3693. Climbing Stairs

3693. Climbing Stairs II

Difficulty: Medium

Topics: Senior, Array, Dynamic Programming, Biweekly Contest 166

You are climbing a staircase with n + 1 steps, numbered from 0 to n.

You are also given a 1-indexed integer array costs of length n, where costs[i] is the cost of step i.

From step i, you can jump only to step i + 1, i + 2, or i + 3. The cost of jumping from step i to step j is defined as: costs[j] + (j - i)²

You start from step 0 with cost = 0.

Return the minimum total cost to reach step n.

Example 1:

• Input: n = 4, costs = [1,2,3,4]
• Output: 13

• Explanation:

  • One optimal path is 0 → 1 → 2 → 4

  Jump	Cost Calculation	Cost
  0 → 1	costs[1] + (1 - 0)² = 1 + 1	2
  1 → 2	costs[2] + (2 - 1)² = 2 + 1	3
  2 → 4	costs[4] + (4 - 2)² = 4 + 4	8

  • Thus, the minimum total cost is 2 + 3 + 8 = 13

Example 2:

• Input: n = 4, costs = [5,1,6,2]
• Output: 11

• Explanation:

  • One optimal path is 0 → 2 → 4

  Jump	Cost Calculation	Cost
  0 → 2	costs[2] + (2 - 0)² = 1 + 4	5
  2 → 4	costs[4] + (4 - 2)² = 2 + 4	6

  • Thus, the minimum total cost is 5 + 6 = 11

Example 3:

• Input: n = 3, costs = [9,8,3]
• Output: 12
• Explanation: The optimal path is 0 → 3 with total cost = costs[3] + (3 - 0)² = 3 + 9 = 12

Example 4:

• Input: n = 1, costs = [5]
• Output: 6

Example 5:

• Input: n = 2, costs = [1,1]
• Output: 4

Example 6:

• Input: n = 5, costs = [1,1,1,1,1]
• Output: 10

Example 7:

• Input: n = 10, costs = [1,1,1,1,1,1,1,1,1,1]
• Output: 20

Example 8:

• Input: n = 100000, costs = 1, 1, ..., 1
• Output: 200000

Example 9:

• Input: n = 4, costs = [10000,10000,10000,10000]
• Output: 20008

Example 10:

• Input: n = 6, costs = [2,5,1,3,4,7]
• Output: 23

Constraints:

• 1 <= n == costs.length <= 10⁵
• 1 <= costs[i] <= 10⁴

Hint:

1. Use dynamic programming where dp[j] represents the minimum cost to reach step j.
2. From step i, you can jump to i+1, i+2, or i+3. Transition depends only on the previous three states.
3. dp[j] = min(dp[i] + costs[j] + (j - i)²) for all i in {j-1, j-2, j-3}.
4. Initialize dp[0] = 0 and compute up to dp[n]; the answer is dp[n].

Solution:

We are given a staircase with n + 1 steps (0 to n) and a 1-indexed costs array. From any step i, we can jump 1, 2, or 3 steps forward. Each jump i → j costs costs[j] + (j - i)². We start at step 0 with cost 0 and need the minimum total cost to reach step n. This is solved efficiently with linear dynamic programming because each state depends only on the previous three states.

Approach

• Define dp[j] = minimum total cost to reach step j.
• Base case: dp[0] = 0 (we start here for free).
• Because a jump is limited to 1, 2, or 3 steps, the only predecessors of step j are j-1, j-2, and j-3 (clamped at 0).
• For each step j from 1 to n:
  • Try jumping from each valid predecessor i ∈ {j-3, j-2, j-1} (but i ≥ 0).
  • Compute dp[i] + costs[j] + (j - i)².
  • Take the minimum over these up to 3 candidates → dp[j].
• The answer is dp[n].

Let's implement this solution in PHP: 3693. Climbing Stairs II

<?php /** * @param Integer $n * @param Integer[] $costs * @return Integer */ function climbStairs(int $n, array $costs): int { ... ... ... /** * go to ./solution.php */ } // Test cases echo climbStairs(4, [1,2,3,4]) . "\n"; // Output: 13 echo climbStairs(4, [5,1,6,2]) . "\n"; // Output: 11 echo climbStairs(3, [9,8,3]) . "\n"; // Output: 12 echo climbStairs(1, [5]) . "\n"; // Output: 6 echo climbStairs(2, [1,1]) . "\n"; // Output: 4 echo climbStairs(5, [1,1,1,1,1]) . "\n"; // Output: 10 echo climbStairs(10, array_fill(0, 10, 1)) . "\n"; // Output: 20 echo climbStairs(100000, array_fill(0, 100000, 1)) . "\n"; // Output: 200000 echo climbStairs(4, [10000,10000,10000,10000]) . "\n"; // Output: 20008 echo climbStairs(6, [2,5,1,3,4,7]) . "\n"; // Output: 23 ?> 

Explanation

• dp array initialization: array_fill(0, $n + 1, 0) creates n+1 slots, all set to 0; dp[0] is already 0.
• Outer loop for ($j = 1; $j <= $n; $j++): fills the DP table in increasing order of step index, which is valid because every transition goes to a smaller index.
• $start = max(0, $j - 3): ensures we only look back up to 3 steps and never index a negative step.
• $stepCost = $costs[$j - 1]: translates the 1-indexed problem statement into PHP's 0-indexed array — cost of step j is $costs[j-1].
• Inner loop for ($i = $start; $i < $j; $i++): considers all reachable predecessors i within distance 1–3.
• Transition: $candidate = $dp[$i] + $stepCost + $distance * $distance combines the cost to reach i, the landing cost on j, and the squared jump distance.
• Minimize: $best tracks the minimum candidate; dp[j] = $best stores the optimal cost for step j.
• Return $dp[$n]: the minimum total cost to reach the top step.

Complexity Analysis

• Time Complexity: O(n) — outer loop runs n times, inner loop runs at most 3 iterations per step.
• Space Complexity: O(n) — for the dp array of size n + 1.
  • (Can be optimized to O(1) since only the last 3 DP values are needed.)

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