Longest Common Substring in DSA Rust - Time & Space Complexity

Time Complexity: Longest Common Substring

O(n * m)

Understanding Time Complexity

We want to understand how the time needed to find the longest common substring grows as the input strings get longer.

How does the work increase when the strings become bigger?

Scenario Under Consideration

Analyze the time complexity of the following code snippet.

fn longest_common_substring(s1: &str, s2: &str) -> usize {
    let bytes1 = s1.as_bytes();
    let bytes2 = s2.as_bytes();
    let mut max_len = 0;
    let mut dp = vec![vec![0; bytes2.len() + 1]; bytes1.len() + 1];

    for i in 1..=bytes1.len() {
        for j in 1..=bytes2.len() {
            if bytes1[i - 1] == bytes2[j - 1] {
                dp[i][j] = dp[i - 1][j - 1] + 1;
                if dp[i][j] > max_len {
                    max_len = dp[i][j];
                }
            }
        }
    }
    max_len
}
    

This code finds the length of the longest substring that appears in both input strings by checking all pairs of characters and building up a table of matching lengths.

Identify Repeating Operations

Identify the loops, recursion, array traversals that repeat.

• Primary operation: Nested loops comparing characters of both strings.
• How many times: Outer loop runs once for each character in the first string (n times), inner loop runs once for each character in the second string (m times).

How Execution Grows With Input

For each character in the first string, the code compares it with every character in the second string, so the work grows with the product of their lengths.

Input Size (n = length of s1, m = length of s2)	Approx. Operations
10 and 10	100 comparisons
100 and 100	10,000 comparisons
1000 and 1000	1,000,000 comparisons

Pattern observation: The number of operations grows roughly by the product of the input sizes.

Final Time Complexity

Time Complexity: O(n * m)

This means the time needed grows proportionally to the product of the lengths of the two strings.

Common Mistake

[X] Wrong: "The algorithm only looks at each string once, so it must be O(n + m)."

[OK] Correct: Because it compares every character of one string with every character of the other, the work multiplies, not adds.

Interview Connect

Understanding this time complexity helps you explain how dynamic programming solves problems by breaking them into smaller parts and why nested loops increase work quickly.

Self-Check

"What if we only compared substrings starting at the same index in both strings? How would the time complexity change?"

Practice

1. What does the Longest Common Substring problem find between two strings?

easy

A. The longest sequence of characters that appears in both strings consecutively

B. The longest sequence of characters that appears in both strings but not necessarily consecutively

C. The total number of common characters between two strings

D. The longest string formed by combining both strings

Solution

1. Step 1: Understand the problem definition

   The Longest Common Substring problem looks for the longest continuous sequence of characters that appears in both strings without interruption.

2. Step 2: Differentiate from similar problems

   Unlike Longest Common Subsequence, which allows gaps, Longest Common Substring requires characters to be consecutive.

3. Final Answer:

   The longest sequence of characters that appears in both strings consecutively -> Option A

4. Quick Check:

   Longest Common Substring = consecutive matching characters [OK]

Hint: Look for continuous matching characters in both strings [OK]

Common Mistakes:

• Confusing substring with subsequence
• Thinking characters can be non-consecutive
• Counting total common characters instead of longest sequence

2. Which Rust code snippet correctly initializes a 2D vector for dynamic programming in Longest Common Substring?

easy

A. let mut dp = vec![vec![0; n + 1]; m + 1];

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

C. let mut dp = vec![vec![0; m + 1]; n];

D. let dp = vec![vec![0; n]; m + 1];

Solution

1. Step 1: Understand dimensions for DP table

   For strings of length m and n, dp table size should be (m+1) x (n+1) to include empty prefixes.

2. Step 2: Check Rust vector initialization syntax

   vec![vec![0; n + 1]; m + 1] creates m+1 rows each with n+1 zeros, matching DP table size.

3. Final Answer:

   let mut dp = vec![vec![0; n + 1]; m + 1]; -> Option A

4. Quick Check:

   DP table size = (m+1) x (n+1) [OK]

Hint: DP table size = (length+1) for both strings [OK]

Common Mistakes:

• Swapping m and n in vector sizes
• Forgetting +1 for empty string row/column
• Using immutable vector when mutability is needed

3. What is the output of this Rust code snippet for strings s1 = "abcde" and s2 = "abfde"?

let s1 = "abcde";
let s2 = "abfde";
let m = s1.len();
let n = s2.len();
let mut dp = vec![vec![0; n + 1]; m + 1];
let mut max_len = 0;
for i in 1..=m {
    for j in 1..=n {
        if s1.as_bytes()[i - 1] == s2.as_bytes()[j - 1] {
            dp[i][j] = dp[i - 1][j - 1] + 1;
            if dp[i][j] > max_len {
                max_len = dp[i][j];
            }
        }
    }
}
println!("{}", max_len);

medium

A. 3

B. 4

C. 2

D. 1

Solution

1. Step 1: Compare characters and fill DP table

   Matching characters are 'a' and 'b' at start, then 'd' and 'e' at the end. The longest continuous match is "ab" with length 2.

2. Step 2: Track max_len during iteration

   max_len updates to 1 for 'a', then 2 for 'b', resets for mismatch, then 1 for 'd' and 1 for 'e'. So max_len = 2.

3. Final Answer:

   2 -> Option C

4. Quick Check:

   Longest common substring length = 2 [OK]

Hint: Check consecutive matches, longest is "ab" length 2 [OK]

Common Mistakes:

• Counting non-consecutive matches
• Confusing substring length with subsequence length
• Ignoring reset of dp on mismatch

4. Identify the bug in this Rust code snippet for Longest Common Substring:

let s1 = "hello";
let s2 = "yellow";
let m = s1.len();
let n = s2.len();
let mut dp = vec![vec![0; n]; m];
let mut max_len = 0;
for i in 1..=m {
    for j in 1..=n {
        if s1.as_bytes()[i - 1] == s2.as_bytes()[j - 1] {
            dp[i][j] = dp[i - 1][j - 1] + 1;
            if dp[i][j] > max_len {
                max_len = dp[i][j];
            }
        }
    }
}
println!("{}", max_len);

medium

A. Loop ranges should start from 0, not 1

B. DP vector size is incorrect; should be (m+1) x (n+1)

C. Comparison should use chars(), not as_bytes()

D. max_len should be initialized to 1, not 0

Solution

1. Step 1: Check DP vector initialization

   DP vector is initialized with size m x n, but loops access dp[i][j] with i and j starting at 1, causing out-of-bounds error.

2. Step 2: Correct DP vector size

   DP vector should be sized (m+1) x (n+1) to safely access dp[i][j] for i and j in 1..=m and 1..=n.

3. Final Answer:

   DP vector size is incorrect; should be (m+1) x (n+1) -> Option B

4. Quick Check:

   DP size must match loop indices [OK]

Hint: DP size = (length+1) to avoid index errors [OK]

Common Mistakes:

• Using wrong DP vector size causing panic
• Starting loops at 0 but indexing with i-1
• Confusing bytes and chars for comparison

5. Given two strings s1 = "ababc" and s2 = "babca", write a Rust function to find the longest common substring and return it. What is the correct longest common substring?

hard

A. "bab"

B. "babc"

C. "abca"

D. "abc"

Solution

1. Step 1: Use DP to find longest common substring length and end index

   By comparing characters and updating dp, the longest common substring length is 3, ending at index 4 in s1.

2. Step 2: Extract substring from s1 using end index and length

   Substring of length 3 ending at index 4 in s1 is "abc".

3. Final Answer:

   "abc" -> Option D

4. Quick Check:

   Longest common substring = "abc" length 3 [OK]

Hint: Track max length and end index, then slice substring [OK]

Common Mistakes:

• Choosing substring from wrong string
• Confusing substring with subsequence
• Not updating end index when max length changes