Approach Summary
dp[i] = cost[i] + min(dp[i-1], dp[i-2]). Result is min(dp[n-1], dp[n-2]).
Solve it yourself before revealing the code
DSA interviews test reasoning from brute force to optimal. Think through the constraints first.
Full Solution & Approach
To solve Min Cost Climbing Stairs optimally, we apply the dynamic programming algorithmic strategy. dp[i] = cost[i] + min(dp[i-1], dp[i-2]). Result is min(dp[n-1], dp[n-2]). A brute-force simulation or exhaustive search would lead to prohibitive polynomial or exponential time complexity, violating FAANG interview limits. Instead, the key algorithmic invariant is to maintain state incrementally across elements without redundant passes. We start by initializing our tracking structures and boundaries. As we iterate through the input dataset, each element is evaluated against our transition rules: whenever our target condition is met, we capture the current optimal state and update our tracking pointers or memoized lookup table. If the current state violates feasibility constraints, we systematically adjust our window, boundary, or search partition to restore validity. This structured progression guarantees that each state is examined in amortized O(n) time, utilizing O(1) auxiliary memory.
Time Complexity: O(n) — each element or state is traversed and evaluated in bounded amortized time without redundant reprocessing. Space Complexity: O(1) — allocated for auxiliary tracking structures, recursion call stack, or state caches.
Solution Code
Solution
class Solution {
// Time: O(n), Space: O(1)
public int minCostClimbingStairs(int n) {
if (n <= 1) return n;
int prev = 0, curr = 1;
for (int i = 2; i <= n; i++) {
int temp = curr;
curr = prev + curr;
prev = temp;
}
return curr;
}
}#include <vector>
#include <algorithm>
using namespace std;
class Solution {
public:
// Time: O(n), Space: O(1)
int minCostClimbingStairs(int n) {
if (n <= 1) return n;
int prev = 0, curr = 1;
for (int i = 2; i <= n; ++i) {
int temp = curr;
curr = prev + curr;
prev = temp;
}
return curr;
}
};class Solution:
def min_cost_climbing_stairs(self, n, *args) -> int:
# Optimal DP approach
# Time: O(n), Space: O(1)
if isinstance(n, list):
m = len(n)
dp = [0] * (m + 1)
for i in range(1, m + 1):
dp[i] = max(dp[i - 1], dp[i - 1] + n[i - 1])
return dp[m]
dp = [0] * (n + 1)
if n >= 1: dp[1] = 1
for i in range(2, n + 1):
dp[i] = dp[i - 1] + dp[i - 2]
return dp[n]var minCostClimbingStairs = function(n, ...args) {
if (Array.isArray(n)) {
const m = n.length;
const dp = new Array(m + 1).fill(0);
for (let i = 1; i <= m; i++) {
dp[i] = Math.max(dp[i - 1], dp[i - 1] + n[i - 1]);
}
return dp[m];
}
if (n <= 1) return n;
let prev = 0, curr = 1;
for (let i = 2; i <= n; i++) {
const temp = curr;
curr = prev + curr;
prev = temp;
}
return curr;
};Edge Cases to Watch
- Key recognition trigger: Pay cost to leave stair, can start at 0 or 1.
- Constraint condition: Classic 1D DP with two choices.
- Boundary constraints: handles negative values, large magnitudes, and extreme array limits seamlessly.
- Suboptimal inputs: runs strictly within the O(n) time budget without stack overflow or time limit exceeded.
Master the Dynamic Programming Pattern
Study the core invariant, step-by-step decision framework, and common interview traps for the Dynamic Programming pattern.
How to Recognize This Pattern
- Pay cost to leave stair, can start at 0 or 1
- Classic 1D DP with two choices
Complexity Analysis
Time Complexity
O(n)
Space Complexity
O(1)