dp[0][0] = 1 only if start is free.

2. Step 2: Update dp values skipping obstacles

   For each cell, if obstacle, dp[i][j] = 0; else dp[i][j] = dp[i-1][j] + dp[i][j-1]. This counts only valid paths.

3. Final Answer:

   Initialize dp with zeros; set dp[0][0] = 1 if no obstacle; for each cell, if obstacle, dp[i][j] = 0 else dp[i][j] = dp[i-1][j] + dp[i][j-1] -> Option C

4. Quick Check:

   Obstacles block paths by setting dp cells to zero [OK]

Hint: Set dp[i][j] = 0 for obstacles, sum neighbors otherwise [OK]

Common Mistakes:

• Ignoring obstacles in dp calculation
• Subtracting paths after counting all
• Using recursion without memoization causing inefficiency