LeetCode 198: House Robber

LeetCode 198: House Robber

• • [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: Space-Optimized Dynamic Programming](#4. 🟡 Solution 2: Space-Optimized Dynamic Programming)
  • • [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: Recursive Approach with Memoization](#5. 🔵 Solution 3: Recursive Approach with Memoization)
  • • [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 198: House Robber

2. 🧠 Solution Overview

This problem requires finding the maximum amount of money you can rob from houses arranged in a straight line without alerting the police. The constraint is that you cannot rob two adjacent houses. Below are the main approaches:

Method	Key Idea	Time Complexity	Space Complexity
Dynamic Programming	DP array storing max profit at each house	O(n)	O(n)
Space-Optimized DP	Two variables tracking previous states	O(n)	O(1)
Recursive with Memoization	Top-down approach with caching	O(n)	O(n)

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

3.1. Algorithm Idea

We use a DP array where dp[i] represents the maximum amount that can be robbed from the first i+1 houses. The key insight is that at each house i, we have two choices: either rob this house and add its value to the maximum amount from houses up to i-2, or skip this house and take the maximum amount from houses up to i-1.

3.2. Key Points

• State Definition : dp[i] = maximum amount robbable from first i+1 houses
• State Transition :
  • If we rob house i: dp[i] = dp[i-2] + nums[i]
  • If we skip house i: dp[i] = dp[i-1]
  • Final: dp[i] = max(dp[i-1], dp[i-2] + nums[i])
• Initialization :
  • dp[0] = nums[0] (only one house)
  • dp[1] = max(nums[0], nums[1]) (two houses)
• Processing Order: Left to right, ensuring subproblems are solved first

3.3. Java Implementation

class Solution {
    public int rob(int[] nums) {
        if (nums == null || nums.length == 0) {
            return 0;
        }
        if (nums.length == 1) {
            return nums[0];
        }
        
        int n = nums.length;
        int[] dp = new int[n];
        dp[0] = nums[0];
        dp[1] = Math.max(nums[0], nums[1]);
        
        for (int i = 2; i < n; i++) {
            dp[i] = Math.max(dp[i - 1], dp[i - 2] + nums[i]);
        }
        
        return dp[n - 1];
    }
}

3.4. Complexity Analysis

• Time Complexity : O(n) - Single pass through all houses
• Space Complexity : O(n) - DP array of size n

4. 🟡 Solution 2: Space-Optimized Dynamic Programming

4.1. Algorithm Idea

We can optimize space by noticing that only the previous two states (i-1 and i-2) are needed to compute the current state i. Instead of storing the entire DP array, we maintain only two variables that represent these states and update them iteratively.

4.2. Key Points

• Variable Tracking :
  • prev1 tracks maximum up to previous house (i-1)
  • prev2 tracks maximum up to two houses before (i-2)
• State Update: At each iteration, calculate current maximum and shift variables
• Edge Cases: Handle empty array, single house, and two houses separately

4.3. Java Implementation

class Solution {
    public int rob(int[] nums) {
        if (nums == null || nums.length == 0) {
            return 0;
        }
        if (nums.length == 1) {
            return nums[0];
        }
        
        int prev2 = 0; // Represents dp[i-2]
        int prev1 = 0; // Represents dp[i-1]
        
        for (int num : nums) {
            int current = Math.max(prev1, prev2 + num);
            prev2 = prev1;
            prev1 = current;
        }
        
        return prev1;
    }
}

4.4. Complexity Analysis

• Time Complexity : O(n) - Same as standard DP
• Space Complexity : O(1) - Only two variables used

5. 🔵 Solution 3: Recursive Approach with Memoization

5.1. Algorithm Idea

This approach solves the problem recursively from the top (end of the street) down to the beginning, caching results to avoid redundant calculations. For each house, we explore both possibilities (rob or skip) and return the maximum.

5.2. Key Points

• Recursive Relation : rob(i) = max(rob(i-1), rob(i-2) + nums[i])
• Base Cases :
  • i < 0: return 0 (no houses)
  • i == 0: return nums[0] (only one house)
• Memoization: Store computed results to avoid exponential time complexity

5.3. Java Implementation

class Solution {
    public int rob(int[] nums) {
        if (nums == null || nums.length == 0) {
            return 0;
        }
        Integer[] memo = new Integer[nums.length];
        return robHelper(nums, nums.length - 1, memo);
    }
    
    private int robHelper(int[] nums, int i, Integer[] memo) {
        if (i < 0) {
            return 0;
        }
        if (memo[i] != null) {
            return memo[i];
        }
        
        if (i == 0) {
            memo[i] = nums[0];
        } else {
            int robCurrent = nums[i] + robHelper(nums, i - 2, memo);
            int skipCurrent = robHelper(nums, i - 1, memo);
            memo[i] = Math.max(robCurrent, skipCurrent);
        }
        
        return memo[i];
    }
}

5.4. Complexity Analysis

• Time Complexity : O(n) - Each subproblem solved once
• Space Complexity : O(n) - For recursion stack and memoization array

6. 📊 Solution Comparison

Solution	Time	Space	Pros	Cons
Standard DP	O(n)	O(n)	Most intuitive, easy to understand	Higher memory usage
Space Optimized	O(n)	O(1)	Optimal space, efficient	Slightly less intuitive
Recursive	O(n)	O(n)	Natural problem expression	Recursion overhead

7. 💡 Summary

For the House Robber problem:

• Standard DP is recommended for learning and understanding the fundamental pattern
• Space-optimized DP is best for interviews and production use with optimal performance
• Recursive approach helps understand the problem's mathematical structure

The key insight is recognizing the optimal substructure - the solution at each step depends only on the solutions to the two previous subproblems.

In life as in dynamic programming, our current decisions are shaped by our past choices, and the optimal path forward often requires balancing immediate gains with long-term consequences.