Questions and Answers
What is a data structure?
A special format for storing and organizing data.
Which of the following describes a priority queue?
What is a heap?
A complete binary tree where the value of each parent node is either higher or lower than the value of its child nodes.
What is a set?
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What is a map in data structures?
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Which of the following is NOT a type of abstract data type (ADT)?
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An algorithm must have a defined input and output.
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What is the purpose of the base case in a recursive algorithm?
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What describes linear recursion?
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Which of the following is an example of tail recursion?
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What is dynamic programming most similar to?
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Study Notes
Data Structures and Algorithms
- Data structure: A specialized format for storing and organizing data.
- Two main types of data structures:
- Linear: Sequential access of elements, stored in order.
- Non-Linear: Non-sequential storage and access of elements.
Abstract Data Type (ADT)
- ADT provides a logical description of data and permitted operations without focusing on implementation details.
- Two parts of ADT:
- Operations: Define how data is manipulated (initializing, adding, accessing, removing).
- Implementation: Details of how data is represented and managed.
Common Data Structures
- Linked List: Stores elements as separate objects; allows for dynamic size.
- Stack: Last-In, First-Out (LIFO) structure; last element added is first removed.
- Queue: First-In, First-Out (FIFO) structure; first element added is first removed.
- Tree: Hierarchical structure visually representing relationships among elements.
- Priority Queue: Processes elements based on custom or natural order, differing from standard FIFO/LIFO.
- Heap: Complete binary tree with parent nodes having a higher or lower value compared to child nodes.
- Set: Collection of unique elements.
- Map: Set of ordered pairs (keys and values).
Algorithm
- A sequence of step-by-step instructions for problem-solving.
- Key characteristics of a good algorithm:
- Finiteness: Must terminate after a specified number of steps.
- Definiteness: Instructions must be clear and unambiguous.
- Input: Should accept zero or more defined data before execution.
- Output: Must produce one or more results related to the input.
- Uniqueness: Each result depends on the inputs and prior outcomes.
Recursive Algorithms
- Must have a base case to stop recurring.
- Change of state implies modification of data during execution.
- Types of recursion:
- Linear recursion: Function calls itself once per invocation (e.g., factorial).
- Tail recursion: Last action of the function is a recursive call.
- Binary recursion: Function calls itself twice in one run (e.g., Fibonacci series).
- Mutual recursion: One function calls another, forming a cycle.
Algorithm Design Paradigms
- Divide and Conquer: Breaks problems into smaller subproblems.
- Greedy Algorithms: Chooses the optimal solution at each step.
- Dynamic Programming: Similar to Divide and Conquer but reuses results from overlapping subproblems.
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Description
This quiz covers the fundamentals of data structures and algorithms, focusing on key concepts such as priority queues and heaps. Dive into the specifics of how these structures store and process data efficiently. Perfect for reinforcing your understanding of essential data structures.
