LeetCode 139: Word Break

LeetCode 139: Word Break

• • [1. 📌 Problem Links](#1. 📌 Problem Links)
  • [2. 🧠 Solution Overview](#2. 🧠 Solution Overview)
  • [3. 🟢 Solution 1: Dynamic Programming (Bottom-Up)](#3. 🟢 Solution 1: Dynamic Programming (Bottom-Up))
  • • [3.1. Algorithm Idea](#3.1. Algorithm Idea)
    • [3.2. Key Points](#3.2. Key Points)
    • [3.3. Java Implementation](#3.3. Java Implementation)
    • [3.4. Complexity Analysis](#3.4. Complexity Analysis)
  • [4. 🟡 Solution 2: Optimized DP with Length Pruning](#4. 🟡 Solution 2: Optimized DP with Length Pruning)
  • • [4.1. Algorithm Idea](#4.1. Algorithm Idea)
    • [4.2. Key Points](#4.2. Key Points)
    • [4.3. Java Implementation](#4.3. Java Implementation)
    • [4.4. Complexity Analysis](#4.4. Complexity Analysis)
  • [5. 🔵 Solution 3: BFS with Visited Tracking](#5. 🔵 Solution 3: BFS with Visited Tracking)
  • • [5.1. Algorithm Idea](#5.1. Algorithm Idea)
    • [5.2. Key Points](#5.2. Key Points)
    • [5.3. Java Implementation](#5.3. Java Implementation)
    • [5.4. Complexity Analysis](#5.4. Complexity Analysis)
  • [6. 📊 Solution Comparison](#6. 📊 Solution Comparison)
  • [7. 💡 Summary](#7. 💡 Summary)

1. 📌 Problem Links

• LeetCode 139 : Word Break

2. 🧠 Solution Overview

This problem requires determining if a string s can be segmented into a space-separated sequence of one or more dictionary words from wordDict. The same word may be reused multiple times. Below are the main approaches:

Method	Key Idea	Time Complexity	Space Complexity
Dynamic Programming	DP array storing segmentability for each position	O(n²)	O(n)
BFS with Pruning	Treat as graph traversal with visited optimization	O(n²)	O(n)
Optimized DP with Length Pruning	DP with max word length optimization	O(n×L)	O(n)

3. 🟢 Solution 1: Dynamic Programming (Bottom-Up)

3.1. Algorithm Idea

We use a DP array where dp[i] represents whether the substring s[0...i-1] (the first i characters) can be segmented into dictionary words. The key insight is that if we can segment the substring ending at position j, and the substring from j to i is in the dictionary, then we can also segment the substring ending at position i.

3.2. Key Points

• State Definition : dp[i] = whether first i characters can be segmented
• State Transition : dp[i] = true if there exists j where dp[j] == true and s[j...i-1] is in wordDict
• Initialization :
  • dp[0] = true (empty string can always be segmented)
  • All other dp[i] initialized to false
• Processing Order: Process positions from 1 to n sequentially

3.3. Java Implementation

class Solution {
    public boolean wordBreak(String s, List<String> wordDict) {
        if (s == null || s.length() == 0) return false;
        
        Set<String> dict = new HashSet<>(wordDict);
        int n = s.length();
        boolean[] dp = new boolean[n + 1];
        dp[0] = true; // Base case: empty string
        
        for (int i = 1; i <= n; i++) {
            for (int j = 0; j < i; j++) {
                // If prefix [0,j) is segmentable and substring [j,i) is in dictionary
                if (dp[j] && dict.contains(s.substring(j, i))) {
                    dp[i] = true;
                    break; // No need to check other j's for this i
                }
            }
        }
        
        return dp[n];
    }
}

3.4. Complexity Analysis

• Time Complexity : O(n²) - Two nested loops over string length
• Space Complexity : O(n) - For DP array and dictionary set

4. 🟡 Solution 2: Optimized DP with Length Pruning

4.1. Algorithm Idea

This approach optimizes the standard DP solution by using the maximum word length from the dictionary to limit the inner loop range. This avoids unnecessary checks when the potential word length exceeds the maximum available dictionary word length.

4.2. Key Points

• Length Pruning : Calculate maxLen - the longest word in dictionary
• Optimized Inner Loop : Only check substrings with length ≤ maxLen
• Reverse Iteration: Check from the end of potential words for better performance

4.3. Java Implementation

class Solution {
    public boolean wordBreak(String s, List<String> wordDict) {
        if (s == null || s.length() == 0) return false;
        
        Set<String> dict = new HashSet<>(wordDict);
        int n = s.length();
        
        // Calculate maximum word length for pruning
        int maxLen = 0;
        for (String word : dict) {
            maxLen = Math.max(maxLen, word.length());
        }
        
        boolean[] dp = new boolean[n + 1];
        dp[0] = true;
        
        for (int i = 1; i <= n; i++) {
            // Only check back up to maxLen characters
            int start = Math.max(0, i - maxLen);
            for (int j = i - 1; j >= start; j--) {
                if (dp[j] && dict.contains(s.substring(j, i))) {
                    dp[i] = true;
                    break;
                }
            }
        }
        
        return dp[n];
    }
}

4.4. Complexity Analysis

• Time Complexity : O(n×L) - Where L is maxLen, typically much smaller than n
• Space Complexity : O(n) - Same as standard DP

5. 🔵 Solution 3: BFS with Visited Tracking

5.1. Algorithm Idea

We can model this as a graph search problem where each position represents a node, and we traverse from each position to all reachable positions using dictionary words. BFS naturally finds the shortest path to the end, and we use a visited array to avoid reprocessing the same states.

5.2. Key Points

• State Representation: Current position in the string
• Graph Traversal: From each position, try all possible dictionary words
• Visited Tracking: Avoid revisiting the same positions
• Termination: Return true when we reach the end of the string

5.3. Java Implementation

class Solution {
    public boolean wordBreak(String s, List<String> wordDict) {
        if (s == null || s.length() == 0) return false;
        
        Set<String> dict = new HashSet<>(wordDict);
        int n = s.length();
        
        // Calculate maximum word length for pruning
        int maxLen = 0;
        for (String word : dict) {
            maxLen = Math.max(maxLen, word.length());
        }
        
        Queue<Integer> queue = new LinkedList<>();
        boolean[] visited = new boolean[n + 1];
        queue.offer(0);
        visited[0] = true;
        
        while (!queue.isEmpty()) {
            int start = queue.poll();
            
            // Try all possible end positions
            for (int end = start + 1; end <= n && end - start <= maxLen; end++) {
                if (!visited[end] && dict.contains(s.substring(start, end))) {
                    if (end == n) {
                        return true; // Reached the end
                    }
                    queue.offer(end);
                    visited[end] = true;
                }
            }
        }
        
        return false;
    }
}

5.4. Complexity Analysis

• Time Complexity : O(n²) - Each position processed once, with up to n checks
• Space Complexity : O(n) - For queue and visited array

6. 📊 Solution Comparison

Solution	Time	Space	Pros	Cons
Standard DP	O(n²)	O(n)	Most intuitive, guaranteed optimal	Slower for long strings
Optimized DP	O(n×L)	O(n)	Much faster with length pruning	Slightly more complex
BFS Approach	O(n²)	O(n)	Natural graph interpretation	May explore unnecessary states

7. 💡 Summary

For the Word Break problem:

• Learning & Understanding : Start with Standard DP to grasp the fundamental state transition concept
• Interviews & Practical Use : Optimized DP with Length Pruning offers the best performance for most scenarios
• Alternative Perspective : BFS Approach provides a different intuition about the problem as a reachability graph

The key insight is recognizing the optimal substructure - the segmentability of a string depends on the segmentability of its prefixes and the presence of the remaining suffix in the dictionary.

Just as words give meaning to random letters, the Word Break problem teaches us that complex challenges can be solved by breaking them into smaller, recognizable patterns and systematically combining their solutions.