Breadth-first Search

• Running time complexity: O(V + E)
• Memory complexity is not good as we have to sort lots of references. That is why DFS is usually preferred.
• But it constructs a shortest path: Dijkstra algorithm does a BFS if all the edge weights are equal to one.
• We have an empty queue at the beginning and we keep checking whether we have visited the given node or not. Keep iterating until queue is not empty.
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Some Features

Time Complexity
Typical ones are: O(n^2) or O(nlogn) or O(n), for more check Big-O Cheatsheet.
In-place
Strictly an in-place sort needs only o(1) memory beyond the items being sorted.
So an in place algorithm does not need any extra memory.
Recursive
Some sorting algorithms are implemented in a recursive manner, especially the divide and conquer ones. Such as merge sort and quick sort.
Stable
Stable sorting algorithms maintain the relative order of records with equal values.

Priority Queue

• It is an abstract data type such as stack or queue
• But every item has an additional property: a priority value
• Priority Queue is usually implemented with heaps, but it can be implemented with self balancing trees as well
• The highest priority element is retrieved first, no FIFO structure here
• Operation: InsertWithPriority(data, priority), getHighestPriorityElement(), peek()

Binary Search Tree

The conditional 〜ば form changes differently in these word types: 『動詞』(verb)、『い形容詞』(i adjective)、『な形容詞』(na adjective)、そして『名詞』(noun)。
And 『動詞』(verb) will include three different categories: u verb, ru verb, and exceptions.

Stack and queue abstract data types implementation with Python.

Stack

• LIFO structure
• Graph algorithm: depth-first search can be implemented with Stack
• Finding Euler-cycles in a Graph
• Finding strongly connected components in a graph
• If we use recursion, the OS will use stacks
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