Longest Common Substring in DSA Go - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the Longest Common Substring problem?

It is the problem of finding the longest sequence of characters that appears in the same order and contiguously in two given strings.

intermediate

How does the dynamic programming approach help solve the Longest Common Substring problem?

It builds a table where each cell represents the length of the longest common suffix of substrings ending at those positions, allowing us to find the maximum length efficiently.

beginner

In the DP table for Longest Common Substring, what does a zero value in a cell mean?

It means the characters at those positions in the two strings do not match, so no common substring ends there.

intermediate

What is the time complexity of the dynamic programming solution for Longest Common Substring?

The time complexity is O(m*n), where m and n are the lengths of the two input strings.

beginner

Why is Longest Common Substring different from Longest Common Subsequence?

Longest Common Substring requires the characters to be contiguous in both strings, while Longest Common Subsequence allows characters to be non-contiguous but in order.

What does each cell in the DP table for Longest Common Substring represent?

A Length of longest common prefix starting at those positions

B Length of longest common suffix ending at those positions

C Total number of matching characters so far

D Length of longest common subsequence so far

Each cell stores the length of the longest common suffix of substrings ending at those positions in the two strings.

If characters at positions i and j in the two strings do not match, what is the DP cell value?

A One

B Previous cell value plus one

C Zero

D Maximum of neighboring cells

No common substring ends at mismatched characters, so the cell value is zero.

What is the main difference between Longest Common Substring and Longest Common Subsequence?

A Substring requires contiguous characters; subsequence does not

B Subsequence requires contiguous characters; substring does not

C Both require contiguous characters

D Both allow non-contiguous characters

Longest Common Substring requires characters to be contiguous in both strings, unlike subsequence.

What is the space complexity of the basic DP solution for Longest Common Substring?

A O(m+n)

B O(1)

C O(max(m,n))

D O(m*n)

The DP table requires space proportional to the product of the lengths of the two strings.

Which of these is a correct step in the DP approach for Longest Common Substring?

A If characters match, add 1 to the diagonal cell value

B If characters match, add 1 to the left cell value

C If characters match, add 1 to the top cell value

D If characters match, reset cell to zero

When characters match, the cell value is 1 plus the value from the diagonal cell representing previous suffix length.

Explain how to use dynamic programming to find the Longest Common Substring between two strings.

Think about building a table that tracks common suffix lengths.

Describe the difference between Longest Common Substring and Longest Common Subsequence with examples.

Focus on whether characters must be next to each other.

Practice

1. What does the Longest Common Substring problem find between two strings s1 and s2?

easy

A. The total count of common characters between the two strings

B. The longest sequence of characters that appear consecutively in both strings

C. The longest sequence of characters that appear in any order in both strings

D. The longest sequence of characters that appear only in the first string

Solution

1. Step 1: Understand the problem definition

   The Longest Common Substring problem looks for the longest sequence of characters that appear consecutively in both strings.

2. Step 2: Differentiate from similar problems

   Unlike subsequence problems, substring requires characters to be continuous and in the same order in both strings.

3. Final Answer:

   The longest sequence of characters that appear consecutively in both strings -> Option B

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 order does not matter
• Counting total common characters instead of longest sequence

2. Which of the following Go code snippets correctly initializes a 2D slice dp for Longest Common Substring of lengths m and n?

easy

A. dp := [][]int{{m+1}, {n+1}}

B. dp := make([]int, m+1)
for i := range dp {
dp[i] = make([]int, n+1)
}

C. var dp [m+1][n+1]int

D. dp := make([][]int, m+1)
for i := range dp {
dp[i] = make([]int, n+1)
}

Solution

1. Step 1: Understand Go slice initialization

   To create a 2D slice, first create the outer slice with length m+1, then initialize each inner slice with length n+1.

2. Step 2: Check each option

   dp := make([][]int, m+1) for i := range dp { dp[i] = make([]int, n+1) } correctly creates dp as [][]int with proper inner slices. dp := make([]int, m+1) for i := range dp { dp[i] = make([]int, n+1) } tries to assign inner slices to int slice, invalid. var dp [m+1][n+1]int uses array syntax but m,n are variables, so invalid. dp := [][]int{{m+1}, {n+1}} creates a slice with two elements, not a 2D slice.

3. Final Answer:

   dp := make([][]int, m+1) for i := range dp { dp[i] = make([]int, n+1) } -> Option D

4. Quick Check:

   Proper 2D slice initialization = dp := make([][]int, m+1) for i := range dp { dp[i] = make([]int, n+1) } [OK]

Hint: Use make for outer slice, then loop to make inner slices [OK]

Common Mistakes:

• Using make for inner slices on a 1D int slice
• Trying to declare arrays with variable sizes
• Incorrect slice literal syntax

3. What is the output of the following Go code that finds the length of the longest common substring between s1 = "abcde" and s2 = "abfde"?

func longestCommonSubstring(s1, s2 string) int {
  m, n := len(s1), len(s2)
  dp := make([][]int, m+1)
  for i := range dp {
    dp[i] = make([]int, n+1)
  }
  maxLen := 0
  for i := 1; i <= m; i++ {
    for j := 1; j <= n; j++ {
      if s1[i-1] == s2[j-1] {
        dp[i][j] = dp[i-1][j-1] + 1
        if dp[i][j] > maxLen {
          maxLen = dp[i][j]
        }
      }
    }
  }
  return maxLen
}

fmt.Println(longestCommonSubstring("abcde", "abfde"))

medium

A. 2

B. 3

C. 4

D. 5

Solution

1. Step 1: Trace matching characters

   Compare characters of "abcde" and "abfde": - 'a' == 'a' (dp[1][1] = 1) - 'b' == 'b' (dp[2][2] = 2) - 'c' != 'f' (reset) - 'd' == 'd' (dp[4][4] = 1) - 'e' == 'e' (dp[5][5] = 2)

2. Step 2: Find longest continuous match

   The longest continuous matches are "ab" (length 2) and "de" (length 2). No longer sequence exists. Thus, maxLen = 2.

3. Final Answer:

   2 -> Option A

4. Quick Check:

   Longest common substring length = 2 [OK]

Hint: Check character by character and track max continuous matches [OK]

Common Mistakes:

• Counting non-continuous matches
• Confusing substring length with subsequence length
• Off-by-one errors in indexing

4. The following Go code attempts to find the longest common substring length but has a bug. What is the bug?

func longestCommonSubstring(s1, s2 string) int {
  m, n := len(s1), len(s2)
  dp := make([][]int, m)
  for i := range dp {
    dp[i] = make([]int, n)
  }
  maxLen := 0
  for i := 0; i < m; i++ {
    for j := 0; j < n; j++ {
      if s1[i] == s2[j] {
        dp[i][j] = dp[i-1][j-1] + 1
        if dp[i][j] > maxLen {
          maxLen = dp[i][j]
        }
      }
    }
  }
  return maxLen
}

medium

A. The condition should use '!=' instead of '=='

B. The loops should start from 1 instead of 0

C. dp slice dimensions should be m+1 and n+1 to avoid index errors

D. maxLen should be initialized to 1

Solution

1. Step 1: Analyze dp slice size

   The dp slice is created with size m and n, but the code accesses dp[i-1][j-1] which can cause index out of range when i or j is 0.

2. Step 2: Correct dp dimensions

   To safely access dp[i-1][j-1], dp should be sized (m+1) x (n+1) and loops should run from 1 to m and n, avoiding negative indices.

3. Final Answer:

   dp slice dimensions should be m+1 and n+1 to avoid index errors -> Option C

4. Quick Check:

   dp size must allow safe dp[i-1][j-1] access [OK]

Hint: Use dp size m+1 and n+1 to avoid negative indices [OK]

Common Mistakes:

• Using dp size m and n causing index errors
• Starting loops at 0 causing negative index access
• Incorrect initialization of maxLen

5. Given two strings s1 = "ababc" and s2 = "babca", write a Go function to find the longest common substring and return the substring itself (not just length). What will be the returned substring?

hard

A. "babc"

B. "ababc"

C. "babc" or "abca" depending on implementation

D. "abca"

Solution

1. Step 1: Identify longest common substrings

   Check substrings common to both: - "ababc" vs "babca" share "babc" as longest common substring of length 4. - "abca" is not a substring of s1. - "babc" appears in both strings consecutively.

2. Step 2: Confirm substring returned

   The longest common substring is "babc" with length 4, so the function should return "babc".

3. Final Answer:

   "babc" -> Option A

4. Quick Check:

   Longest common substring = "babc" [OK]

Hint: Find longest continuous matching substring, return that string [OK]

Common Mistakes:

• Returning longest common subsequence instead
• Confusing substring with subsequence
• Returning multiple substrings instead of one