Edit Distance Problem Levenshtein in DSA C - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the Edit Distance (Levenshtein distance) between two strings?

It is the minimum number of single-character edits (insertions, deletions, or substitutions) required to change one string into the other.

beginner

Which operations are allowed in calculating the Levenshtein distance?

Insertion, deletion, and substitution of a single character.

intermediate

What is the base case when using dynamic programming for the Levenshtein distance?

When one string is empty, the distance is the length of the other string (all insertions or deletions).

intermediate

How does the dynamic programming table for Levenshtein distance get filled?

Each cell represents the minimum edit distance between prefixes of the two strings, calculated using previous results for insert, delete, and substitute operations.

intermediate

What is the time complexity of the standard dynamic programming solution for Levenshtein distance?

O(m * n), where m and n are the lengths of the two strings.

Which of the following is NOT an allowed operation in Levenshtein distance?

A Insertion

B Transposition

C Substitution

D Deletion

Levenshtein distance allows insertion, deletion, and substitution. Transposition is not allowed.

If one string is empty and the other has length 5, what is the edit distance?

A 0

B 1

C Cannot determine

D 5

The distance equals the length of the non-empty string because all characters must be inserted or deleted.

What does the cell dp[i][j] represent in the dynamic programming table for Levenshtein distance?

A Distance between prefixes of length i and j

B Number of substitutions only

C Distance between full strings

D Length of the longest common subsequence

dp[i][j] stores the minimum edit distance between the first i characters of string one and first j characters of string two.

What is the time complexity of the Levenshtein distance dynamic programming solution?

A O(m^2 + n^2)

B O(m + n)

C O(m * n)

D O(2^(m+n))

The solution fills a table of size m by n, so the time complexity is O(m * n).

Which of these best describes the Levenshtein distance?

A Minimum number of edits to convert one string to another

B Longest common subsequence length

C Number of matching characters

D Maximum number of substitutions

Levenshtein distance measures the minimum edits (insertions, deletions, substitutions) to convert one string into another.

Explain how to use dynamic programming to find the Levenshtein distance between two strings.

Think about comparing prefixes and building up solutions.

Describe the three operations allowed in the Edit Distance problem and how they affect the distance calculation.

Imagine changing one word into another step by step.