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Dynamic Programming

The pattern: Break a problem into overlapping subproblems, solve each once, store the result, and reuse it. DP = recursion + memoization (top-down) or iterative table-filling (bottom-up).

Why this matters in interviews: DP is the most-feared interview topic, but it follows predictable patterns. Once you identify the “state” and “transition,” the code writes itself. ~25% of hard-level problems use DP.

When to Recognize It

How It Works

Think of climbing stairs. To reach step 5, you came from step 4 or step 3. To reach step 4, you came from step 3 or step 2. Notice step 3 gets computed twice? DP stores that answer so you only compute it once.

flowchart TD
    A["dp[5] = ?"]:::client
    B["dp[4]"]:::service
    C["dp[3]"]:::service
    D["dp[3]"]:::data
    E["dp[2]"]:::data
    F["dp[2]"]:::data
    G["dp[1]"]:::data

    A --> B
    A --> C
    B --> D
    B --> E
    C --> F
    C --> G

    classDef client fill:#4c3a5e,stroke:#818cf8,color:#e2e8f0
    classDef service fill:#1a3a2a,stroke:#4ade80,color:#e2e8f0
    classDef data fill:#3b3520,stroke:#fbbf24,color:#e2e8f0

The DP framework (4 steps):

  1. Define the state: What does dp[i] (or dp[i][j]) represent?
  2. Find the transition: How does dp[i] relate to smaller states?
  3. Set base cases: What’s the answer for the smallest inputs?
  4. Determine the order: Fill the table so dependencies are computed first.

Template Code

Code

# 1D DP: Climbing Stairs (number of ways to reach step n)
def climb_stairs(n):
    if n <= 2:
        return n
    dp = [0] * (n + 1)
    dp[1], dp[2] = 1, 2

    for i in range(3, n + 1):
        dp[i] = dp[i-1] + dp[i-2]  # transition

    return dp[n]

# Space-optimized (only need last 2 values)
def climb_stairs_opt(n):
    if n <= 2:
        return n
    prev2, prev1 = 1, 2
    for i in range(3, n + 1):
        curr = prev1 + prev2
        prev2, prev1 = prev1, curr
    return prev1
// 1D DP: Climbing Stairs
int climbStairs(int n) {
    if (n <= 2) return n;
    int[] dp = new int[n + 1];
    dp[1] = 1; dp[2] = 2;
    for (int i = 3; i <= n; i++) {
        dp[i] = dp[i-1] + dp[i-2];
    }
    return dp[n];
}

// Space-optimized
int climbStairsOpt(int n) {
    if (n <= 2) return n;
    int prev2 = 1, prev1 = 2;
    for (int i = 3; i <= n; i++) {
        int curr = prev1 + prev2;
        prev2 = prev1;
        prev1 = curr;
    }
    return prev1;
}
// 1D DP: Climbing Stairs
int climbStairs(int n) {
    if (n <= 2) return n;
    vector<int> dp(n + 1);
    dp[1] = 1; dp[2] = 2;
    for (int i = 3; i <= n; i++) {
        dp[i] = dp[i-1] + dp[i-2];
    }
    return dp[n];
}
// 1D DP: Climbing Stairs
function climbStairs(n) {
    if (n <= 2) return n;
    const dp = new Array(n + 1).fill(0);
    dp[1] = 1; dp[2] = 2;
    for (let i = 3; i <= n; i++) {
        dp[i] = dp[i-1] + dp[i-2];
    }
    return dp[n];
}

Variations

2D DP: Longest Common Subsequence (LCS)

dp[i][j] = LCS of text1[0..i-1] and text2[0..j-1].

Code

def longest_common_subsequence(text1, text2):
    m, n = len(text1), len(text2)
    dp = [[0] * (n + 1) for _ in range(m + 1)]

    for i in range(1, m + 1):
        for j in range(1, n + 1):
            if text1[i-1] == text2[j-1]:
                dp[i][j] = dp[i-1][j-1] + 1
            else:
                dp[i][j] = max(dp[i-1][j], dp[i][j-1])

    return dp[m][n]
int longestCommonSubsequence(String text1, String text2) {
    int m = text1.length(), n = text2.length();
    int[][] dp = new int[m + 1][n + 1];
    for (int i = 1; i <= m; i++) {
        for (int j = 1; j <= n; j++) {
            if (text1.charAt(i-1) == text2.charAt(j-1))
                dp[i][j] = dp[i-1][j-1] + 1;
            else
                dp[i][j] = Math.max(dp[i-1][j], dp[i][j-1]);
        }
    }
    return dp[m][n];
}
int longestCommonSubsequence(string text1, string text2) {
    int m = text1.size(), n = text2.size();
    vector<vector<int>> dp(m + 1, vector<int>(n + 1, 0));
    for (int i = 1; i <= m; i++) {
        for (int j = 1; j <= n; j++) {
            if (text1[i-1] == text2[j-1])
                dp[i][j] = dp[i-1][j-1] + 1;
            else
                dp[i][j] = max(dp[i-1][j], dp[i][j-1]);
        }
    }
    return dp[m][n];
}
function longestCommonSubsequence(text1, text2) {
    const m = text1.length, n = text2.length;
    const dp = Array.from({length: m + 1}, () => Array(n + 1).fill(0));
    for (let i = 1; i <= m; i++) {
        for (let j = 1; j <= n; j++) {
            if (text1[i-1] === text2[j-1])
                dp[i][j] = dp[i-1][j-1] + 1;
            else
                dp[i][j] = Math.max(dp[i-1][j], dp[i][j-1]);
        }
    }
    return dp[m][n];
}

0/1 Knapsack

dp[i][w] = max value using first i items with weight capacity w. For each item: take it (dp[i-1][w - weight[i]] + value[i]) or skip it (dp[i-1][w]).

Longest Increasing Subsequence (LIS)

dp[i] = length of LIS ending at index i. For each j < i where nums[j] < nums[i]: dp[i] = max(dp[i], dp[j] + 1).

Optimization: Use patience sorting with binary search for O(n log n).

Complexity

Pattern Time Space
1D DP (climbing stairs, house robber) O(n) O(n) or O(1)
2D DP (LCS, grid paths) O(m * n) O(m * n) or O(n)
0/1 Knapsack O(n * W) O(n * W) or O(W)
LIS (DP) O(n²) O(n)
LIS (binary search) O(n log n) O(n)

Common Mistakes

Practice Problems

Key Takeaways