Database structure Structures Link list and trees and Recurison complete Database structure Structures Link list and trees and Recurison complete Database structure Structures Link list and trees and Recurison complete Database structure Structures Link list and trees and Recurison complete Database structure Structures Link list and trees and Recurison complete
Database structure Structures Link list and trees and Recurison complete Database structure Structures Link list and trees and Recurison complete Database structure Structures Link list and trees and Recurison complete
This document provides an overview of data structures and algorithms. It introduces common linear data structures like stacks, queues, and linked lists. It discusses the need for abstract data types and different data types. It also covers implementing stacks as a linked list and common stack operations. Key applications of stacks include function call stacks which use a LIFO structure to remember the order of function calls and returns.
1.History of C Language, Structure of a C program, Statements, Basic Data Types, Variables &Constants, Input & Output statements, Operators and Precedence, Expressions, Simple C programs.
Linear search examines each element of a list sequentially, one by one, and checks if it is the target value. It has a time complexity of O(n) as it requires searching through each element in the worst case. While simple to implement, linear search is inefficient for large lists as other algorithms like binary search require fewer comparisons.
1. Data structures organize data in memory for efficient access and processing. They represent relationships between data values through placement and linking of the values.
2. Algorithms are finite sets of instructions that take inputs, produce outputs, and terminate after a finite number of unambiguous steps. Common data structures and algorithms are analyzed based on their time and space complexity.
3. Data structures can be linear, with sequential elements, or non-linear, with branching elements. Abstract data types define operations on values independently of implementation through inheritance and polymorphism.
This document discusses linked lists and polynomials represented as linked lists. It provides details on singly linked lists, including how to implement insertion and deletion of nodes. It also describes how to represent stacks and queues as dynamically linked lists. Finally, it discusses representing polynomials using arrays or linked lists, and how to perform addition and multiplication of polynomials in each representation.
This document provides an overview of data structures and algorithms. It introduces common linear data structures like stacks, queues, and linked lists. It discusses the need for abstract data types and different data types. It also covers implementing stacks as a linked list and common stack operations. Key applications of stacks include function call stacks which use a LIFO structure to remember the order of function calls and returns.
1.History of C Language, Structure of a C program, Statements, Basic Data Types, Variables &Constants, Input & Output statements, Operators and Precedence, Expressions, Simple C programs.
Linear search examines each element of a list sequentially, one by one, and checks if it is the target value. It has a time complexity of O(n) as it requires searching through each element in the worst case. While simple to implement, linear search is inefficient for large lists as other algorithms like binary search require fewer comparisons.
1. Data structures organize data in memory for efficient access and processing. They represent relationships between data values through placement and linking of the values.
2. Algorithms are finite sets of instructions that take inputs, produce outputs, and terminate after a finite number of unambiguous steps. Common data structures and algorithms are analyzed based on their time and space complexity.
3. Data structures can be linear, with sequential elements, or non-linear, with branching elements. Abstract data types define operations on values independently of implementation through inheritance and polymorphism.
This document discusses linked lists and polynomials represented as linked lists. It provides details on singly linked lists, including how to implement insertion and deletion of nodes. It also describes how to represent stacks and queues as dynamically linked lists. Finally, it discusses representing polynomials using arrays or linked lists, and how to perform addition and multiplication of polynomials in each representation.
This document discusses algorithms and their complexity. It provides an example of a linear search algorithm to find a target value in an array. The complexity of this algorithm is analyzed for the worst and average cases. In the worst case, the target is the last element and all n elements must be checked, resulting in O(n) time complexity. On average, about half the elements (n+1)/2 need to be checked, resulting in average time complexity of O(n).
This document provides an overview of the Data Structures I course. It outlines the course objectives of becoming familiar with problem solving, algorithms, data structures, and tracing algorithms. The course will cover fundamentals of data structures and algorithms, static and dynamic data structures, searching and sorting algorithms, recursion, abstract data types, stacks, queues and trees. Exams, labs, participation and quizzes will be used for grading. Pseudo-code is introduced as a way to express algorithms independent of a programming language. Examples of algorithms for determining even/odd numbers and computing weekly wages are provided.
Introduction to datastructure and algorithmPratik Mota
Introduction to data structure and algorithm
-Basics of Data Structure and Algorithm
-Practical Examples of where Data Structure Algorithms is used
-Asymptotic Notations [ O(n), o(n), θ(n), Ω(n), ω(n) ]
-Calculation of Time and Space Complexity
-GNU gprof basic
This document provides an introduction to data structures and algorithms. It discusses key concepts like abstract data types (ADTs), different types of data structures including linear and non-linear structures, analyzing algorithms to assess efficiency, and selecting appropriate data structures based on required operations and resource constraints. The document also covers topics like classifying data structures, properties of algorithms, analyzing time and space complexity, and examples of iterative and recursive algorithms and their complexity analysis.
This document introduces key concepts in data structures and algorithms. It defines an algorithm as a set of instructions to accomplish a task, and discusses how algorithms are evaluated based on their time and space complexity. It then defines data structures as organized data with relationships and permitted operations, and lists common examples like arrays, stacks and queues. The document also introduces abstract data types as mathematical models with defined operations.
The document discusses recursion, which is when a function calls itself. It provides examples of recursive algorithms, types of recursion including single, multiple, direct, indirect, anonymous, and structural recursion. It also discusses recursive functions like the factorial function, tail recursion, and mutually recursive functions. Additionally, it covers recursive data types, the order of execution in recursive functions, and how recursion uses the call stack in memory.
Iteration uses loops to repeatedly execute a block of code a specified number of times or until a condition is met. Recursion provides an alternative approach by having a function call itself repeatedly until a base case is reached. Both approaches can solve problems but recursion uses more memory than iteration due to its use of function calls and stack. Whether to use iteration or recursion depends on the specific problem and considerations of processing speed and memory usage.
This document discusses various applications of common data structures like linked lists, stacks, queues, and trees. It provides examples of how linked lists are used to implement queues and stacks, and in web browsers to store browsing history. It also gives examples of how stacks can be used for reversing words, undo/redo functions, matching parentheses in compilers, and modeling real-world examples like plates in a cupboard. Applications of queues include asynchronous data transfer and resource sharing. Trees are used in operating systems to represent folder structures, in HTML for the document object model, for network routing, syntax trees in compilers, and modeling game moves in AI.
The document provides an overview of common topics that confuse new C programmers, including control structures, variable types, pointers, arrays, structs, linked lists, and recursion. It discusses each concept in 1-3 paragraphs, explaining what they are and providing basic examples. It also covers debugging strategies such as printing variables, reducing complexity, and inserting early returns to isolate issues.
The document discusses recursion, including:
1) Recursion is a programming technique where a method calls itself to solve a problem. It involves a base case and recursive calls.
2) Examples of recursive definitions and programs are given, such as computing factorials and sums recursively.
3) Recursion can be direct, where a method calls itself, or indirect through multiple method calls eventually leading back to the original method.
1. The Python if statement is used to execute code conditionally based on the evaluation of an expression. It can be used with simple conditions, if-else statements, and nested if statements.
2. Python if-else statements allow executing one block of code if a condition is true and another block if the condition is false.
3. Nested if statements in Python allow checking multiple conditions, with inner if statements executing only if the outer conditions are met.
The document discusses list data structures and their implementation using arrays and linked memory. It describes common list operations like insertion, removal, searching, and provides examples of how to implement them with arrays and linked lists. Key list operations include adding and removing elements from different positions, accessing elements by index or pointer, and traversing the list forward and backward. Linked lists offer more flexibility than arrays by not requiring predefined memory allocation.
The document provides an overview of data structures and algorithms. It discusses key topics like:
1) Different types of data structures including primitive, linear, non-linear, and arrays.
2) The importance of algorithms and how to write them using steps, comments, variables, and control structures.
3) Common operations on data structures like insertion, deletion, searching, and sorting.
4) Different looping and selection statements that can be used in algorithms like for, while, and if-then-else.
5) How arrays can be used as a structure to store multiple values in a contiguous block of memory.
Introduction to data structures and AlgorithmDhaval Kaneria
This document provides an introduction to algorithms and data structures. It defines algorithms as step-by-step processes to solve problems and discusses their properties, including being unambiguous, composed of a finite number of steps, and terminating. The document outlines the development process for algorithms and discusses their time and space complexity, noting worst-case, average-case, and best-case scenarios. Examples of iterative and recursive algorithms for calculating factorials are provided to illustrate time and space complexity analyses.
this is a briefer overview about the Big O Notation. Big O Notaion are useful to check the Effeciency of an algorithm and to check its limitation at higher value. with big o notation some examples are also shown about its cases and some functions in c++ are also described.
This document provides an overview of data structures and algorithms. It discusses key concepts like interfaces, implementations, time complexity, space complexity, asymptotic analysis, and common control structures. Some key points:
- A data structure organizes data to allow for efficient operations. It has an interface defining operations and an implementation defining internal representation.
- Algorithm analysis considers best, average, and worst case time complexities using asymptotic notations like Big O. Space complexity also measures memory usage.
- Common control structures include sequential, conditional (if/else), and repetitive (loops) structures that control program flow based on conditions.
The document discusses strings, arrays, pointers, and sorting algorithms in C programming. It provides definitions and examples of:
1) Strings as null-terminated character arrays. It demonstrates initializing and printing a string.
2) One-dimensional and two-dimensional arrays. It shows how to declare, initialize, access, and print multi-dimensional arrays.
3) Pointers as variables that store memory addresses. It explains pointer declaration and dereferencing pointers using asterisk (*) operator.
4) Bubble sort algorithm that iterates through an array and swaps adjacent elements if out of order, putting largest elements at the end of the array in each iteration.
This document is a dissertation that describes a case study of a virtual language learning community called Writing Through the Internet (WTI). The study was conducted through participant observation of the online class, interviews with selected students, and analysis of publicly available documents.
Key findings included insights about the importance of multiple means of interaction in virtual classes, the inevitability of change in virtual communities, the effort required to develop and maintain language learning communities, challenges of virtual interviews related to context and time, and the need to adjust plans based on students' varying technological capabilities.
Recommendations for online teachers and researchers centered around having backup plans, establishing clear rules, allowing sufficient time, considering time zone differences, providing multiple but optional means of
This document contains the slides for a presentation on Java 8 Lambdas and Streams. The presentation will cover lambdas, including their concept, syntax, functional interfaces, variable capture, method references, and default methods. It will also cover streams. The slides provide some incomplete definitions that will be completed during the presentation. Questions from attendees are welcome. A quick survey asks about past experience with lambdas and streams.
This document discusses algorithms and their complexity. It provides an example of a linear search algorithm to find a target value in an array. The complexity of this algorithm is analyzed for the worst and average cases. In the worst case, the target is the last element and all n elements must be checked, resulting in O(n) time complexity. On average, about half the elements (n+1)/2 need to be checked, resulting in average time complexity of O(n).
This document provides an overview of the Data Structures I course. It outlines the course objectives of becoming familiar with problem solving, algorithms, data structures, and tracing algorithms. The course will cover fundamentals of data structures and algorithms, static and dynamic data structures, searching and sorting algorithms, recursion, abstract data types, stacks, queues and trees. Exams, labs, participation and quizzes will be used for grading. Pseudo-code is introduced as a way to express algorithms independent of a programming language. Examples of algorithms for determining even/odd numbers and computing weekly wages are provided.
Introduction to datastructure and algorithmPratik Mota
Introduction to data structure and algorithm
-Basics of Data Structure and Algorithm
-Practical Examples of where Data Structure Algorithms is used
-Asymptotic Notations [ O(n), o(n), θ(n), Ω(n), ω(n) ]
-Calculation of Time and Space Complexity
-GNU gprof basic
This document provides an introduction to data structures and algorithms. It discusses key concepts like abstract data types (ADTs), different types of data structures including linear and non-linear structures, analyzing algorithms to assess efficiency, and selecting appropriate data structures based on required operations and resource constraints. The document also covers topics like classifying data structures, properties of algorithms, analyzing time and space complexity, and examples of iterative and recursive algorithms and their complexity analysis.
This document introduces key concepts in data structures and algorithms. It defines an algorithm as a set of instructions to accomplish a task, and discusses how algorithms are evaluated based on their time and space complexity. It then defines data structures as organized data with relationships and permitted operations, and lists common examples like arrays, stacks and queues. The document also introduces abstract data types as mathematical models with defined operations.
The document discusses recursion, which is when a function calls itself. It provides examples of recursive algorithms, types of recursion including single, multiple, direct, indirect, anonymous, and structural recursion. It also discusses recursive functions like the factorial function, tail recursion, and mutually recursive functions. Additionally, it covers recursive data types, the order of execution in recursive functions, and how recursion uses the call stack in memory.
Iteration uses loops to repeatedly execute a block of code a specified number of times or until a condition is met. Recursion provides an alternative approach by having a function call itself repeatedly until a base case is reached. Both approaches can solve problems but recursion uses more memory than iteration due to its use of function calls and stack. Whether to use iteration or recursion depends on the specific problem and considerations of processing speed and memory usage.
This document discusses various applications of common data structures like linked lists, stacks, queues, and trees. It provides examples of how linked lists are used to implement queues and stacks, and in web browsers to store browsing history. It also gives examples of how stacks can be used for reversing words, undo/redo functions, matching parentheses in compilers, and modeling real-world examples like plates in a cupboard. Applications of queues include asynchronous data transfer and resource sharing. Trees are used in operating systems to represent folder structures, in HTML for the document object model, for network routing, syntax trees in compilers, and modeling game moves in AI.
The document provides an overview of common topics that confuse new C programmers, including control structures, variable types, pointers, arrays, structs, linked lists, and recursion. It discusses each concept in 1-3 paragraphs, explaining what they are and providing basic examples. It also covers debugging strategies such as printing variables, reducing complexity, and inserting early returns to isolate issues.
The document discusses recursion, including:
1) Recursion is a programming technique where a method calls itself to solve a problem. It involves a base case and recursive calls.
2) Examples of recursive definitions and programs are given, such as computing factorials and sums recursively.
3) Recursion can be direct, where a method calls itself, or indirect through multiple method calls eventually leading back to the original method.
1. The Python if statement is used to execute code conditionally based on the evaluation of an expression. It can be used with simple conditions, if-else statements, and nested if statements.
2. Python if-else statements allow executing one block of code if a condition is true and another block if the condition is false.
3. Nested if statements in Python allow checking multiple conditions, with inner if statements executing only if the outer conditions are met.
The document discusses list data structures and their implementation using arrays and linked memory. It describes common list operations like insertion, removal, searching, and provides examples of how to implement them with arrays and linked lists. Key list operations include adding and removing elements from different positions, accessing elements by index or pointer, and traversing the list forward and backward. Linked lists offer more flexibility than arrays by not requiring predefined memory allocation.
The document provides an overview of data structures and algorithms. It discusses key topics like:
1) Different types of data structures including primitive, linear, non-linear, and arrays.
2) The importance of algorithms and how to write them using steps, comments, variables, and control structures.
3) Common operations on data structures like insertion, deletion, searching, and sorting.
4) Different looping and selection statements that can be used in algorithms like for, while, and if-then-else.
5) How arrays can be used as a structure to store multiple values in a contiguous block of memory.
Introduction to data structures and AlgorithmDhaval Kaneria
This document provides an introduction to algorithms and data structures. It defines algorithms as step-by-step processes to solve problems and discusses their properties, including being unambiguous, composed of a finite number of steps, and terminating. The document outlines the development process for algorithms and discusses their time and space complexity, noting worst-case, average-case, and best-case scenarios. Examples of iterative and recursive algorithms for calculating factorials are provided to illustrate time and space complexity analyses.
this is a briefer overview about the Big O Notation. Big O Notaion are useful to check the Effeciency of an algorithm and to check its limitation at higher value. with big o notation some examples are also shown about its cases and some functions in c++ are also described.
This document provides an overview of data structures and algorithms. It discusses key concepts like interfaces, implementations, time complexity, space complexity, asymptotic analysis, and common control structures. Some key points:
- A data structure organizes data to allow for efficient operations. It has an interface defining operations and an implementation defining internal representation.
- Algorithm analysis considers best, average, and worst case time complexities using asymptotic notations like Big O. Space complexity also measures memory usage.
- Common control structures include sequential, conditional (if/else), and repetitive (loops) structures that control program flow based on conditions.
The document discusses strings, arrays, pointers, and sorting algorithms in C programming. It provides definitions and examples of:
1) Strings as null-terminated character arrays. It demonstrates initializing and printing a string.
2) One-dimensional and two-dimensional arrays. It shows how to declare, initialize, access, and print multi-dimensional arrays.
3) Pointers as variables that store memory addresses. It explains pointer declaration and dereferencing pointers using asterisk (*) operator.
4) Bubble sort algorithm that iterates through an array and swaps adjacent elements if out of order, putting largest elements at the end of the array in each iteration.
This document is a dissertation that describes a case study of a virtual language learning community called Writing Through the Internet (WTI). The study was conducted through participant observation of the online class, interviews with selected students, and analysis of publicly available documents.
Key findings included insights about the importance of multiple means of interaction in virtual classes, the inevitability of change in virtual communities, the effort required to develop and maintain language learning communities, challenges of virtual interviews related to context and time, and the need to adjust plans based on students' varying technological capabilities.
Recommendations for online teachers and researchers centered around having backup plans, establishing clear rules, allowing sufficient time, considering time zone differences, providing multiple but optional means of
This document contains the slides for a presentation on Java 8 Lambdas and Streams. The presentation will cover lambdas, including their concept, syntax, functional interfaces, variable capture, method references, and default methods. It will also cover streams. The slides provide some incomplete definitions that will be completed during the presentation. Questions from attendees are welcome. A quick survey asks about past experience with lambdas and streams.
Java 8 Stream API and RxJava ComparisonJosé Paumard
The slides of my JavaOne talk: Java 8 Stream API and RxJava Comparison: Patterns and Performances.
The spliterators patterns can be found here: https://github.com/JosePaumard/jdk8-spliterators.
A notification is a short message briefly displayed on the status line to announce an event. Notifications can take different forms like icons in the status bar or alerts. The NotificationManager class notifies users of background events. An example shows creating a notification with the NotificationBuilder, displaying it, and canceling it. When clicked, a PendingIntent launches an activity to attend to the notification.
Functional programming in Java 8 allows for:
1) The use of lambda expressions and streams for a more declarative programming style without iteration.
2) Functional interfaces like Function, Predicate, and Consumer along with lambda expressions enable function composition and chaining.
3) Features like immutable collections help reduce mutability and side effects, important aspects of functional programming.
Functional programming has started (re)gaining prominence in recent years, and with good reason too. Functional programs lend an elegant solution to the concurrency problem, result in more modular systems, are more concise and are easier to test. While modern languages like Scala and Clojure have embraced the functional style whole-heartedly, Java has lagged a bit behind in its treatment of functions as first-class citizens. With the advent of Java 8 and its support for lambdas, however, Java programmers can finally start reaping the power of functional programs as well. Even without Java 8, it is possible to adopt a functional style with the aid of excellent libraries such as Guava.
Java 8 supports lambdas. It's API also comes with Streams support.
But Knowing some concepts on Functional Programming may help you get a lot more from what this new version of Java has to offer.
Functional programming is all the rage. It can undoubtedly produce cleaner and more expressive code, but why switch to Scala or wait for Java 8? In fact, there are many ways to dramatically improve your Java code quality by using a more functional style today. This presentation discusses how techniques such as functional programming, using fluent APIs, and other clean coding practices can make your code more concise, more readable, and much easier to maintain. But more importantly, you will learn about libraries such as LambdaJ and Google Guava that make this possible today with your existing Java code base.
L'API la plus utilisée du JDK est sans aucun doute l'API Collection. Brillamment conçue il y a un peu plus de 15 ans, elle est encore aujourd'hui au coeur de toutes les applications Java. En 2004, elle a subi son premier lifting, avec l'introduction des génériques. Cette mise à jour, bien qu'importante, n'a cependant pas modifié ses patterns d'utilisation. Avec l'introduction des lambdas en Java 8, l'API Collection est à nouveau réécrite, mais cette fois la situation est différente : ses patterns d'utilisation sont complètement changés.
La première partie de cette conférence introduit les lambda expressions, comment les écrire, et ce qu'elle nous apportent en tant que développeurs. La deuxième partir présente en détail les nouveaux patterns introduits par les API Stream et Collector. Ces nouvelles API vont changer la façon dont nous allons pouvoir traiter les collections de grande taille, y compris en parallèle, avec un modèle de programmation très simple, et des patterns très puissants. Cette puissance sera montrée dans des exemples réels, qui monteront comment Java 8 va pouvoir nous aider à écrire simplement du code efficace et performant.
In this Meetup Victor Perepelitsky - R&D Technical Leader at LivePerson leading the 'Real Time Event Processing Platform' team , will talk about Java 8', 'Stream API', 'Lambda', and 'Method reference'.
Victor will clarify what functional programming is and how can you use java 8 in order to create better software.
Victor will also cover some pain points that Java 8 did not solve regarding functionality and see how you can work around it.
Java 8, Streams & Collectors, patterns, performances and parallelizationJosé Paumard
This document discusses Java 8 streams and collectors. It provides an overview of streams, operations on streams like forEach(), filter(), and peek(), and functional interfaces like Consumer and Predicate. It notes that streams efficiently process data in a parallel and pipelined manner without storing intermediate data. Key points covered include what streams are, how to create them from collections, common stream operations, and issues around concurrency with mutable operations.
Alphorm.com Formation Autodesk Revit 2018 : Les nouveautésAlphorm
Formation complète ici:
http://www.alphorm.com/tutoriel/formation-en-ligne-autodesk-revit-2018-les-nouveautes
Autodesk Revit fait sans doute partie des logiciels d’architecture 3D les plus performants actuellement. Il dispose d'une large gamme de fonctionnalités permettant de produire des images 3D de qualité.
Autodesk Revit est un puissant logiciel de traitement d'images 3D. Plus précisément, le programme permet de concevoir, de visualiser, et bien entendu de modifier des créations en architecture 3D. Il a été conçu pour être bien précis dans ses œuvres, de l'esquisse au crayon à la vitesse et la flexibilité de l'application.
Cette formation Revit 2018 vous permettra de maîtriser les nouveautés de Revit 2018, le logiciel d'architecture 3D, utilisé par de nombreux passionnés et professionnels.
Au cours de cette formation Revit 2018, vous aborderez les nouveautés concernant les améliorations de textes, la création des escaliers multi-étages, l’hébergement des garde-corps sur une topographie, la fonctionnalité de liaison d’un fichier Navisworks sur Revit, les améliorations concernant la création des garde-corps par esquisse.
Avec cette formation Revit 2018 les nouveautés, vous apprendrez à utiliser les nouveautés du logiciel de manière optimale afin de devenir un Spécialiste sur Revit 2018.
Formation complète ici:
http://www.alphorm.com/tutoriel/formation-en-ligne-java-server-faces
Nous rappelons tout d’abord les technologies JSP et Servlet, qui servent de base à JSF. Avec JSP et Servlet, nous créons des applications Web dynamiques. Puis, en nous appuyant sur les Servlets, nous abordons JSF. Ce qui caractérise les pages JSF, c’est l’absence totale de code Java et la présence de balises spécifiques, les facelets. Pour faire le lien entre les pages HTML et les composants Java, nous présentons Expression Language, qui permet d’écrire des expressions qui lisent ou écrivent les données.
Ensuite, nous détaillons certains composants essentiels à la construction des pages Web, et après ce chapitre, vous êtes capable de créer une interface riche et agréable à l’utilisateur. Ces composants ont ou pas un équivalent HTML. Ces composants graphiques sont ensuite accompagnés de convertisseurs, listeners et de validateurs qui permettront d’éviter de coder les règles élémentaires liées à la saisie et à la modification des données.
Nous n’oublions pas le code Java, car les beans managés ont leur chapitre, afin de préciser les règles à suivre pour qu’ils s’intègrent parfaitement dans l’application. Plusieurs choix s’offrent à nous, nous choisissons le plus pertinent.
Pour améliorer le rendu et la fluidité de l’interface, nous utiliserons Ajax, et nous montrerons comment il est simple d’introduire cette technologie dans les pages JSF. Avec Ajax, qui utilise donc du JavaScript, nous obtiendrons des mises à jour plus discrètes et plus rapides.
Enfin, nous finirons par décrire les détails de configuration concernant les beans, l’internationalisation, les flux et la sécurité. La configuration repose pour une bonne part sur l’écriture de fichiers XML, et par ailleurs beaucoup sur le respect de règles et de conventions. Le paramétrage sera abordé en prenant comme exemple GlassFish.
The document discusses various topics related to C++, data structures, algorithms, and functions. It covers atomic variables in C++, pointers, arrays, structures, functions, recursion, and searching techniques. It also discusses complexity of algorithms. Some key topics include linear and binary search, recursion types like linear, tail, and mutual recursion, and analyzing complexity of algorithms.
This document contains information about data structures and algorithms taught in a university course. It includes:
- A list of textbook, grading criteria like tests and projects, and software used like C/C++ editors.
- Descriptions of basic concepts like functions, recursion, and searching algorithms. Function topics include parameter passing, resolving references, and stack implementation. Recursion covers writing recursive functions and types. Searching covers linear and binary search.
- Examples of recursion problems and their solutions, like calculating sums, finding elements in arrays, and printing patterns like triangles. It also provides exercises on recursion and searching techniques.
C++ is an object-oriented programming language created by Bjarne Stroustrup in 1985 that maintains aspects of C while adding object-oriented features like classes. C++ can be used to create everything from small programs to large applications and is a powerful general-purpose language. Inheritance allows code reusability by creating new classes from existing classes or base classes, where derived classes inherit capabilities from base classes but can add their own features and refinements. Polymorphism enables objects to react differently to the same function call through virtual functions, allowing different classes that inherit from a base class to provide their own implementation of a function.
C++ (pronounced "see plus plus") is a computer programming language based on C. It was created for writing programs for many different purposes. In the 1990s, C++ became one of the most used programming languages in the world.
The C++ programming language was developed by Bjarne Stroustrup at Bell Labs in the 1980s, and was originally named "C with classes". The language was planned as an improvement on the C programming language, adding features based on object-oriented programming. Step by step, a lot of advanced features were added to the language, like operator overloading, exception handling and templates.
C++ is an object-oriented programming language created by Bjarne Stroustrup in 1985 that maintains aspects of C while adding object-oriented features like classes. C++ can be used to create small programs or large applications across many domains. Key concepts covered include functions, classes, inheritance, polymorphism, and memory management techniques like realloc() and free().
This document contains information about a Data Structures and Algorithms course taught by Associate Professor Yusuf Sahillioğlu at Middle East Technical University. It provides details about the course instructor, textbook, grading breakdown, course outline covering topics like sorting, lists, trees and graphs, and motivational examples demonstrating how data structures can be used to efficiently store and process data. It also includes an introduction to programming in C++ covering object-oriented concepts like classes, objects, encapsulation and information hiding.
This document contains information about a Data Structures and Algorithms course taught by Professor Yusuf Sahillioğlu at Middle East Technical University. It provides details about the course objectives, textbook, grading breakdown, course outline covering topics like sorting, lists, trees and graphs, and motivational examples demonstrating how data structures can be used to efficiently store and process data. It also introduces some basic C++ concepts like classes, objects, encapsulation and information hiding that will be used in the course.
Y. N. D. Aravind presents on functions in C programming. The presentation covers:
- The objectives of functions, parameters, arrays, and recursion.
- The definition of a function as reusable block of code that performs a specific task.
- The four categories of functions based on arguments and return values.
- Passing arguments to functions by value (copying) versus by reference (address).
The document discusses different types of functions in C programming. It begins by explaining what functions are and their basic components like function name, arguments, return type, etc. It then describes the four categories of functions:
1) Functions with no arguments and no return values
2) Functions with arguments but no return values
3) Functions with arguments and return values
4) Functions with no arguments but return values
Examples of each category are provided to illustrate how they work. The document also covers other topics like library functions, user-defined functions, and differences between local and global variables.
Multidimensional arrays store data in tabular form with multiple indices. A 3D array declaration would be datatype arrayName[size1][size2][size3]. Elements can be initialized and accessed similar to 2D arrays but with additional nested brackets and loops for each dimension. Functions allow dividing a problem into smaller logical parts. Functions are defined with a return type, name, parameters and body. Arguments are passed by value or reference and arrays can also be passed to functions. Recursion occurs when a function calls itself, requiring a base case to terminate the recursion.
This document provides an overview of various data structures and algorithms implemented in C++. It begins with introductions to fundamental C++ concepts like classes, inheritance, templates, and pointers. It then covers common linear data structures like linked lists, stacks, and queues. For each, it provides high-level explanations of their properties and implementations using both linked lists and arrays. It also briefly introduces trees, binary trees, binary search trees, and other more advanced topics like graphs and graph algorithms. The document is intended as a set of prelecture notes to introduce key concepts before more in-depth lectures and implementations.
The document outlines an advanced Python course covering various Python concepts like object orientation, comprehensions, extended arguments, closures, decorators, generators, context managers, classmethods, inheritance, encapsulation, operator overloading, and Python packages. The course agenda includes how everything in Python is an object, comprehension syntax, *args and **kwargs, closures and decorators, generators and iterators, context managers, staticmethods and classmethods, inheritance and encapsulation, operator overloading, and Python package layout.
Functions, classes, and objects are fundamental concepts in object-oriented programming. Here's a brief explanation of each:
Functions:
Functions are blocks of code that perform specific tasks or computations.
They encapsulate a set of instructions and can take parameters (input) and return values (output).
Functions are reusable, promoting code modularity and maintainability.
Classes:
Classes are blueprints or templates for creating objects.
They define the structure and behavior of objects by specifying attributes (data members) and methods (functions) that the objects will have.
Classes serve as a model for creating multiple instances (objects) with similar characteristics.
Objects:
Objects are instances of classes.
They are concrete representations of the class's blueprint, with their own unique data values and the ability to perform actions using the methods defined in the class.
Objects are used to model and manipulate real-world entities in code.
In summary, functions are used to define specific tasks or operations, classes serve as templates for creating objects with shared attributes and behaviors, and objects are instances of classes that represent real-world entities and can interact with their environment. These concepts are central to object-oriented programming and software development.
The document discusses functions in Python. It begins by defining what a function is - a reusable block of code that performs a specific task. Functions allow encapsulation of code into single reusable units. The document then provides an example of a simple greet function that takes a name parameter and returns a greeting string. It also demonstrates how to define and call functions in Python.
The document discusses stacks and recursion. It begins by explaining stacks and their applications, including evaluating expressions and adding very large integers by treating them as stacks of numerals. It then covers recursion, defining it as a problem-solving approach that breaks a problem into smaller instances of itself. Key aspects of recursion like base cases, recursive calls, and stack frames are explained. An example of adding large integers recursively using stacks is provided, along with code examples of recursively calculating factorials and Fibonacci numbers. Advantages and disadvantages of recursion compared to iteration are also discussed.
This PPT File helps IT freshers with the Basic Interview Questions, which will boost there confidence before going to the Interview. For more details and Interview Questions please log in www.rekruitin.com and click on Job Seeker tools. Also register on the and get employed.
By ReKruiTIn.com
The document discusses various Python concepts related to functions:
1. Functions allow encapsulating reusable blocks of code and are defined using the def keyword. Parameters and a return value can be included.
2. User-defined functions are functions created to perform specific tasks. They are defined using def and can take parameters.
3. Lambda functions are anonymous inline functions defined using lambda arguments: expression syntax. They are often used for short tasks.
4. Recursive functions call themselves, requiring a base case to terminate recursion. They can solve problems by breaking them into smaller instances.
This document discusses user-defined functions in C++. It covers value-returning functions, the return statement, function prototypes, and flow of execution. Standard (predefined) functions are discussed as well as advantages of using functions such as modularity, reusability, and the ability to focus on individual parts of a program. Function definitions require a heading specifying return type, name, and parameters, as well as a function body and return statement.
The document discusses using the CSV module in Python to work with CSV files where rows can be accessed as dictionaries by using the DictReader and DictWriter classes. DictReader allows iterating over rows and accessing fields by name, inferring names from the first row. DictWriter requires specifying field names and writes rows from a dictionary, where keys must match field names. Examples are given for reading and writing CSV files using these classes.
Similar to Database structure Structures Link list and trees and Recurison complete (20)
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeWalaa Eldin Moustafa
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
Enhanced Enterprise Intelligence with your personal AI Data Copilot.pdfGetInData
Recently we have observed the rise of open-source Large Language Models (LLMs) that are community-driven or developed by the AI market leaders, such as Meta (Llama3), Databricks (DBRX) and Snowflake (Arctic). On the other hand, there is a growth in interest in specialized, carefully fine-tuned yet relatively small models that can efficiently assist programmers in day-to-day tasks. Finally, Retrieval-Augmented Generation (RAG) architectures have gained a lot of traction as the preferred approach for LLMs context and prompt augmentation for building conversational SQL data copilots, code copilots and chatbots.
In this presentation, we will show how we built upon these three concepts a robust Data Copilot that can help to democratize access to company data assets and boost performance of everyone working with data platforms.
Why do we need yet another (open-source ) Copilot?
How can we build one?
Architecture and evaluation
Natural Language Processing (NLP), RAG and its applications .pptxfkyes25
1. In the realm of Natural Language Processing (NLP), knowledge-intensive tasks such as question answering, fact verification, and open-domain dialogue generation require the integration of vast and up-to-date information. Traditional neural models, though powerful, struggle with encoding all necessary knowledge within their parameters, leading to limitations in generalization and scalability. The paper "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks" introduces RAG (Retrieval-Augmented Generation), a novel framework that synergizes retrieval mechanisms with generative models, enhancing performance by dynamically incorporating external knowledge during inference.
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...sameer shah
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
3. What The Course Is About
All programs manipulate data.
All programs represent data in some way.
Data structures is concerned with the
representation and manipulation of data.
Data manipulation requires an algorithm.
4. CHAPTER 0: INTRODUTION
What is Data Structures?
– A data structure is defined by
(1) the logical arrangement of data elements,
combined with
(2) the set of operations we need to access the
elements.
5. What is Data Structures?
Example:library
– is composed of elements
(books)
– Accessing a particular
book requires knowledge
of the arrangement of the
books
– Users access books only
through the librarian
the logical arrangement of data elements,
combined with
the set of operations we need to access the
elements.
6. Basic Data Structures
Structures include
– linked lists
– Stack, Queue
– binary trees
– …and others
7. What is Algorithm?
Algorithm:
– A computable set of steps to achieve a desired
result
Example: Find an element
1 2 3 4 5 6 7
1
2
3
4
5
6
7
8. Chapter 0: C++ LANGUAGE
1. ADDRESS
For every variable there are two attributes:
address and value
cout << "Value of y is: " << y << "n";
cout << "Address of y is: " << &y << "nn";
In memory with address 3: value: 45.
In memory with address 2: value "Dave"
9. Chapter 0: C++ LANGUAGE
2. POINTERS
1. is a variable whose value is also an address.
2. A pointer to an integer is a variable that can
store the address of that integer
a: value of variable
&a: address of a
*a means you are printing the value at the location specified by a
10. Chapter 0: C++ LANGUAGE
int i; //A
int * a; //B
cout<<"The address of i "<< &i << " value="<<i <<endl;
cout<<"The address of a " << &a << " value = " << a<< endl;
i = 10; //C
a = &i; //D
cout<<"after assigning value:"<<endl;
cout<<"The address of i "<< &i << " value="<<i <<endl;
cout<<"The address of a " << &a << " value = " << a<< " point to: "<< *a;
11. Chapter 0: C++ LANGUAGE
Points to Remember
• Pointers give a facility to access the value of
a variable indirectly.
• You can define a pointer by including a *
before the name of the variable.
• You can get the address where a variable is
stored by using &.
12. Chapter 0: C++ LANGUAGE
3. ARRAYS
1. An array is a data structure
2. used to process multiple elements with the same data
type when a number of such elements are known.
3. An array is a composite data structure; that means it
had to be constructed from basic data types such as
array integers.
1. int a[5];
2. for(int i = 0;i<5;i++)
1. {a[i]=i; }
13. Chapter 0: C++ LANGUAGE
4. ADDRESS OF EACH ELEMENT IN AN
ARRAY
Each element of the array has a memory
address.
void printdetail(int a[])
{
for(int i = 0;i<5;i++)
{
cout<< "value in array “<< a[i] <<“ at address: “ << &a[i]);
}
14. Chapter 0: C LANGUAGE
5. ACCESSING & MANIPULATING AN
ARRAY USING POINTERS
– You can access an array element by using a pointer.
– If an array stores integers->use a pointer to integer to
access array elements.
15. Chapter 0: C++ LANGUAGE
6. ANOTHER CASE OF MANIPULATING AN
ARRAY USING POINTERS
The array limit is a pointer constant : cannot
change its value in the program.
int a[5]; int *b;
a=b; //error
b=a; //OK
It works correctly even using
a++ ???
16. Chapter 0: C++ LANGUAGE
7. TWO-DIMENSIONAL ARRAY
int a[3][2];
17. Chapter 0: C++ LANGUAGE
9. STRUCTURES
Structures are used when
you want to process data of
multiple data types
But you still want to refer to
the data as a single entity
Access data:
structurename.membernam
e
18. Chapter 1: C++ LANGUAGE
10. STRUCTURE POINTERS
Process the structure using a structure pointer
19. CHAPTER 2: FUNCTION & RECURSION
1. FUNCTION
– provide modularity to the software
– divide complex tasks into small manageable tasks
– avoid duplication of work
20. CHAPTER 2: FUNCTION & RECURSION
2. THE CONCEPT OF STACK
– A stack is memory in which values are stored and
retrieved in "last in first out" manner by using
operations called push and pop.
21. CHAPTER 2: FUNCTION & RECURSION
3. THE SEQUENCE OF EXECUTION DURING A
FUNCTION CALL
– When the function is called, the current execution is
temporarily stopped and the control goes to the called
function. After the call, the execution resumes from the
point at which the execution is stopped.
– To get the exact point at which execution is resumed, the
address of the next instruction is stored in the stack. When
the function call completes, the address at the top of the
stack is taken.
22. CHAPTER 2: FUNCTION & RECURSION
3. THE SEQUENCE OF EXECUTION
DURING A FUNCTION CALL
– Functions or sub-programs are implemented
using a stack.
– When a function is called, the address of the next
instruction is pushed into the stack.
– When the function is finished, the address for
execution is taken by using the pop operation.
23. CHAPTER 2: FUNCTION & RECURSION
3. THE SEQUENCE OF
EXECUTION DURING A
FUNCTION CALL
Result:?
24. CHAPTER 2: FUNCTION & RECURSION
4. PARAMETER * REFERENCE PASSING
– passing by value
the value before and after the call remains the same
– passing by reference
changed value after the function completes
25. CHAPTER 2: FUNCTION & RECURSION
6. RESOLVING VARIABLE REFERENCES
When a variable can
be resolved by using
multiple references,
the local definition is
given more preference
26. CHAPTER 2: FUNCTION & RECURSION
7. RECURSION
– A method of
programming
whereby a function
directly or indirectly
calls itself
– Problems: stop
recursion?
30. CHAPTER 2: FUNCTION & RECURSION
8. STACK OVERHEADS IN RECURSION
– two important results: the depth of recursion and
the stack overheads in recursion
31. CHAPTER 2: FUNCTION & RECURSION
9. WRITING A RECURSIVE FUNCTION
– Recursion enables us to write a program in a
natural way. The speed of a recursive program
is slower because of stack overheads.
– In a recursive program you have to specify
recursive conditions, terminating conditions, and
recursive expressions.
33. CHAPTER 2: FUNCTION & RECURSION
10. TYPES OF RECURSION
– LINEAR RECURSION
only makes a single call to itself each time the
function runs
34. CHAPTER 2: FUNCTION & RECURSION
10. TYPES OF RECURSION
– TAIL RECURSION
Tail recursion is a form of linear recursion.
In tail recursion, the recursive call is the last thing
the function does. Often, the value of the recursive
call is returned.
35. CHAPTER 2: FUNCTION & RECURSION
10. TYPES OF RECURSION
– BINARY RECURSION
Some recursive functions don't just have one call to
themself, they have two (or more).
36. CHAPTER 2: FUNCTION & RECURSION
10. TYPES OF RECURSION
– EXPONENTIAL RECURSION
An exponential recursive function is one that, if you
were to draw out a representation of all the function
calls, would have an exponential number of calls in
relation to the size of the data set
(exponential meaning if there were n elements, there
would be O(an
) function calls where a is a positive
number)
37. CHAPTER 2: FUNCTION & RECURSION
10. TYPES OF RECURSION
– EXPONENTIAL RECURSION
38. CHAPTER 2: FUNCTION & RECURSION
10. TYPES OF RECURSION
– NESTED RECURSION
In nested recursion, one of the arguments to the
recursive function is the recursive function itself
These functions tend to grow extremely fast.
39.
40.
41. CHAPTER 2: FUNCTION & RECURSION
10. TYPES OF RECURSION
– MUTUAL RECURSION
A recursive function doesn't necessarily need to call
itself.
Some recursive functions work in pairs or even larger
groups. For example, function A calls function B which
calls function C which in turn calls function A.
45. Week3: Recursion Excercises (1)
E1. (44/174) Write a program to compute: S
= 1 + 2 + 3 + …n using recursion.
46. Week3: Recursion Excercises (2-3)
E3(a). Write a program to print a revert
number Example: input n=12345. Print out:
54321.
E3(b). Write a program to print this number
Example: input n=12345. Print out: 12345.
47. Week3: Recursion Excercises (4)
E4. Write a recursion function to find the sum
of every number in a int number. Example:
n=1980 => Sum=1+9+8+0=18.
48. Week3: Recursion Excercises (5)
E4. Write a recursion function to calculate:
– S=a[0]+a[1]+…a[n-1]
A: array of integer numbers
49. Week3: Recursion Excercises (6)
E4. Write a recursion function to find an
element in an array (using linear algorithm)
53. Week 4
CHAPTER 3: SEARCHING TECHNIQUES
1. LINEAR (SEQUENTIAL) SEARCH
2. BINARY SEARCH
3. COMPLEXITY OF ALGORITHMS
54. SEARCHING TECHNIQUES
To finding out whether a particular element is
present in the list.
2 methods: linear search, binary search
The method we use depends on how the
elements of the list are organized
– unordered list:
linear search: simple, slow
– an ordered list
binary search or linear search: complex, faster
55. 1. LINEAR (SEQUENTIAL) SEARCH
How?
– Proceeds by sequentially comparing the key with
elements in the list
– Continues until either we find a match or the end
of the list is encountered.
– If we find a match, the search terminates
successfully by returning the index of the element
– If the end of the list is encountered without a
match, the search terminates unsuccessfully.
56. 1. LINEAR (SEQUENTIAL) SEARCH
void lsearch(int list[],int n,int element)
{ int i, flag = 0;
for(i=0;i<n;i++)
if( list[i] == element)
{ cout<<“found at position”<<i;
flag =1;
break; }
if( flag == 0)
cout<<“ not found”;
}
flag: what for???
57. 1. LINEAR (SEQUENTIAL) SEARCH
int lsearch(int list[],int n,int element)
{ int i, find= -1;
for(i=0;i<n;i++)
if( list[i] == element)
{find =i;
break;}
return find;
}
Another way using flag
average time: O(n)
58. 2. BINARY SEARCH
List must be a sorted one
We compare the element with the element
placed approximately in the middle of the list
If a match is found, the search terminates
successfully.
Otherwise, we continue the search for the
key in a similar manner either in the upper
half or the lower half.
61. void bsearch(int list[],int n,int element)
{
int l,u,m, flag = 0;
l = 0; u = n-1;
while(l <= u)
{ m = (l+u)/2;
if( list[m] == element)
{cout<<"found:"<<m;
flag =1;
break;}
else
if(list[m] < element)
l = m+1;
else
u = m-1;
}
if( flag == 0)
cout<<"not found";
}
average time: O(log2n)
62. BINARY SEARCH: Recursion
int Search (int list[], int key, int left, int right)
{
if (left <= right) {
int middle = (left + right)/2;
if (key == list[middle])
return middle;
else if (key < list[middle])
return Search(list,key,left,middle-1);
else return Search(list,key,middle+1,right);
}
return -1;
}
63. 3. COMPLEXITY OF ALGORITHMS
In Computer Science, it is important to measure the
quality of algorithms, especially the specific amount
of a certain resource an algorithm needs
Resources: time or memory storage (PDA?)
Different algorithms do same task with a different set
of instructions in less or more time, space or effort
than other.
The analysis has a strong mathematical background.
The most common way of qualifying an algorithm is
the Asymptotic Notation, also called Big O.
64. 3. COMPLEXITY OF ALGORITHMS
It is generally written as
Polynomial time algorithms,
– O(1) --- Constant time --- the time does not change in response to
the size of the problem.
– O(n) --- Linear time --- the time grows linearly with the size (n) of
the problem.
– O(n2
) --- Quadratic time --- the time grows quadratically with the
size (n) of the problem. In big O notation, all polynomials with the
same degree are equivalent, so O(3n2
+ 3n + 7) = O(n2
)
Sub-linear time algorithms
– O(logn) -- Logarithmic time
Super-polynomial time algorithms
– O(n!)
– O(2n
)
65. 3. COMPLEXITY OF ALGORITHMS
Example1: complexity of an algorithm
void f ( int a[], int n )
{
int i;
cout<< "N = “<< n;
for ( i = 0; i < n; i++ )
cout<<a[i];
printf ( "n" );
}
?
?
2 * O(1) + O(N)
O(N)
66. 3. COMPLEXITY OF ALGORITHMS
Example2: complexity of an algorithm
void f ( int a[], int n )
{ int i;
cout<< "N = “<< n;
for ( i = 0; i < n; i++ )
for (int j=0;j<n;j++)
cout<<a[i]<<a[j];
for ( i = 0; i < n; i++ )
cout<<a[i];
printf ( "n" );
}
?
?
2 * O(1) + O(N)+O(N2
)
O(N2)
67. 3. COMPLEXITY OF ALGORITHMS
Linear Search
– O(n).
Binary Search
– O(log2 N)
70. SORTING TECHNIQUES
We need to do sorting for the following reasons :
a) By keeping a data file sorted, we can do binary
search on it.
b) Doing certain operations, like matching data in
two different files, become much faster.
There are various methods for sorting: Bubble sort,
Insertion sort, Selection sort, Quick sort, Heap sort,
Merge sort…. They having different average and
worst case behaviours:
71. 1. BUBBLE SORT
Introduction:
Bubble sorting is a simple sorting technique in
which we arrange the elements of the list by
forming pairs of adjacent elements. That
means we form the pair of the ith and (i+1)th
element. If the order is ascending, we
interchange the elements of the pair if the
first element of the pair is greater than the
second element.
75. 1. BUBBLE SORT
void bsort(int list[], int n)
{
int i,j;
for(i=0;i<(n-1);i++)
for(j=0;j<(n-(i+1));j++)
if(list[j] > list[j+1])
swap(&list[j],&list[j+1]);
}
76. 2. INSERTION SORT
Introduction:
Basic Idea: Insert a record R into a
sequence of ordered records: R1,R2,.., Ri
with keys K1 <= K2 <= ... <= Ki , such that,
the resulting sequence of size i+1 is also
ordered with respect to key values.
79. 2. INSERTION SORT
Algorithm Insertion_Sort; (* Assume Ro has Ko = -maxint *)
void InsertionSort( Item &list[])
{ // Insertion_Sort
Item r;
int i,j;
list[0].key = -maxint;
for (j=2; j<=n; j++)
{ r=list[j];
i=j-1;
while ( r.key < list[i].key )
{// move greater entries to the right
list[i+1]:=list[i];
i:=i-1;
};
list[i+1] = r // insert into it's place
}
80. 3. SELECTION SORT
Selection sort is a simplicity sorting algorithm. It
works as its name as it is. Here are basic
steps of selection sort algorithm:
1. Find the minimum element in the list
2. Swap it with the element in the first position
of the list
3. Repeat the steps above for all remainder
elements of the list starting at the second
position.
83. 3. SELECTION SORT
void selection_sort(int list[], int n){
int i, j, min; for (i = 0; i < n - 1; i++) {
min = i;
for (j = i+1; j < n; j++) {
if (list[j] < list[min]) {
min = j;
}
}
swap(&list[i], &list[min]);
}
88. 4. QUICK SORT
Chọn ngẫu nhiên một phần tử X của dãy (chẳng
hạn phần tử đầu tiên) và cố gắng phân chia dãy
này thành 3 dãy con liên tiếp nhau:
+ Dãy 1: Gồm những phần tử nhỏ hơn X.
+ Dãy 2: Gồm những phần tử bằng X.
+ Dãy 3: Gồm những phần tử lớn hơn X.
Sau đó áp dụng lại giải thuật này cho dãy con thứ
nhất và dãy con thứ ba (dãy con này có số phần
tử lớn hơn 1).
94. Introduction
When dealing with many problems we need a dynamic
list, dynamic in the sense that the size requirement
need not be known at compile time. Thus, the list
may grow or shrink during runtime. A linked list is a
data structure that is used to model such a dynamic
list of data items, so the study of the linked lists as
one of the data structures is important.
Data: Streets in a city. Mapquest.
Manipulation: Do something with the data. Add a new street. Close a street. Make a street one way. Find route from your home to nearest gas station. Input/output data.
Data representation: simple variables…int, float
array variables… int [], float []
Others to be studied in this course.
Algorithm…sequence of steps that results in the performance of a specific task (find nearest gas station to home).
#include &lt;iostream.h&gt;
#include &lt;conio.h&gt;
#include &lt;stdlib.h&gt;
void nhap(int a[][5],int m,int n);
void xuat(
int a[][5],int m,int n);
void duyet(int a[][5],int m,int n,int x,int y);
int count (int a[][5],int m,int n,int x,int y);
//=======================main ===================================
void main()
{
//randomize();
int a[5][5];
nhap (a,5,5);
xuat(a,5,5);
cout&lt;&lt;&quot;================&quot;&lt;&lt;endl;
int x,y;
cin&gt;&gt;x&gt;&gt;y;
cout&lt;&lt;a[x][y]&lt;&lt;endl;
duyet(a,5,5,x,y);
cout&lt;&lt;&quot;================&quot;&lt;&lt;endl;
xuat(a,5,5);
}
//==============================================================
void duyet(int a[][5],int m,int n,int x,int y)
{
if (a[x][y]!=1) return;
a[x][y]=9;
if (x-1&gt;=0) duyet(a,m,n,x-1,y);
if (x+1&lt;=m-1) duyet(a,m,n,x+1,y);
if (y-1&gt;=0) duyet(a,m,n,x,y-1);
if (y+1&lt;=n-1) duyet(a,m,n,x,y+1);
}
//==============================================================
int count (int a[][5],int m,int n,int x,int y)
{
if (a[x][y]!=1) return 0;
a[x][y]=9;
if (x-1&gt;=0) count(a,m,n,x-1,y);
if (x+1&lt;=m-1) count(a,m,n,x+1,y);
if (y-1&gt;=0) count(a,m,n,x,y-1);
if (y+1&lt;=n-1) count(a,m,n,x,y+1);
}
//===========================nhap mang===============================
void nhap(int a[][5],int m,int n)
{
for (int i=0;i&lt;m;i++)
for (int j=0;j&lt;n;j++)
a[i][j]=rand()%2;
}
//===========================nhap mang===============================
void xuat(int a[][5 ],int m,int n)
{
for (int i=0;i&lt;m;i++)
{
for (int j=0;j&lt;n;j++)
cout&lt;&lt;a[i][j]&lt;&lt;&quot; &quot;;
cout&lt;&lt;endl;
}
}