Climbing Stairs Problem in DSA Java - Time & Space Complexity

Time Complexity: Climbing Stairs Problem

O(n)

Understanding Time Complexity

We want to know how the time to solve the climbing stairs problem changes as the number of stairs grows.

Specifically, how many steps does the program take to find the total ways to climb?

Scenario Under Consideration

Analyze the time complexity of the following code snippet.

public int climbStairs(int n) {
    if (n <= 2) return n;
    int first = 1, second = 2;
    for (int i = 3; i <= n; i++) {
        int third = first + second;
        first = second;
        second = third;
    }
    return second;
}
    

This code calculates how many distinct ways there are to climb n stairs, taking 1 or 2 steps at a time.

Identify Repeating Operations

Identify the loops, recursion, array traversals that repeat.

• Primary operation: The for-loop that runs from 3 to n, adding two numbers each time.
• How many times: It runs (n - 2) times, once for each stair from 3 up to n.

How Execution Grows With Input

As the number of stairs increases, the loop runs more times, adding up the ways step by step.

Input Size (n)	Approx. Operations
10	8 additions
100	98 additions
1000	998 additions

Pattern observation: The number of operations grows roughly in a straight line with n.

Final Time Complexity

Time Complexity: O(n)

This means the time to find the answer grows directly with the number of stairs.

Common Mistake

[X] Wrong: "Because the problem looks like Fibonacci, it must take exponential time."

[OK] Correct: This code uses a simple loop and stores only two numbers, so it runs quickly in a straight line, not repeating work.

Interview Connect

Understanding this time complexity shows you can spot when a problem looks complex but can be solved efficiently with simple loops.

Self-Check

"What if we changed the code to use recursion without storing results? How would the time complexity change?"

Practice

1. What does the Climbing Stairs problem ask you to find?
Given n steps, how many distinct ways can you climb to the top if you can take 1 or 2 steps at a time?

easy

A. The maximum number of steps you can take at once

B. The minimum number of steps to reach the top

C. The total number of distinct ways to reach the top

D. The number of steps you skip

Solution

1. Step 1: Understand the problem statement

   The problem asks for the count of different ways to climb to the top using 1 or 2 steps at a time.

2. Step 2: Identify what is being counted

   We count all possible sequences of 1-step and 2-step moves that sum to n steps.

3. Final Answer:

   The total number of distinct ways to reach the top -> Option C

4. Quick Check:

   Counting ways = total distinct sequences [OK]

Hint: Count sequences of 1 or 2 steps summing to n [OK]

Common Mistakes:

• Confusing number of ways with minimum steps
• Thinking about maximum steps allowed
• Counting skipped steps instead of ways

2. Which of the following Java method signatures correctly represents a function to solve the Climbing Stairs problem for n steps?

easy

A. public int climbStairs(int n)

B. public void climbStairs(int n)

C. public int climbStairs()

D. public int climbStairs(String n)

Solution

1. Step 1: Check method return type and parameters

   The method should return an integer count and accept an integer input for steps.

2. Step 2: Validate each option

   public int climbStairs(int n) returns int and takes int n, which fits the problem. Others either have wrong return type or parameter type.

3. Final Answer:

   public int climbStairs(int n) -> Option A

4. Quick Check:

   Return int count, input int steps [OK]

Hint: Return int, input int n for steps [OK]

Common Mistakes:

• Using void return type instead of int
• Missing input parameter
• Using wrong parameter type like String

3. What is the output of the following Java code snippet?

public class Solution {
  public static int climbStairs(int n) {
    if (n <= 2) return n;
    int a = 1, b = 2, c = 0;
    for (int i = 3; i <= n; i++) {
      c = a + b;
      a = b;
      b = c;
    }
    return c;
  }
  public static void main(String[] args) {
    System.out.println(climbStairs(4));
  }
}

medium

A. 3

B. 7

C. 4

D. 5

Solution

1. Step 1: Trace the loop for n=4

   Initial a=1, b=2. Loop runs for i=3 and i=4.
   i=3: c=1+2=3, a=2, b=3
   i=4: c=2+3=5, a=3, b=5

2. Step 2: Return value after loop

   Return c=5, which is the number of ways to climb 4 steps.

3. Final Answer:

   5 -> Option D

4. Quick Check:

   Ways(4) = 5 [OK]

Hint: Use Fibonacci-like addition for steps > 2 [OK]

Common Mistakes:

• Returning b instead of c
• Off-by-one errors in loop
• Misunderstanding base cases

4. Identify the error in the following Java code for climbing stairs:

public int climbStairs(int n) {
  if (n == 1) return 1;
  int[] dp = new int[n];
  dp[0] = 1;
  dp[1] = 2;
  for (int i = 2; i <= n; i++) {
    dp[i] = dp[i-1] + dp[i-2];
  }
  return dp[n];
}

medium

A. Wrong return statement should be dp[n-1]

B. Array index out of bounds due to dp size and loop range

C. Incorrect base case for n == 1

D. Missing initialization of dp[2]

Solution

1. Step 1: Check array size and indexing

   Array dp size is n, but loop runs i from 2 to n inclusive, accessing dp[n], which is out of bounds.

2. Step 2: Identify correct loop range and return

   Loop should run i < n, and return dp[n-1] to avoid index error.

3. Final Answer:

   Array index out of bounds due to dp size and loop range -> Option B

4. Quick Check:

   Loop index exceeds array size [OK]

Hint: Array size n means max index n-1 [OK]

Common Mistakes:

• Looping to i <= n instead of i < n
• Returning dp[n] instead of dp[n-1]
• Incorrect base case handling

5. You want to find the number of ways to climb 10 steps but now you can take 1, 2, or 3 steps at a time. Which approach correctly adapts the Climbing Stairs problem?

hard

A. Use dp[i] = dp[i-1] + dp[i-2] + dp[i-3] with base dp[0]=1, dp[1]=1, dp[2]=2

B. Use dp[i] = dp[i-1] * dp[i-2] * dp[i-3] with base dp[0]=1

C. Use dp[i] = dp[i-1] + dp[i-2] only, ignoring 3 steps

D. Use recursion without memoization for all steps

Solution

1. Step 1: Understand the new step options

   Since you can take 1, 2, or 3 steps, ways to reach step i depend on previous 3 steps.

2. Step 2: Formulate dp relation and base cases

   dp[i] = dp[i-1] + dp[i-2] + dp[i-3]. Base cases: dp[0]=1 (one way to stand still), dp[1]=1, dp[2]=2.

3. Final Answer:

   Use dp[i] = dp[i-1] + dp[i-2] + dp[i-3] with base dp[0]=1, dp[1]=1, dp[2]=2 -> Option A

4. Quick Check:

   Include all step sizes in dp sum [OK]

Hint: Sum dp of last 3 steps for 1,2,3 step moves [OK]

Common Mistakes:

• Ignoring 3-step moves in dp formula
• Multiplying dp values instead of adding
• Using recursion without caching causing slow run