Can you walk me through the meaning behind a Depth First Search Algorithm for a binary tree?
I have designed and developed tree data structures that are a collection of nodes like a graph. It starts with a root node that has children nodes and similar nodes called grandfather nodes. In my current role, I do work with data table structures like Level Order Transversals and Depth First Transversals, so I'm quite familiar with how they work. My understanding of Depth First Search Algorithm for a binary tree is that a tree is a graph that has no cycles (with a cycle being a path in the graph that starts and ends at the same vertex). A child node, on the other hand, has one parent. It is for this reason that trees are not a recursive data structure. This also applies to graphs for finding a path between two nodes, finding the shortest path from one node to another, and finding the shortest path that visits all nodes, respectively.
When looking at a question of this magnitude, there are several different factors to consider. You will want to get clarification from the interviewer on whether they want to hear the meaning for tree structure data, time complexity, extra space, or a node stack. Let's examine what some of these mean and how they might come up in the course of an interview. Time Complexity - has four transversals O(n) as they visit every node exactly once. Extra Space - requires O(w) Level Order Transversal where w is the maximum width of a Binary Tree which stores nodes of different levels. Interview-related questions might look something like typical binary tree numbers. 1, 3, 7, 15, or, in a worst-case scenario, a value of 2h is Ceil(n/2) will usually come up as good answers to this question. A couple more that might come are when is extra space required for Level Order Transversal and Depth First Transversal. Two good responses can be a more balanced position for Level Order Transversal and a less balanced position for Depth First Transversal.
"I'm familiar with the meaning of this question. There are a couple of ways to answer it, so that I will give you both examples. For starters, the two most common methods of searching a graph or a tree related to a depth-first search are depth-first search and breadth-first search. Depending on the application you are using and the result you want to achieve, this should be determined by the type of data in your tree or graph data structure. For a Breath First Search, I start at the root node. I then search all their children nodes moving from left to right, then I repeat the process at the level below the root node. I typically repeat this on each level until I reach the end of the tree or node. As a general practice, I use a queue as an intermediary step as a way of keeping track of what nodes I need to search."
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