What are some common collision resolution techniques used in hash tables?
- Binary search, Hashing, Graph traversal, Divide and conquer
- Breadth-first search, Depth-first search, Dijkstra's algorithm, Bellman-Ford algorithm
- Bubble sort, Merge sort, Quick sort, Radix sort
- Linear probing, Quadratic probing, Separate chaining, Double hashing
Common collision resolution techniques include linear probing, quadratic probing, separate chaining, and double hashing. These methods address the issue of two keys hashing to the same index in the hash table.
The longest common substring problem aims to find the _______ string that appears in two or more given strings.
- Common
- Longest
- Shortest
- Unique
The longest common substring problem aims to find the common string that appears in two or more given strings. It involves identifying the substring that is present in all given strings and has the maximum length.
Topological sorting is essential in optimizing _______ schedules, ensuring that tasks are executed in the correct order.
- Algorithm
- Dependency
- Execution
- Job
Topological sorting is essential in optimizing job schedules, ensuring that tasks are executed in the correct order based on dependencies. It is commonly used in project management and task scheduling.
Compared to arrays, linked lists have _______ access time but _______ memory overhead.
- Constant, Constant
- Constant, Linear
- Linear, Constant
- Linear, Linear
Compared to arrays, linked lists have constant access time but linear memory overhead. Linked lists provide constant time for insertion and deletion at any position, but they require additional memory for storing the next pointer in each node.
Which step of the merge sort algorithm combines two sorted halves of an array into a single sorted array?
- Divide
- Merge
- Sort
- Split
The step of the merge sort algorithm that combines two sorted halves of an array into a single sorted array is the "Merge" step. In this phase, the sorted subarrays are merged to produce a larger sorted array.
Explain the concept of associativity and its role in optimizing Matrix Chain Multiplication.
- Associativity is irrelevant in Matrix Chain Multiplication and does not affect the final result.
- Associativity is only applicable in certain matrix dimensions and has limited impact on optimization.
- Associativity is the property that the result of a series of matrix multiplications is independent of the placement of parentheses. It plays a crucial role in optimizing Matrix Chain Multiplication by providing flexibility in choosing the order of multiplication, allowing for the most efficient arrangement.
- Associativity refers to the grouping of matrices in a specific order to achieve the optimal solution in Matrix Chain Multiplication.
Associativity is the property that the result of a series of matrix multiplications is independent of the placement of parentheses. In optimizing Matrix Chain Multiplication, this concept allows for flexibility in choosing the order of multiplication, enabling the algorithm to find the most efficient arrangement for minimizing computational cost.
An optimization technique for Edit Distance involves using _______ to prune unnecessary calculations.
- Binary Search
- Divide and Conquer
- Dynamic Programming
- Greedy Algorithms
An optimization technique for Edit Distance involves using dynamic programming to prune unnecessary calculations. Dynamic programming stores the results of subproblems, eliminating redundant computations and significantly improving efficiency.
The Knapsack Problem involves selecting a subset of items to maximize the _______ while ensuring that the total _______ of selected items does not exceed a given limit.
- Profit, Weight
- Weight, Profit
- Value, Size
- Size, Value
In the Knapsack Problem, the goal is to maximize the profit while ensuring that the total weight of selected items does not exceed a given limit. Therefore, the correct options are Profit for the first blank and Weight for the second blank.
Dynamic programming optimizes the time complexity of finding the Longest Palindromic Substring from _______ to _______.
- O(n log n), O(n)
- O(n), O(n^2)
- O(n^2), O(n log n)
- O(n^2), O(n^2)
Dynamic programming optimizes the time complexity of finding the Longest Palindromic Substring from O(n^2) to O(n), making the algorithm more efficient by using the results of smaller subproblems to build up to the final solution.
How can you optimize bubble sort to reduce its time complexity?
- Implement bubble sort recursively
- Increase the number of passes through the array
- Use a larger data type for array elements
- Use an optimized version with a flag to check if any swaps occurred in a pass
To optimize bubble sort and reduce its time complexity, you can use an optimized version that includes a flag to check if any swaps occurred in a pass. If no swaps occur, the array is already sorted, and the algorithm can terminate early, improving its efficiency.