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Abstract data types (ADTs) define a data type in terms of its possible values and operations, independent of implementation details. An ADT consists of a logical form defining the data items and operations, and a physical form comprising the data structures and algorithms that implement it. Simple data types like integers and characters are ADTs with basic storage structures like memory locations and operations implemented by hardware/software. More complex ADTs require user-defined data structures to organize data in memory or files along with subroutines implementing each operation.
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Abstract Data Types
The document discusses abstract data types (ADTs). It defines an ADT as a collection of data together with a set of operations on that data. An ADT specifies what data can be stored and what operations can be performed. Simple ADTs are predefined types like integers, while complex ADTs like lists are user-defined. Lists are presented as an example complex ADT, with elements ordered in a sequence and operations to insert, find, delete and traverse elements.
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Algo>Queues
1. A queue is a first-in, first-out (FIFO) data structure where items are inserted at the rear of the queue and deleted from the front.
2. Queues can be implemented using arrays or linked lists, with operations including enqueue to add an item to the rear, and dequeue to remove an item from the front.
3. Queues have many applications where processing or accessing data in a first-come, first-served order is important, such as in operating systems, communication software, and printing.
Application of Stacks
The document discusses stacks and converting expressions between infix, postfix, and prefix notation. It provides examples and algorithms for converting infix expressions to postfix. The algorithms involve using an operator stack to determine the order of operations based on precedence rules. Operands are added to the output string and operators are pushed to the stack or popped and added to the output string based on their precedence.
Algo>Stacks
Stacks are a special type of list where insertions and deletions only occur at one end, called the top. There are two main types - static stacks have a fixed size and are implemented as arrays, while dynamic stacks can grow as needed and are implemented as linked lists. Common stack operations include push to insert at the top, pop to remove from the top, and functions to check if the stack is empty or return the top element. Stacks are useful data structures with LIFO (last in, first out) behavior and have various applications like implementing function call stacks in compilers.
Analysis of Algorithum
The document discusses algorithm analysis and asymptotic analysis. It introduces key concepts like best case, worst case, and average case running times. It explains that worst case analysis is most important and easiest to analyze. The document covers analyzing algorithms using pseudocode, counting primitive operations, and determining asymptotic running time using Big-O notation. Examples are provided to illustrate these concepts, including analyzing algorithms for finding the maximum element in an array and computing prefix averages.
Algo>ADT list & linked list
This document discusses linked lists and their implementation. It begins by defining a list as a sequence of zero or more elements of a given type that can be linearly ordered. Linked lists are introduced as a flexible data structure that uses nodes connected by pointers to dynamically allocate elements in memory. The key operations on linked lists are described, including appending, traversing, inserting, deleting nodes. Code examples are provided to implement these operations using a ListNode struct containing a data element and pointer to the next node. Functions like appendNode and displayList are demonstrated, with appendNode adding to the end of the list and displayList traversing the list to output each element.
Algo>Arrays
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Algorithum Analysis
This chapter discusses algorithm analysis and asymptotic analysis of functions. It introduces the Big-O, Theta, little-o, and little-omega notations for classifying algorithms by their growth rates. Functions can have the same rate of growth (Theta), a slower rate (little-o), or a faster rate (little-omega). Rules are provided for manipulating Big-O expressions, and typical time complexities like constant, logarithmic, linear, quadratic, and exponential functions are covered.
Sorting algorithums > Data Structures & Algorithums
The document discusses sorting algorithms including bubble sort, selection sort, insertion sort, and merge sort. It provides pseudocode and explanations of how each algorithm works. Bubble sort, selection sort, and insertion sort have O(n2) runtime and are best for small datasets, while merge sort uses a divide-and-conquer approach to sort arrays with O(n log n) runtime, making it more efficient for large datasets. Radix sort is also discussed as an alternative sorting method that is optimized for certain data types.
Sorting algos > Data Structures & Algorithums
The document describes the merge sort algorithm. Merge sort is a sorting algorithm that works by dividing an unsorted list into halves, recursively sorting each half, and then merging the sorted halves into one sorted list. It has the following steps:
1. Divide: Partition the input list into equal halves.
2. Conquer: Recursively sort each half by repeating steps 1 and 2.
3. Combine: Merge the sorted halves into one sorted output list.
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Huffman > Data Structures & Algorithums
Huffman coding is a data compression technique that uses variable-length code words to encode characters based on their frequency of occurrence. It involves building a Huffman tree from character frequencies, assigning code words by traversing the tree, and encoding the text. This results in more common characters having shorter code words, reducing the number of bits needed and allowing for smaller file sizes compared to fixed-length encodings like ASCII.
Graphs > Discrete structures , Data Structures & Algorithums
The document describes Prim's algorithm for finding a minimum spanning tree (MST) in a weighted, undirected graph. Prim's algorithm is a greedy algorithm that grows the MST by iteratively adding the closest vertex to the tree. It starts with a single vertex and adds the neighboring vertex with the lowest weight edge until all vertices are included in the tree. Pseudocode is provided to illustrate the steps of Prim's algorithm, which initializes all vertex keys to infinity except the starting vertex, then extracts the minimum key vertex and updates neighboring vertex keys if a lower weight edge is found. Examples are shown applying Prim's algorithm to find the MST of a sample graph.
Data Structures & Algorithms
This document discusses data structures and their importance in computer programming. It defines a data structure as a scheme for organizing related data that considers both the items stored and their relationships. Data structures are used to store data efficiently and allow for operations like searching and modifying the data. The document outlines common data structure types like arrays, lists, matrices, and linked lists. It also discusses abstract data types and how they are implemented through data structures. The goals of the course are to learn commonly used data structures and how to measure the costs and benefits of different structures.
Turn over
Employee turnover is defined as the movement of employees in and out of an organization. It is measured by taking the number of employees who leave in a year and dividing it by the average number of employees. There are different types of turnover, including voluntary, involuntary, functional, dysfunctional, controllable, and uncontrollable. When employees leave, it can negatively impact an organization through a loss in productivity, costs associated with replacing employees, and lowered employee morale.
Attribution Theories
Attribution theory seeks to explain how and why people make causal attributions about the behavior of others. When an employee does an unsatisfactory job, the manager will use attribution theory to form a judgment. The manager will consider various perspectives, including the employee's background and experiences (selective perception), one-sided characteristics (halo effect), performance relative to others (contrast effect), and stereotypes. An effective manager makes decisions through a directive style, understanding the context around the unsatisfactory outcome, gathering information from others, and focusing on helping the employee improve rather than just the outcome. The manager's judgment and decision can impact the employee's future job performance.
Attribution Theory
Attribution theory examines how people explain the causes of behavior. There are two types of attribution theory: internal attribution theory, which attributes a person's behavior to internal characteristics like ability or effort, and external attribution theory, which attributes behavior to outside factors such as other people or circumstances. Managers make attributions about employees' job performance through selective perception, only seeing certain attributes, and through halo and contrast effects, where they judge employees based on one trait or in comparison to others. Proper attribution is important for managers since their judgments guide decision-making that impacts employees.
Absenteeism
Absenteeism refers to regularly being absent from work or other obligations without good reason. There are two types of absenteeism: voluntary and involuntary. Involuntary absenteeism involves being absent due to unavoidable reasons like illness or family emergencies. Voluntary absenteeism is deliberate absence without a valid excuse. Employers measure absenteeism by tracking the number of days lost to absence over time. Controlling voluntary absenteeism involves disciplinary approaches, positive reinforcement, or combination approaches, as well as no fault policies and paid time off programs.
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The document discusses turnover in organizations. It defines turnover as the process where employees leave an organization and must be replaced. It outlines six types of turnover: involuntary, voluntary, functional, dysfunctional, uncontrollable, and controllable. Involuntary turnover involves termination for poor performance. Voluntary turnover occurs when an employee chooses to leave.
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Algo>Abstract data type
- 2. 2 Abstract Data Types Abstract Data Type (ADT): a definition for a data type solely in terms of a set of values and a set of operations on that data type. Each ADT operation is defined by its inputs and outputs. ENCAPSULATION: Hide Implementation details
- 3. 3 Def. e.g. whole numbers (integers) and arithmetic operators for addition, subtraction, multiplication and division. e.g. Flight reservation Basic operations: find empty seat, reserve a seat, cancel a seat assignment Why "abstract?" Data, operations, and relations are studied independent of implementation. What not how is the focus. a collection of related data items together with an associated set of operations Abstract Data Types
- 4. 4 Def. Consists of storage structures (data structures) to store the data items and algorithms for the basic operations. The storage structures/data structures used in implementations are provided in a language (primitive or built-in) or are built from the language constructs (user-defined). In either case, successful software design uses data abstraction: Separating the definition of a data type from its implementation. Abstract Data Types
- 5. 5 Data Structure A data structure is the physical implementation of an ADT. Each operation associated with the ADT is implemented by one or more subroutines in the implementation. Data structure usually refers to an organization of data in main memory. File structure is an organization for data on peripheral storage, such as a disk drive.
- 6. 6 Data Type ADT: Type Operations Data Items: Logical Form Data Items: Physical Form Data Structure: Storage Space Subroutines
- 7. 7 Simple Data Types Memory: 2-state devices ↔ bits 0 and 1 Organized into bytes (8 bits) and words (machine dependent — e.g., 2 bytes). Each byte (or word) has an address making it possible to store and retrieve contents of any given memory location. Therefore: the most basic form of data: sequences of bits simple data types (values are atomic — can't be subdivided) are ADTs. Implementations have: » Storage structures: memory locations » Algorithms: system hardware/software to do basic operations.
- 8. 8 Boolean data Data values: {false, true} In C/C++: false = 0, true = 1 (or nonzero) Operations: and && or || not ! x !x 0 1 1 0 && 0 1 0 0 0 1 0 1 | | 0 1 0 0 1 1 1 1
- 9. 9 Character Data Store numeric codes (ASCII, EBCDIC, Unicode) 1 byte for ASCII and EBCDIC, 2 bytes for Unicode Basic operation: comparison to determine ifEqual, Less than ,Greater than, etc. use their numeric codes (i.e. use ordinal value) ASCII/EBCDIC Unicode
- 10. 10 Integer Data Non-negative (unsigned) integer: Store its base-two representation in a fixed number w of bits (e.g., w = 16 or w = 32) 88 = 00000000010110002 Signed integer: Store in a fixed number w of bits using one of the following representations:
- 11. 11 Sign-magnitude representation Save one bit (usually most significant) for sign (0 = +, 1 = – ) Use base-two representation in the other bits. 88 → _0000000010110000 ↑ sign bit Cumbersome for arithmetic computations –88 → _000000001011000 ↓ 1
- 12. 12 Two's complement representation For negative n (–n): (1) Find w-bit base-2 representation of n (2) Complement each bit. (3) Add 1 Example: –88 1. 88 as a 16-bit base-two number 0000000001011000 Same as sign mag.For nonnegative n: Use ordinary base-two representation with leading (sign) bit 0 2. Complement this bit string 3. Add 1 1111111110100111 1111111110101000 (Flip all bits from rightmost 0 to the end)