Divide & Conquer - WebDevStudy

1.

Convert Sorted Array to Binary Search Tree (Easy)

Given an integer array nums where the elements are sorted in ascending order, convert it to a height-balanced binary search tree.

class TreeNode:
    def __init__(self, val=0, left=None, right=None):
        self.val = val
        self.left = left
        self.right = right

2.

Sort List (Medium)

Given the head of a linked list, return the list after sorting it in ascending order.

class ListNode:
    def __init__(self, val=0, next=None):
        self.val = val
        self.next = next

# Time complexity: O(n * log(n))
# Space complexity: O(1)
def sortList(head: Optional[ListNode]) -> Optional[ListNode]:
    if not head: return head

    dummy = ListNode(0, head)
    length = getLength(head)
    step = 1
    while length > step:
        curr = dummy.next
        tail = dummy
        # Process entire list, merging sections of step length
        # Example:
        #   list = 4 -> 2 -> 1 -> 3; step = 1
        #   left = 4; right = 2. Merge into 2 -> 4
        #   left = 1; right = 3. Merge into 1 -> 3
        #   Reached end of list. Increase step
        #   list = 2 -> 4 -> 1 -> 3; step = 2
        #   left = 2 -> 4; right = 1 -> 3. Merge into 1 -> 2 -> 3 -> 4
        while curr:
            left = curr
            right = split(left, step)  # right points to node after left's tail
            curr = split(right, step)  # curr points to node after right's tail
            tail = merge(left, right, tail)  # tail points to end of merged list
        step *= 2
    return dummy.next

def getLength(head: Optional[ListNode]) -> int:
    count = 0
    while head:
        count += 1
        head = head.next
    return count

# Given head node, skip step nodes, cut at tail, return tail.next
def split(head: Optional[ListNode], step: int):
    # (step - 1) because prev node is needed to cut at tail
    for _ in range(step - 1):
        if head:
            head = head.next

    # In case steps reach end of original list
    if not head: return None

    cutoff = head.next
    head.next = None
    return cutoff

# Merge two sorted lists, append to head, return tail of new list
def merge(a, b, head) -> Optional[ListNode]:
    curr = head
    while a and b:
        if a.val < b.val:
            curr.next = a
            a = a.next
        else:
            curr.next = b
            b = b.next
        curr = curr.next

    curr.next = a or b
    while curr.next:
        curr = curr.next
    return curr

Alternative solution:

# Time complexity: O(n * log(n))
# Space complexity: O(log(n))
def sortList(head: Optional[ListNode]) -> Optional[ListNode]:
    if not head or not head.next:
        return head

    # prev is needed to split list. Consider a list of length 2
    prev = slow = fast = head
    while fast and fast.next:
        prev = slow
        slow = slow.next
        fast = fast.next.next
    prev.next = None

    # Recurse until lists are length 1
    left = sortList(head)
    right = sortList(slow)
    return merge(left, right)

def merge(a, b) -> Optional[ListNode]:
    dummy = curr = ListNode()
    while a and b:
        if a.val < b.val:
            curr.next = a
            a = a.next
        else:
            curr.next = b
            b = b.next
        curr = curr.next

    curr.next = a or b
    return dummy.next

3.

Construct Quad Tree (Medium)

Given a n * n matrix grid of 0's and 1's only, represent grid with a Quad-Tree.

Return the root of the Quad-Tree representing grid.

A Quad-Tree is a tree data structure in which each internal node has exactly four children. Each node has two attributes:

val: True if the node represents a grid of 1's or False if the node represents a grid of 0's.
isLeaf: True if the node is a leaf node on the tree or False if the node has four children.

# Definition for a QuadTree node.
class Node:
    def __init__(self, val, isLeaf, topLeft, topRight, bottomLeft, bottomRight):
        self.val = val
        self.isLeaf = isLeaf
        self.topLeft = topLeft
        self.topRight = topRight
        self.bottomLeft = bottomLeft
        self.bottomRight = bottomRight

Construct a Quad-Tree from a two-dimensional area using the following steps:

If the current grid has the same value (i.e all 1's or all 0's), set isLeaf True and set val to the value of the grid and set the four children to Null and stop.
If the current grid has different values, set isLeaf to False and set val to any value and divide the current grid into four sub-grids.
Recurse for each of the children with the proper sub-grid.