Introduction to Data Structures

These flashcards cover essential definitions and concepts related to data structures and algorithms from the provided lecture notes.

Data Structure

A defined format for managing, assessing, retrieving, and storing data.

Primitive Data Structures

Basic data structures that can be operated on machine and data instructions directly, like integers and pointers.

Non-Primitive Data Structures

Data structures derived from primitive data structures, such as stacks, graphs, and trees.

Traversing

The process of visiting each element of a data structure to perform operations like searching or sorting.

Insertion

The process of adding elements to a data structure at any location.

Deletion

The removal of an element from a data structure.

Searching

Detecting the location of an element within a data structure.

Sorting

The organization of data in a particular order.

Merging

The process of combining two lists of elements to form a new list.

Pointer

A derived data type that stores the address of another variable.

Dereferencing

The process of retrieving the value stored at the location referenced by a pointer.

Dynamic Memory Allocation

A programming technique where memory is allocated during runtime, enabling flexible data structure management.

Malloc() Function

Allocates a single block of requested memory.

Calloc() Function

Allocates multiple blocks of requested memory and initializes all bytes to zero.

Realloc() Function

Changes the size of previously allocated memory.

Free() Function

Deallocates memory previously allocated with malloc() or calloc().

Union

A user-defined data type that can store different data types in the same memory location.

Algorithm

A finite sequence of instructions that can be performed in a finite amount of time.

Input Specification

The well-defined data that an algorithm needs to process to generate output.

Output Specification

The well-defined data that results from the computation of an algorithm.

Effectiveness of Algorithm

Ensures outputs are feasible with available resources and avoids unnecessary steps.

Finiteness

An algorithm should terminate after a finite number of steps.

Definiteness

Each step of the algorithm must be clearly defined and unambiguous.