How does DFS traverse through a graph or tree?
- Explore nodes randomly
- Iteratively explore each branch until all nodes are visited
- Recursively explore each branch until all nodes are visited
- Traverse nodes level-wise
DFS traverses through a graph or tree by recursively exploring each branch until all nodes are visited. It starts at the root node, explores as far as possible, backtracks, and continues until all nodes are covered.
Imagine you are developing a social network platform where you need to find the shortest path between two users in a friendship graph. Would DFS be appropriate for this scenario? Justify your answer.
- Depends on the graph structure
- Maybe
- No
- Yes
No, DFS would not be appropriate for finding the shortest path in a friendship graph. DFS is not designed for finding the shortest path, as it explores paths deeply, not necessarily the shortest ones. Instead, algorithms like Dijkstra's or BFS are more suitable for this task.
How does the Fibonacci sequence relate to the golden ratio?
- The Fibonacci sequence is unrelated to the golden ratio.
- The golden ratio is the difference between Fibonacci numbers.
- The golden ratio is the sum of Fibonacci numbers.
- The ratio of consecutive Fibonacci numbers converges to the golden ratio.
The Fibonacci sequence is intimately connected to the golden ratio. As you progress in the sequence, the ratio of consecutive Fibonacci numbers converges to the golden ratio, approximately 1.6180339887. This relationship adds a layer of elegance to both concepts.
Manacher's Algorithm is particularly efficient when the input string contains many _______ palindromes.
- Disjoint
- Isolated
- Non-contiguous
- Overlapping
Manacher's Algorithm excels when the input string contains many overlapping palindromes. Its linear time complexity remains effective even in scenarios with a high density of overlapping palindromes.
How does merge sort perform in terms of time complexity compared to other sorting algorithms for large datasets?
- O(log n)
- O(n log n)
- O(n)
- O(n^2)
Merge sort excels in time complexity for large datasets, performing at O(n log n), which is more efficient than O(n^2) algorithms like bubble sort or insertion sort. This makes merge sort a preferred choice for large-scale sorting tasks.
In the coin change problem, what is meant by the term "minimum number of coins"?
- The fewest number of coins required to represent a given amount.
- The least valuable coins in the currency.
- The lowest denomination of coins in the given set.
- The smallest physical size of the coins.
In the coin change problem, the term "minimum number of coins" refers to the fewest number of coins needed to represent a given target amount. The goal is to optimize the combination of coins used to minimize the total count.
Compare and contrast separate chaining and open addressing collision resolution strategies in hash tables.
- Both methods involve creating secondary data structures to handle collisions. Separate chaining uses linked lists, while open addressing stores elements directly in the hash table.
- Separate chaining and open addressing are identical in their approach to collision resolution.
- Separate chaining and open addressing both involve redistributing colliding elements to other locations. Separate chaining uses a single array, while open addressing uses multiple arrays.
- Separate chaining uses a single array to store all elements, while open addressing distributes elements across multiple arrays. Both methods avoid collisions by using dynamic resizing.
Separate chaining and open addressing are two common strategies for handling collisions in hash tables. Separate chaining involves creating linked lists at each index to store colliding elements, while open addressing places elements directly in the hash table, using methods like linear probing or quadratic probing to find alternative locations for collisions.
Naive pattern matching compares each character of the pattern with each character of the text _______.
- In reverse order
- One by one
- Randomly
- Simultaneously
Naive pattern matching compares each character of the pattern with each character of the text one by one. It involves a simple character-by-character comparison, starting from the beginning of the text, and sliding the pattern one position at a time until a match is found or the end of the text is reached.
The time complexity of the standard dynamic programming approach for Matrix Chain Multiplication is _______.
- O(2^n)
- O(n)
- O(n^2)
- O(n^3)
The time complexity of the standard dynamic programming approach for Matrix Chain Multiplication is O(n^3), where 'n' is the number of matrices being multiplied. This is achieved through a bottom-up dynamic programming approach that efficiently calculates the optimal parenthesization.
What does Longest Increasing Subsequence (LIS) refer to?
- The longest subarray with elements in non-decreasing order.
- The longest subarray with elements in strictly increasing order.
- The maximum sum of elements in a subarray with consecutive elements.
- The minimum sum of elements in a subarray with consecutive elements.
Longest Increasing Subsequence (LIS) refers to the longest subarray with elements in strictly increasing order. The goal is to find the length of this subsequence.