How does a hash table handle collisions?
- By ignoring collisions and overwriting existing values.
- By rearranging the elements in the table.
- By resizing the hash table to accommodate more elements.
- By using techniques such as chaining or open addressing to resolve conflicts.
Hash tables handle collisions by employing techniques such as chaining or open addressing. Chaining involves maintaining a linked list at each bucket to store colliding elements, while open addressing involves finding the next available slot in the table.
Multidimensional arrays are arrays of _______ arrays.
- Heterogeneous
- Homogeneous
- Linear
- Non-linear
Multidimensional arrays are arrays of homogeneous arrays, meaning that each element in the outer array points to another array of the same data type.
Reversing a linked list recursively involves changing the _______ of each node.
- Data
- Next pointer
- Previous pointer
- Value
Reversing a linked list recursively involves changing the previous pointer of each node. In each recursive call, the next pointer of each node is redirected to its previous node, gradually reversing the entire list.
In the context of strings, what does the term "edit" refer to in the Edit Distance algorithm?
- All of the above.
- Deleting characters from a string.
- Inserting characters into a string.
- Modifying characters in a string.
In the context of strings and the Edit Distance algorithm, the term "edit" refers to all three operations: deleting characters, inserting characters, and modifying characters in a string. These operations are used to transform one string into another.
Can you explain the concept of "patience" in the context of the LIS problem?
- It indicates the randomness introduced to the LIS problem.
- It is a measure of how many piles are formed during the patience sorting algorithm.
- It refers to the time complexity of the algorithm.
- It represents the ability to wait for the optimal solution in the LIS problem.
In the context of the LIS problem, "patience" refers to the number of piles formed during the patience sorting algorithm. The more piles formed, the longer the increasing subsequence, and the patience value correlates with the length of the LIS.
What does LCS stand for in dynamic programming?
- Least Common Sequence
- Longest Common Subarray
- Longest Common Subsequence
- Longest Continuous Subsequence
LCS stands for Longest Common Subsequence in dynamic programming. It refers to the longest subsequence that is common to two or more sequences but not necessarily in a contiguous manner.
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.