Data Structures Handout 1 & 2 Review

Questions and Answers

What is a characteristic that distinguishes a priority queue from a standard queue?

Elements are processed based on a custom-defined order. (correct)

Elements are stored in a set.

Elements are processed based on their input time.

Elements are accessed in a linear fashion only.

Which of the following describes a heap structure?

A complete binary tree where each parent node's value is either higher or lower than its children. (correct)

A set of ordered pairs without any specific structure.

A collection of elements with no repeated values.

A complete binary tree with unordered parent-child relationships.

What defines the abstract approach of an Abstract Data Type (ADT)?

The internal structure is more important than the external operations.

The operations allowed are defined without regard to their implementation. (correct)

The focus is solely on the data storage mechanism.

The data structure implementation must be defined explicitly.

Which operation is NOT considered a main operation of an Abstract Data Type (ADT)?

Sorting Signup and view all the answers

What is NOT a characteristic of an algorithm as defined in the content?

Generality Signup and view all the answers

What characteristic defines a stack data structure?

It retrieves the last element added first. Signup and view all the answers

What is the primary feature that distinguishes dynamic programming from divide and conquer algorithms?

It actively reuses the results of overlapping subproblems. Signup and view all the answers

Which type of recursion is characterized by a function calling itself multiple times?

Binary recursion Signup and view all the answers

What is required for a recursive algorithm to function properly?

A base case to terminate the recursion. Signup and view all the answers

In the context of algorithm design paradigms, what does a greedy algorithm ensure?

It always chooses the optimal solution at each step. Signup and view all the answers

Study Notes

Fundamentals of Data Structures and Algorithms

Data structures are specialized formats for storing and organizing data.
Algorithms provide a step-by-step sequence of instructions for problem-solving.

Types of Data Structures

Linear Data Structures: Elements accessed sequentially.
Non-Linear Data Structures: Elements accessed non-sequentially.

Abstract Data Types (ADT)

ADTs specify the logical view of data and operations allowed without implementation details.
Two parts of an ADT:
- Public/External: Operations for adding, accessing, and removing data.
- Private/Internal: Implementation details representing data structurally.

Key Data Structures

Linked List: Stores elements as separate objects.
Stack: Last-In, First-Out (LIFO) structure.
Queue: First-In, First-Out (FIFO) structure.
Tree: Hierarchical structure represented graphically.
Set: Collection of unique elements.
Map: Set of ordered pairs, known as keys (identifiers) and values (content).
Graph: Consists of vertices (nodes) and edges (relations).

Characteristics of an Algorithm

Finiteness: Must terminate after a specified number of steps.
Definiteness: Each instruction must be clear and unambiguous.
Input: Should have zero or more well-defined data before execution.
Output: Must produce one or more results related to the input.
Uniqueness: Each step's result depends on input and prior results.

Recursive Algorithms

Recursive algorithms call themselves to solve problems.
Base Case: Condition for terminating recursion.
Change of State: Data modification during recursion.

Types of Recursion

Linear Recursion: Function calls itself once (e.g., factorial).
Tail Recursion: Last operation is a recursive call (e.g., GCD).
Binary Recursion: Function calls itself twice (e.g., Fibonacci series).
Mutual Recursion: Functions call each other in pairs (e.g., even/odd checks).

Recursion vs. Iteration

Recursion: A function calling itself requires a base case to terminate.
Iteration: Involves repeating instructions; terminates when conditions are met.
Recursive calls consume additional memory; iteration does not.
Infinite recursion can lead to memory issues, while infinite loops can occur without extra memory constraints.

Algorithm Design Paradigms

Divide and Conquer: Problems are divided into smaller subproblems.
Greedy Algorithms: Optimal choices are made at each step.
Dynamic Programming: Subproblem results are reused for efficiency on overlapping problems.

Description

This quiz encompasses key concepts from handouts 1 and 2 on Data Structures and Algorithms. It covers essential topics like priority queues and heaps, emphasizing their definitions and structures. Test your understanding of these fundamental data structures and their applications.