Given a grid with obstacles (represented by 1s) and free spaces (represented by 0s), write a function to find the shortest path from the top-left corner to the bottom-right corner. If no path exists, return -1.
Google values candidates who can solve complex problems efficiently. Pathfinding algorithms are fundamental in many Google products, such as Google Maps, making this question relevant for assessing analytical and coding skills.
Understand the problem and clarify any doubts.
Discuss potential algorithms like Breadth-First Search (BFS) or A*.
Implement the algorithm step-by-step, ensuring to handle edge cases.
Test the solution against various scenarios.
To solve the problem of finding the shortest path in a grid with obstacles, I would use the Breadth-First Search (BFS) algorithm. This algorithm is suitable as it explores all possible paths layer by layer, ensuring that the shortest one is found first. I would start by initializing a queue with the starting point and a visited set to track the cells we've already explored. In each iteration, I would dequeue a cell, check if it's the goal (bottom-right corner), and if not, enqueue all its valid neighbors (up, down, left, right) that haven't been visited yet. If we reach the goal, I would return the number of steps taken; otherwise, if the queue is exhausted without reaching the goal, I would return -1. This approach ensures a time complexity of O(N*M), where N is the number of rows and M is the number of columns.
Practice implementing pathfinding algorithms in different languages.
Familiarize yourself with various grid representations.
Be ready to explain the time and space complexity of your solution.
Consider how your algorithm scales with larger inputs and optimize if necessary.
It's best to implement the algorithm from scratch to demonstrate your understanding.
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