Data Structure and Algorithms
  • Introduction
  • 面经
    • 亚马逊面经
  • Sorting
    • Quick Sort
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    • Heap Sort
  • Palindrome
    • Check String Palindrom
    • Palindrome Partitioning
    • Palindrome Partitioning II
    • Longest Palindromic Substring
    • Valid Palindrome
  • Linked List
    • Remove Duplicates from Sorted List
    • Remove Duplicates from Sorted List II
    • Remove Nth Node From End of List
    • Remove Linked List Elements
    • Remove Duplicates from Unsorted List
    • Remove duplicate Circular Linked list
    • Reverse Linked List
    • Reverse Linked List II
    • Reverse Nodes in k-Group
    • Partition List
    • Insertion Sort List
    • Reorder List
    • Linked List Cycle
    • Rotate List
    • Merge k Sorted Lists
    • Copy List with Random Pointer
    • Nth to Last Node in List
    • Add Two Numbers
    • Add Two Numbers II
    • Palindrome Linked List
  • Binary Search
    • Sqrt(x)
    • Search a 2D Matrix
    • Search a 2D Matrix II
    • Search Insert Position
    • First Position of Target
    • Last Position of Target
    • Count of Smaller Number
    • Search for a Range
    • Search in a Big Sorted Array
    • First Bad Version
    • Find Minimum in Rotated Sorted Array
    • Find Minimum in Rotated Sorted Array II
    • Search in Rotated Sorted Array
    • Search in Rotated Sorted Array II
    • Find Peak Element*
    • Recover Rotated Sorted Array
    • Rotate String
    • Wood Cut
    • Total Occurrence of Target
    • Closest Number in Sorted Array
    • K Closest Number in Sorted Array
    • Maximum Number in Mountain Sequence
    • Search Insert Position *
    • Pow(x, n)
    • Divide Two Integers
  • Graph
    • Clone Graph
    • Topological Sorting
    • Permutations
    • Permutations II
    • Subsets
    • Subsets II
    • Word Ladder
    • Word Ladder II
    • N-Queens
    • N-Queens II
    • Connected Component in Undirected Graph
    • Six Degrees
    • String Permutation II
    • Letter Case Permutation
  • Data Structure
    • Min Stack
    • Implement a Queue by Two Stacks
    • Largest Rectangle in Histogram
    • Max Tree
    • Rehashing
    • LRU Cache
    • Subarray Sum
    • Anagrams
    • Longest Consecutive Sequence
    • Data Stream Median
    • Heapify
    • Ugly Number
    • Ugly Number II
  • Misc
    • PlaceHolder
    • Fibonacci
  • Array and Numbers
    • Merge Sorted Array
    • Merge Two Sorted Arrays
    • Median of two Sorted Arrays
    • Best Time to Buy and Sell Stock
    • Best Time to Buy and Sell Stock II
    • Best Time to Buy and Sell Stock III
    • Maximum Subarray
    • Maximum Subarray II
    • Maximum Subarray III
    • Minimum Subarray
    • Maximum Subarray Difference
    • Subarray Sum
    • Subarray Sum Closest
    • Two Sum
    • 3Sum
    • 3Sum Closest
    • 4Sum
    • k Sum
    • k Sum II
    • Partition Array
    • Sort Letters by Case
    • Sort Colors
    • Sort Colors II
    • Interleaving Positive and Negative Numbers
    • Spiral Matrix
    • Spiral Matrix II
    • Rotate Image
  • Dynamic Programming I
    • Triangle
    • Minimum Path Sum
    • Unique Paths
    • Unique Paths II
    • Climbing Stairs
    • Jump Game
    • Jump Game II
    • 01 Matrix
    • Longest Line of Consecutive One in Matrix
    • Shortest Path in Binary Matrix
  • Dynamic Programming II
    • Word Break
    • Longest Common Subsequence
    • Longest Common Substring
    • Edit Distance
    • Distinct Subsequences
    • Interleaving String
    • k Sum
  • Binary Tree And Divide Conquer
    • Binary Tree Preorder Traversal*
    • Binary Tree Inorder Traversal*
    • Binary Tree Postorder Traversal*
    • Maximum Depth of Binary Tree
    • Minimum Depth of Binary Tree
    • Balanced Binary Tree
    • Lowest Common Ancestor
    • Binary Tree Maximum Path Sum
    • Binary Tree Maximum Path Sum II
    • Binary Tree Level Order Traversal*
    • Binary Tree Level Order Traversal II
    • Binary Tree Zigzag Level Order Traversal
    • Validate Binary Search Tree
    • Inorder Successor in Binary Search Tree
    • Binary Search Tree Iterator
    • Search Range in Binary Search Tree
    • Insert Node in a Binary Search Tree
    • Remove Node in Binary Search Tree
    • Find the kth largest element in the BST
    • Kth Smallest Element in a BST
    • Serialize and Deserialize Binary Tree*
    • Construct Binary Tree from Preorder and Inorder Traversal
    • Convert Sorted Array to Binary Search Tree
    • Unique Binary Search Trees *
    • Unique Binary Search Trees II *
    • Recover Binary Search Tree
    • Same Tree
    • Symmetric Tree
    • Path Sum*
    • Path Sum II*
    • Flatten Binary Tree to Linked List
    • Populating Next Right Pointers in Each Node
    • Sum Root to Leaf Numbers
    • Binary Tree Right Side View
    • Count Complete Tree Nodes
    • Invert Binary Tree
    • Binary Tree Paths*
    • Subtree of Another Tree
  • A家面试总结
  • Expedia面经收集
  • Python 常用语句
  • lotusflare
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  1. Binary Search

Wood Cut

Given n pieces of wood with length L[i] (integer array). Cut them into small pieces to guarantee you could have equal or more than k pieces with the same length. What is the longest length you can get from the n pieces of wood? Given L & k, return the maximum length of the small pieces.

Example

For L=[232, 124, 456], k=7, return 114.

Solution

给定了L是一个木板数组,给定了需要切成的块数,求解能切最大长度的最少的等长木板数。

(1)最后取的能切的最大长度,所以不可能超过木板中的最长的那根,

所以start = 1 最小长度,end = 最大长度,(二分法问题,索引,具体的数值 都可以作为求解的依据。)

(2) 这里的mid , 可以间接的求出可以切出多少块木板,这样就可以得出,如果当前的mid 宽度可以切出的木板数量,如果大于了给定的K块,则说明需要切出更少的块数,则需要增加长度,这样,start = mid 注解:12月26日,二分法不一定用数组索引同时也可以用尺寸单位,二分法的基本就是排除法,考虑这道题是否可以使用DP来实现。

class Solution:
    """
    @param L: Given n pieces of wood with length L[i]
    @param k: An integer
    return: The maximum length of the small pieces.
    """
    def woodCut(self, L, k):
        # very important here, we are supposed the min = 1 not 0.1 or deciaml value
        if sum(L) < k:
            return 0
        start, end = 1, max(L)
        while start + 1 < end:
            mid = ( start + end ) / 2
            pieces = sum( l / mid for l in L )
            if pieces >= k :
                start = mid
            else:
                end = mid
        # why is greater and equal k instead of == k, because it may cannot cut right into k piece
        if sum(l / end for l in L) >= k:
            return end
        return start
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