📓
Algorithms
  • Introduction to Data Structures & Algorithms with Leetcode
  • Strings
    • Dutch Flags Problem
      • List Partitoning
    • Counters
      • Majority Vote
      • Removing Parentheses
      • Remove Duplicates from Sorted Array
    • Maths
      • Lone Integer
      • Pigeonhole
      • Check If N and Its Double Exist
      • Find Numbers with Even Number of Digits
    • Two Pointers
      • Remove Element
      • Replace Elements with Greatest Element on Right Side
      • Valid Mountain Array
      • Sort Array by Parity
      • Squares of a Sorted Array
      • Max Consecutive Ones
    • Sliding Window
      • Max Consecutive Ones 3
    • Stacks
      • Balanced Brackets
    • General Strings & Arrays
      • Move Zeros
      • Unique Elements
      • Merge Sorted Array
    • Matrices
      • Valid Square
      • Matrix Search Sequel
  • Trees
    • Untitled
  • Recursion
    • Introduction
    • Backtracking
      • Permutations
  • Dynamic Programming
    • Introduction
    • Minimum (Maximum) Path to Reach a Target
      • Min Cost Climbing Stairs
      • Coin Change
      • Minimum Path Sum
      • Triangle
      • Minimum Cost to Move Chips to The Same Position
      • Consecutive Characters
      • Perfect Squares
    • Distinct Ways
      • Climbing Stairs
      • Unique Paths
      • Number of Dice Rolls with Target Sum
    • Merging Intervals
      • Minimum Cost Tree From Leaf Values
    • DP on Strings
      • Levenshtein Distance
      • Longest Common Subsequence
  • Binary Search
    • Introduction
      • First Bad Version
      • Sqrt(x)
      • Search Insert Position
    • Advanced
      • KoKo Eating Banana
      • Capacity to Ship Packages within D Days
      • Minimum Number of Days to Make m Bouquets
      • Split array largest sum
      • Minimum Number of Days to Make m Bouquets
      • Koko Eating Bananas
      • Find K-th Smallest Pair Distance
      • Ugly Number 3
      • Find the Smallest Divisor Given a Threshold
      • Kth smallest number in multiplication table
  • Graphs
    • Binary Trees
      • Merging Binary Trees
      • Binary Tree Preorder Traversal
      • Binary Tree Postorder Traversal
      • Binary Tree Level Order Traversal
      • Binary Tree Inorder Traversal
      • Symmetric Tree
      • Populating Next Right Pointers in Each Node
      • Populating Next Right Pointers in Each Node II
      • 106. Construct Binary Tree from Inorder and Postorder Traversal
      • Serialise and Deserialise a Linked List
      • Maximum Depth of Binary Tree
      • Lowest Common Ancestor of a Binary Tree
    • n-ary Trees
      • Untitled
      • Minimum Height Trees
    • Binary Search Trees
      • Counting Maximal Value Roots in Binary Tree
      • Count BST nodes in a range
      • Invert a Binary Tree
      • Maximum Difference Between Node and Ancestor
      • Binary Tree Tilt
  • Practice
  • Linked Lists
    • What is a Linked List?
    • Add Two Numbers
      • Add Two Numbers 2
    • Reverse a Linked List
    • Tortoise & Hare Algorithm
      • Middle of the Linked List
  • Bitshifting
    • Introduction
  • Not Done Yet
    • Uncompleted
    • Minimum Cost For Tickets
    • Minimum Falling Path Sum
Powered by GitBook
On this page

Was this helpful?

  1. Dynamic Programming
  2. Minimum (Maximum) Path to Reach a Target

Min Cost Climbing Stairs

PreviousMinimum (Maximum) Path to Reach a TargetNextCoin Change

Last updated 4 years ago

Was this helpful?

On a staircase, the i-th step has some non-negative cost cost[i] assigned (0 indexed).

Once you pay the cost, you can either climb one or two steps. You need to find minimum cost to reach the top of the floor, and you can either start from the step with index 0, or the step with index 1.

class Solution:
    def minCostClimbingStairs(self, cost: List[int]) -> int:
        for i in range(2, len(cost)):
            cost[i] = min(cost[i] + cost[i - 1], cost[i] + cost[i - 2])
        return min(cost[-1], cost[-2])

The first 2 steps are our basecases, from there every value up to the length of the cost we work out the value.

The value is the minimum of:

  • The current step added to the step immediately before it (cost[i - 1])

  • The current step added to the step 2 before it (cost[i - 2])

This is Fibonacci sequence.

We return whichever one is minimum between [-2] and [-1]. Since we can go up 2 steps, we can end at [-2] instead of [-1] depending on which is minimum.

https://leetcode.com/problems/min-cost-climbing-stairs/