This document provides an overview of algorithms and recursion from a lecture. It discusses performance analysis using Big O notation. Common time complexities like O(1), O(n), O(n^2) are introduced. The document defines an algorithm as a set of well-defined steps to solve a problem and categorizes algorithms as recursive vs iterative, logical, serial/parallel/distributed, deterministic/non-deterministic, exact/approximate, and quantum. Examples of recursive algorithms like factorials, greatest common divisor, and the Fibonacci sequence are presented along with their recursive definitions and code implementations.
The document discusses various sorting algorithms that use the divide-and-conquer approach, including quicksort, mergesort, and heapsort. It provides examples of how each algorithm works by recursively dividing problems into subproblems until a base case is reached. Code implementations and pseudocode are presented for key steps like partitioning arrays in quicksort, merging sorted subarrays in mergesort, and adding and removing elements from a heap data structure in heapsort. The algorithms are compared in terms of their time and space complexity and best uses.
OOPS concepts are one of the most important concepts in high level languages. Here in this PPT we will learn more about Object oriented approach in python programming which includes details related to classes and objects, inheritance, dat abstraction, polymorphism and many more with examples and code.
This document discusses data abstraction and abstract data types (ADTs). It defines an ADT as a collection of data along with a set of operations on that data. An ADT specifies what operations can be performed but not how they are implemented. This allows data structures to be developed independently from solutions and hides implementation details behind the ADT's operations. The document provides examples of list ADTs and an array-based implementation of a list ADT in C++.
1. Asymptotic notation such as Big-O, Omega, and Theta are used to describe the running time of algorithms as the input size n approaches infinity, rather than giving the exact running time.
2. Big-O notation gives an upper bound and describes worst-case running time, Omega notation gives a lower bound and describes best-case running time, and Theta notation gives a tight bound where the worst and best cases are equal up to a constant.
3. Common examples of asymptotic running times include O(1) for constant time, O(log n) for logarithmic time, O(n) for linear time, and O(n^2) for quadratic time.
This document discusses priority queues. It defines a priority queue as a queue where insertion and deletion are based on some priority property. Items with higher priority are removed before lower priority items. There are two main types: ascending priority queues remove the smallest item, while descending priority queues remove the largest item. Priority queues are useful for scheduling jobs in operating systems, where real-time jobs have highest priority and are scheduled first. They are also used in network communication to manage limited bandwidth.
This presentation is all about various built in
datastructures which we have in python.
List
Dictionary
Tuple
Set
and various methods present in each data structure
The document discusses sorting algorithms and randomized quicksort. It explains that quicksort is an efficient sorting algorithm that was developed by Tony Hoare in 1960. The quicksort algorithm works by picking a pivot element and reordering the array so that all smaller elements come before the pivot and larger elements come after. It then recursively applies this process to the subarrays. Randomized quicksort improves upon quicksort by choosing the pivot element randomly, making the expected performance of the algorithm good for any input.
The document discusses various sorting algorithms that use the divide-and-conquer approach, including quicksort, mergesort, and heapsort. It provides examples of how each algorithm works by recursively dividing problems into subproblems until a base case is reached. Code implementations and pseudocode are presented for key steps like partitioning arrays in quicksort, merging sorted subarrays in mergesort, and adding and removing elements from a heap data structure in heapsort. The algorithms are compared in terms of their time and space complexity and best uses.
OOPS concepts are one of the most important concepts in high level languages. Here in this PPT we will learn more about Object oriented approach in python programming which includes details related to classes and objects, inheritance, dat abstraction, polymorphism and many more with examples and code.
This document discusses data abstraction and abstract data types (ADTs). It defines an ADT as a collection of data along with a set of operations on that data. An ADT specifies what operations can be performed but not how they are implemented. This allows data structures to be developed independently from solutions and hides implementation details behind the ADT's operations. The document provides examples of list ADTs and an array-based implementation of a list ADT in C++.
1. Asymptotic notation such as Big-O, Omega, and Theta are used to describe the running time of algorithms as the input size n approaches infinity, rather than giving the exact running time.
2. Big-O notation gives an upper bound and describes worst-case running time, Omega notation gives a lower bound and describes best-case running time, and Theta notation gives a tight bound where the worst and best cases are equal up to a constant.
3. Common examples of asymptotic running times include O(1) for constant time, O(log n) for logarithmic time, O(n) for linear time, and O(n^2) for quadratic time.
This document discusses priority queues. It defines a priority queue as a queue where insertion and deletion are based on some priority property. Items with higher priority are removed before lower priority items. There are two main types: ascending priority queues remove the smallest item, while descending priority queues remove the largest item. Priority queues are useful for scheduling jobs in operating systems, where real-time jobs have highest priority and are scheduled first. They are also used in network communication to manage limited bandwidth.
This presentation is all about various built in
datastructures which we have in python.
List
Dictionary
Tuple
Set
and various methods present in each data structure
The document discusses sorting algorithms and randomized quicksort. It explains that quicksort is an efficient sorting algorithm that was developed by Tony Hoare in 1960. The quicksort algorithm works by picking a pivot element and reordering the array so that all smaller elements come before the pivot and larger elements come after. It then recursively applies this process to the subarrays. Randomized quicksort improves upon quicksort by choosing the pivot element randomly, making the expected performance of the algorithm good for any input.
The document discusses recursion, including:
1) Recursion involves breaking a problem down into smaller subproblems until a base case is reached, then building up the solution to the overall problem from the solutions to the subproblems.
2) A recursive function is one that calls itself, with each call typically moving closer to a base case where the problem can be solved without recursion.
3) Recursion can be linear, involving one recursive call, or binary, involving two recursive calls to solve similar subproblems.
1. Arrays declared with a fixed size limit the program size, while dynamically allocated arrays using heap memory allow the size to be determined at runtime.
2. The heap segment is used for dynamic memory allocation using functions like malloc() and new to request memory from the operating system as needed.
3. Deallocation of dynamically allocated memory is required using free() and delete to avoid memory leaks and ensure memory is returned to the operating system.
The document discusses control structures in C programming, including conditional statements and loops.
It describes the if statement and if-else statement, which allow conditional execution of code based on boolean expressions. It also covers the switch statement for selecting between multiple code blocks based on an integral expression.
The document also discusses three loop structures in C - while, for, and do-while loops. The while and for loops repeat code while/for a given condition is true. The do-while loop executes code once before checking the condition. Increment/decrement operators are commonly used to update loop counters in for loops.
A presentation about the ideas of recursion and recursive functions.
This is my lecture presentation during A. Paruj Ratanaworabhan’s basic preparatory programming course for freshmen: Introduction to Programming: A Tutorial for New Comers Using Python
The document discusses different notation styles for representing arithmetic expressions, including infix, prefix, and postfix notations. It provides examples of converting expressions between these notations. Infix notation is the conventional style that humans use, but prefix and postfix notations are better suited for computer parsing. The document also covers parsing expressions, operator precedence, and the steps to convert between infix and prefix and infix and postfix notations.
The document discusses 2D arrays, including their definition as an array of arrays, implementation methods of row-major and column-major ordering, and formulas for calculating element addresses based on storage method. It also covers basic arithmetic operations on 2D arrays like addition, subtraction, and multiplication. Examples of problems involving 2D arrays and their solutions are provided.
This document discusses deterministic and non-deterministic algorithms. A deterministic algorithm always produces the same output for a given input, while a non-deterministic algorithm may have multiple possible outputs for one input. Non-deterministic algorithms have two stages: a guessing stage that generates a potential solution, and a verification stage that checks if the guess is correct. Examples of non-deterministic algorithms given are a search algorithm that guesses a location containing the search value, and a sorting algorithm that guesses the sorted order of elements.
The document discusses space complexity, which is the amount of computer memory required during program execution based on input size. Space complexity includes fixed space requirements like instruction space and simple variables, as well as variable space requirements that depend on input instance characteristics like input/output size and values. It provides examples of calculating space complexity for different algorithms, showing that space complexity can be a constant plus a variable component related to input size.
This document discusses object-oriented programming in C++. It covers several topics related to OOP in C++ including classes, constructors, destructors, inheritance, polymorphism, and templates. The document consists of lecture slides that define key concepts and provide examples to illustrate how various OOP features work in C++.
The document discusses functions, modules, and how to modularize Python programs. It provides examples of defining functions, using parameters, returning values, and function scope. It also discusses creating modules, importing modules, and the difference between running a Python file as a module versus running it as the main script using the __name__ == "__main__" check. The key points are that functions help break programs into reusable and readable components, modules further help organize code, and the __name__ check allows code to run differently depending on how it is imported or run directly.
The document discusses arrays in Java. It begins by defining what an array is - a structured data type that stores a fixed number of elements of the same type. It then covers how to declare and initialize one-dimensional arrays, manipulate array elements using loops and indexes, and how to pass arrays as parameters to methods. The document also discusses arrays of objects and multidimensional arrays.
This document provides an overview of basic object-oriented programming (OOP) concepts including objects, classes, inheritance, polymorphism, encapsulation, and data abstraction. It defines objects as entities with both data (characteristics) and behavior (operations). Classes are blueprints that are used to create objects. Inheritance allows objects to inherit properties from parent classes. Polymorphism allows code to take different forms. Encapsulation wraps data and functions into classes, hiding information. Data abstraction focuses on important descriptions without details.
1) Stacks are linear data structures that follow the LIFO (last-in, first-out) principle. Elements can only be inserted or removed from one end called the top of the stack.
2) The basic stack operations are push, which adds an element to the top of the stack, and pop, which removes an element from the top.
3) Stacks have many applications including evaluating arithmetic expressions by converting them to postfix notation and implementing the backtracking technique in recursive backtracking problems like tower of Hanoi.
File handling in C programming uses file streams as the means of communication between programs and data files. The input stream extracts data from files and supplies it to the program, while the output stream stores data from the program into files. To handle file input/output, header file fstream.h is included, which contains ifstream and ofstream classes. Common file operations include opening, reading, writing, and closing files using functions like fopen(), fgetc(), fputs(), fclose(), and checking for end-of-file conditions. Files can be opened in different modes like read, write, append depending on the operation to be performed.
Functions allow programmers to break programs into smaller, reusable parts. There are two types of functions in C: library functions and user-defined functions. User-defined functions make programs easier to understand, debug, test and maintain. Functions are declared with a return type and can accept arguments. Functions can call other functions, allowing for modular and structured program design.
The document discusses files and file operations in C/C++. It defines a file as a collection of bytes stored on a secondary storage device. There are different types of files like text files, data files, program files, and directory files. It describes opening, reading, writing, appending, and closing files using functions like fopen(), fread(), fwrite(), fclose(), etc. It also discusses random and sequential file access and modifying file contents using functions like fseek(), fread(), fwrite().
The document discusses file handling in C programming. It explains that console I/O functions use keyboard and monitor for input and output but the data is lost when the program terminates. Files provide a permanent way to store and access data. The document then describes different file handling functions like fopen(), fclose(), fgetc(), fputc(), fprintf(), fscanf() for reading from and writing to files. It also discusses opening files in different modes, reading and writing characters and strings to files, and using formatted I/O functions for files.
In this module of python programming you will be learning about control statements. A control statement is a statement that determines whether other statements will be executed. An if statement decides whether to execute another statement, or decides which of two statements to execute. A loop decides how many times to execute another statement.
- A linked list is a data structure where each node contains a data field and a pointer to the next node.
- It allows dynamic size and efficient insertion/deletion compared to arrays.
- A doubly linked list adds a pointer to the previous node, allowing traversal in both directions.
- A circular linked list connects the last node back to the first node, making it a continuous loop.
- Variations require changes to the node structure and functions like append/delete to handle the added previous/next pointers.
Data Structures and Algorithms Lecture 2: Analysis of Algorithms, Asymptotic ...TechVision8
This document discusses analyzing the running time of algorithms. It introduces pseudocode as a way to describe algorithms, primitive operations that are used to count the number of basic steps an algorithm takes, and asymptotic analysis to determine an algorithm's growth rate as the input size increases. The key points covered are using big-O notation to focus on the dominant term and ignore lower-order terms and constants, and analyzing two algorithms for computing prefix averages to demonstrate asymptotic analysis.
This document provides an overview of algorithm analysis and asymptotic complexity. It discusses learning outcomes related to analyzing algorithm efficiency using Big O, Omega, and Theta notation. Key points covered include:
- Defining the problem size n and relating algorithm running time to n
- Distinguishing between best-case, worst-case, and average-case complexity
- Using asymptotic notation like Big O to give upper bounds on complexity rather than precise calculations
- Common asymptotic categories like O(n), O(n^2), O(n log n) that classify algorithm growth rates
The document discusses recursion, including:
1) Recursion involves breaking a problem down into smaller subproblems until a base case is reached, then building up the solution to the overall problem from the solutions to the subproblems.
2) A recursive function is one that calls itself, with each call typically moving closer to a base case where the problem can be solved without recursion.
3) Recursion can be linear, involving one recursive call, or binary, involving two recursive calls to solve similar subproblems.
1. Arrays declared with a fixed size limit the program size, while dynamically allocated arrays using heap memory allow the size to be determined at runtime.
2. The heap segment is used for dynamic memory allocation using functions like malloc() and new to request memory from the operating system as needed.
3. Deallocation of dynamically allocated memory is required using free() and delete to avoid memory leaks and ensure memory is returned to the operating system.
The document discusses control structures in C programming, including conditional statements and loops.
It describes the if statement and if-else statement, which allow conditional execution of code based on boolean expressions. It also covers the switch statement for selecting between multiple code blocks based on an integral expression.
The document also discusses three loop structures in C - while, for, and do-while loops. The while and for loops repeat code while/for a given condition is true. The do-while loop executes code once before checking the condition. Increment/decrement operators are commonly used to update loop counters in for loops.
A presentation about the ideas of recursion and recursive functions.
This is my lecture presentation during A. Paruj Ratanaworabhan’s basic preparatory programming course for freshmen: Introduction to Programming: A Tutorial for New Comers Using Python
The document discusses different notation styles for representing arithmetic expressions, including infix, prefix, and postfix notations. It provides examples of converting expressions between these notations. Infix notation is the conventional style that humans use, but prefix and postfix notations are better suited for computer parsing. The document also covers parsing expressions, operator precedence, and the steps to convert between infix and prefix and infix and postfix notations.
The document discusses 2D arrays, including their definition as an array of arrays, implementation methods of row-major and column-major ordering, and formulas for calculating element addresses based on storage method. It also covers basic arithmetic operations on 2D arrays like addition, subtraction, and multiplication. Examples of problems involving 2D arrays and their solutions are provided.
This document discusses deterministic and non-deterministic algorithms. A deterministic algorithm always produces the same output for a given input, while a non-deterministic algorithm may have multiple possible outputs for one input. Non-deterministic algorithms have two stages: a guessing stage that generates a potential solution, and a verification stage that checks if the guess is correct. Examples of non-deterministic algorithms given are a search algorithm that guesses a location containing the search value, and a sorting algorithm that guesses the sorted order of elements.
The document discusses space complexity, which is the amount of computer memory required during program execution based on input size. Space complexity includes fixed space requirements like instruction space and simple variables, as well as variable space requirements that depend on input instance characteristics like input/output size and values. It provides examples of calculating space complexity for different algorithms, showing that space complexity can be a constant plus a variable component related to input size.
This document discusses object-oriented programming in C++. It covers several topics related to OOP in C++ including classes, constructors, destructors, inheritance, polymorphism, and templates. The document consists of lecture slides that define key concepts and provide examples to illustrate how various OOP features work in C++.
The document discusses functions, modules, and how to modularize Python programs. It provides examples of defining functions, using parameters, returning values, and function scope. It also discusses creating modules, importing modules, and the difference between running a Python file as a module versus running it as the main script using the __name__ == "__main__" check. The key points are that functions help break programs into reusable and readable components, modules further help organize code, and the __name__ check allows code to run differently depending on how it is imported or run directly.
The document discusses arrays in Java. It begins by defining what an array is - a structured data type that stores a fixed number of elements of the same type. It then covers how to declare and initialize one-dimensional arrays, manipulate array elements using loops and indexes, and how to pass arrays as parameters to methods. The document also discusses arrays of objects and multidimensional arrays.
This document provides an overview of basic object-oriented programming (OOP) concepts including objects, classes, inheritance, polymorphism, encapsulation, and data abstraction. It defines objects as entities with both data (characteristics) and behavior (operations). Classes are blueprints that are used to create objects. Inheritance allows objects to inherit properties from parent classes. Polymorphism allows code to take different forms. Encapsulation wraps data and functions into classes, hiding information. Data abstraction focuses on important descriptions without details.
1) Stacks are linear data structures that follow the LIFO (last-in, first-out) principle. Elements can only be inserted or removed from one end called the top of the stack.
2) The basic stack operations are push, which adds an element to the top of the stack, and pop, which removes an element from the top.
3) Stacks have many applications including evaluating arithmetic expressions by converting them to postfix notation and implementing the backtracking technique in recursive backtracking problems like tower of Hanoi.
File handling in C programming uses file streams as the means of communication between programs and data files. The input stream extracts data from files and supplies it to the program, while the output stream stores data from the program into files. To handle file input/output, header file fstream.h is included, which contains ifstream and ofstream classes. Common file operations include opening, reading, writing, and closing files using functions like fopen(), fgetc(), fputs(), fclose(), and checking for end-of-file conditions. Files can be opened in different modes like read, write, append depending on the operation to be performed.
Functions allow programmers to break programs into smaller, reusable parts. There are two types of functions in C: library functions and user-defined functions. User-defined functions make programs easier to understand, debug, test and maintain. Functions are declared with a return type and can accept arguments. Functions can call other functions, allowing for modular and structured program design.
The document discusses files and file operations in C/C++. It defines a file as a collection of bytes stored on a secondary storage device. There are different types of files like text files, data files, program files, and directory files. It describes opening, reading, writing, appending, and closing files using functions like fopen(), fread(), fwrite(), fclose(), etc. It also discusses random and sequential file access and modifying file contents using functions like fseek(), fread(), fwrite().
The document discusses file handling in C programming. It explains that console I/O functions use keyboard and monitor for input and output but the data is lost when the program terminates. Files provide a permanent way to store and access data. The document then describes different file handling functions like fopen(), fclose(), fgetc(), fputc(), fprintf(), fscanf() for reading from and writing to files. It also discusses opening files in different modes, reading and writing characters and strings to files, and using formatted I/O functions for files.
In this module of python programming you will be learning about control statements. A control statement is a statement that determines whether other statements will be executed. An if statement decides whether to execute another statement, or decides which of two statements to execute. A loop decides how many times to execute another statement.
- A linked list is a data structure where each node contains a data field and a pointer to the next node.
- It allows dynamic size and efficient insertion/deletion compared to arrays.
- A doubly linked list adds a pointer to the previous node, allowing traversal in both directions.
- A circular linked list connects the last node back to the first node, making it a continuous loop.
- Variations require changes to the node structure and functions like append/delete to handle the added previous/next pointers.
Data Structures and Algorithms Lecture 2: Analysis of Algorithms, Asymptotic ...TechVision8
This document discusses analyzing the running time of algorithms. It introduces pseudocode as a way to describe algorithms, primitive operations that are used to count the number of basic steps an algorithm takes, and asymptotic analysis to determine an algorithm's growth rate as the input size increases. The key points covered are using big-O notation to focus on the dominant term and ignore lower-order terms and constants, and analyzing two algorithms for computing prefix averages to demonstrate asymptotic analysis.
This document provides an overview of algorithm analysis and asymptotic complexity. It discusses learning outcomes related to analyzing algorithm efficiency using Big O, Omega, and Theta notation. Key points covered include:
- Defining the problem size n and relating algorithm running time to n
- Distinguishing between best-case, worst-case, and average-case complexity
- Using asymptotic notation like Big O to give upper bounds on complexity rather than precise calculations
- Common asymptotic categories like O(n), O(n^2), O(n log n) that classify algorithm growth rates
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.
A review of automatic differentiationand its efficient implementationssuserfa7e73
Automatic differentiation is a powerful tool for automatically calculating derivatives of mathematical functions and algorithms. It works by expressing the target function as a sequence of elementary operations and then applying the chain rule to differentiate each operation. This can be done using either forward or reverse mode. Forward mode calculates how changes in inputs propagate through the function to influence the outputs, while reverse mode calculates how changes in outputs backpropagate to influence the inputs. Both modes require performing the computation twice - once for the forward pass and once for the derivative pass. Careful implementation is required to make automatic differentiation efficient in terms of speed and memory usage.
The document discusses algorithm analysis and asymptotic analysis. It introduces key concepts such as algorithms, running time analysis, experimental studies vs theoretical analysis, pseudocode, primitive operations, counting operations, big-O notation, and analyzing algorithms to determine asymptotic running time. As an example, it analyzes two algorithms for computing prefix averages - one with quadratic running time O(n^2) and one with linear running time O(n).
The document discusses algorithm analysis and asymptotic analysis. It introduces key concepts such as algorithms, running time analysis, experimental studies vs theoretical analysis, pseudocode, primitive operations, counting operations, big-O notation, and analyzing algorithms to determine asymptotic running time. As an example, it analyzes two algorithms for computing prefix averages - one with quadratic running time O(n^2) and one with linear running time O(n).
The document discusses algorithm analysis and asymptotic analysis. It introduces key concepts such as algorithms, running time analysis, experimental vs theoretical analysis, pseudocode, primitive operations, counting operations, and asymptotic analysis using big-O notation. As an example, it analyzes an algorithm for finding the maximum element in an array, showing that it runs in O(n) time. It also analyzes two algorithms for computing prefix averages, showing one runs in O(n^2) time while the other improves it to O(n) time.
The document discusses algorithm analysis and asymptotic notation. It introduces algorithms for computing prefix averages of an array in quadratic and linear time. Specifically:
- An algorithm that computes prefix averages by directly applying the definition runs in O(n^2) time as its inner loop iterates over i elements n times.
- A more efficient algorithm that maintains a running sum runs in O(n) time, as each of its n iterations performs a constant number of operations.
- Asymptotic analysis allows algorithms to be classified based on growth rate, ignoring constant factors. This provides an algorithm-independent analysis of computational complexity.
The document introduces algorithms and their role in computing. It defines computational problems as relationships between inputs and outputs. Algorithms are tools used to solve well-specified computational problems and must be correct and efficient. Examples of algorithms are given for common problems like determining if a number is prime or sorting a list alphabetically. Key aspects of algorithms like variables, instructions, selection, repetition and documentation are outlined. Examples are provided of analyzing algorithm efficiency based on runtime. Pseudo-code conventions for writing algorithms are also presented.
CP4151 ADSA unit1 Advanced Data Structures and AlgorithmsSheba41
The document introduces algorithms and their role in computing. It defines computational problems as relationships between inputs and outputs. Algorithms are tools used to solve well-specified computational problems and must be correct and efficient. Examples of algorithms are provided, along with their components like variables, instructions, selections, and repetitions. The document also discusses algorithm efficiency and provides examples comparing the efficiencies of different sorting algorithms. Pseudo-code conventions for writing algorithms are also outlined.
This document discusses algorithm analysis and asymptotic notation. It introduces algorithms for computing prefix averages in arrays. One algorithm runs in quadratic time O(n^2) by applying the definition directly. A more efficient linear time O(n) algorithm is also presented that maintains a running sum. Asymptotic analysis determines the worst-case running time of an algorithm as a function of the input size using big-O notation. This provides an analysis of algorithms that is independent of implementation details and hardware.
The document discusses algorithms and their analysis. It begins by defining an algorithm and listing requirements like being unambiguous and finite. It describes writing algorithms using pseudocode or flowcharts and proving their correctness. The document then discusses analyzing algorithms by measuring their time and space efficiency using orders of growth. It explains analyzing best, worst, and average cases and counting basic operations. Finally, it provides examples of analyzing simple algorithms involving if statements and loops.
This document provides an introduction to algorithms and algorithm problem solving. It discusses understanding the problem, designing an algorithm, proving correctness, analyzing the algorithm, and coding the algorithm. It also provides examples of algorithm problems involving air travel, a xerox shop, document similarity, and drawing geometric figures. Key aspects of algorithms like being unambiguous, having well-defined inputs and outputs, and being finite are explained. Techniques for exact and approximate algorithms are also covered.
The document discusses algorithm analysis and computational complexity, specifically focusing on time complexity and big O notation. It defines key concepts like best case, average case, and worst case scenarios. Common time complexities like constant, logarithmic, linear, quadratic, and exponential functions are examined. Examples are provided to demonstrate how to calculate the time complexity of different algorithms using big O notation. The document emphasizes that worst case analysis is most useful for program design and comparing algorithms.
This document provides an introduction to the analysis of algorithms. It defines an algorithm and lists key properties including being finite, definite, and able to produce the correct output for any valid input. Common computational problems and basic algorithm design strategies are outlined. Asymptotic notations for analyzing time and space efficiency are introduced. Examples of algorithms for calculating the greatest common divisor and determining if a number is prime are provided and analyzed. Fundamental data structures and techniques for analyzing recursive algorithms are also discussed.
Algorithm and its Properties
Computational Complexity
TIME COMPLEXITY
SPACE COMPLEXITY
Complexity Analysis and Asymptotic notations.
Big-oh-notation (O)
Omega-notation (Ω)
Theta-notation (Θ)
The Best, Average, and Worst Case Analyses.
COMPLEXITY Analyses EXAMPLES.
Comparing GROWTH RATES
Dr. James Mountstephens will be teaching the Algorithm Analysis course this semester. He outlines some rules for lectures, including being punctual and not talking during lectures. He describes the course as crucial and difficult, covering complex topics. Students will have quizzes, assignments, a midterm, and final exam. The textbook is recommended reading. The first two lectures may be the hardest, covering introductions to algorithms and their analysis.
This document discusses algorithm analysis tools. It explains that algorithm analysis is used to determine which of several algorithms to solve a problem is most efficient. Theoretical analysis counts primitive operations to approximate runtime as a function of input size. Common complexity classes like constant, linear, quadratic, and exponential time are defined based on how quickly runtime grows with size. Big-O notation represents the asymptotic upper bound of a function's growth rate to classify algorithms.
This document summarizes a lecture on dynamic data structures and tree structures. It discusses different types of trees like binary trees and binary search trees. It describes how to traverse trees using preorder, inorder and postorder traversal. It also explains how to insert and delete nodes from a sorted binary tree in O(h(T)) time where h(T) is the height of the tree. Other tree types mentioned include 2-3 trees, AVL trees, B-trees and red-black trees.
This document discusses various dynamic data structures including pointers, linked lists, stacks, and queues. It provides details on pointers and how they allow dynamically allocated memory to be addressed. Linked lists, including singly linked, doubly linked, circular lists are covered. Implementation of stacks and queues as first-in last-out and first-in first-out data structures is also summarized. Examples of using each data structure are given.
This document discusses various searching methods for arrays, including linear search, binary search, and table search. Linear search sequentially checks each element until the target is found. Binary search works on sorted data by eliminating half the remaining elements at each step. Table search searches arrays of strings by first comparing individual string characters, then using binary search on the sorted string table. The document provides pseudocode algorithms and examples for each search method.
This document provides an overview of sorting algorithms. It begins by defining sorting and discussing characteristics like indirect sorting, distribution sorting, and stable sorting. It then explains several common sorting algorithms like selection sort, insertion sort, quicksort, bucket sort, radix sort, and merge sort. For each algorithm, it provides examples to illustrate how the algorithm works step-by-step to sort a list of numbers. The document aims to help readers understand different approaches to sorting and how to select the appropriate algorithm for a given sorting problem.
This document discusses various algorithms and problems including:
1) The Towers of Hanoi puzzle and its solution requiring 2n-1 moves for n disks.
2) Permutations and two methods for generating all possible permutations.
3) The n-queens problem of placing n queens on an n×n chessboard without any queens threatening each other, solved using backtracking.
4) Backtracking as a general method for constraint satisfaction problems.
The document discusses different numeral systems including binary, decimal, octal and hexadecimal. It provides steps for converting numbers between these numeral systems, whether it be from decimal to other bases or vice versa. Specifically, it explains dividing or multiplying by the base to convert to/from decimal, and breaking into bits and grouping when converting between octal and hexadecimal.
The document discusses different data types including primitive data types like integer, real, boolean, and char, as well as complex data types like arrays, matrices, and records/structures. For each data type, it provides definitions, examples of values and operations, and in some cases pseudocode. It discusses integer and real numbers in depth, explaining how they are represented and operated on in computers. Boolean logic and truth tables are also covered. The last part provides exercises for readers to practice applying concepts from the document.
This document is the introduction chapter of a book on data structures and algorithms. It outlines what the book covers, including pseudocode examples of common data structures and algorithms. It assumes the reader has basic knowledge of Big O notation, imperative programming, and object-oriented concepts. It provides tips for working through the examples and outlines the book's structure. The book is intended to serve as both a reference and teaching tool for data structures and algorithms.
We are pleased to share with you the latest VCOSA statistical report on the cotton and yarn industry for the month of March 2024.
Starting from January 2024, the full weekly and monthly reports will only be available for free to VCOSA members. To access the complete weekly report with figures, charts, and detailed analysis of the cotton fiber market in the past week, interested parties are kindly requested to contact VCOSA to subscribe to the newsletter.
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...Social Samosa
The Modern Marketing Reckoner (MMR) is a comprehensive resource packed with POVs from 60+ industry leaders on how AI is transforming the 4 key pillars of marketing – product, place, price and promotions.
Introduction to Jio Cinema**:
- Brief overview of Jio Cinema as a streaming platform.
- Its significance in the Indian market.
- Introduction to retention and engagement strategies in the streaming industry.
2. **Understanding Retention and Engagement**:
- Define retention and engagement in the context of streaming platforms.
- Importance of retaining users in a competitive market.
- Key metrics used to measure retention and engagement.
3. **Jio Cinema's Content Strategy**:
- Analysis of the content library offered by Jio Cinema.
- Focus on exclusive content, originals, and partnerships.
- Catering to diverse audience preferences (regional, genre-specific, etc.).
- User-generated content and interactive features.
4. **Personalization and Recommendation Algorithms**:
- How Jio Cinema leverages user data for personalized recommendations.
- Algorithmic strategies for suggesting content based on user preferences, viewing history, and behavior.
- Dynamic content curation to keep users engaged.
5. **User Experience and Interface Design**:
- Evaluation of Jio Cinema's user interface (UI) and user experience (UX).
- Accessibility features and device compatibility.
- Seamless navigation and search functionality.
- Integration with other Jio services.
6. **Community Building and Social Features**:
- Strategies for fostering a sense of community among users.
- User reviews, ratings, and comments.
- Social sharing and engagement features.
- Interactive events and campaigns.
7. **Retention through Loyalty Programs and Incentives**:
- Overview of loyalty programs and rewards offered by Jio Cinema.
- Subscription plans and benefits.
- Promotional offers, discounts, and partnerships.
- Gamification elements to encourage continued usage.
8. **Customer Support and Feedback Mechanisms**:
- Analysis of Jio Cinema's customer support infrastructure.
- Channels for user feedback and suggestions.
- Handling of user complaints and queries.
- Continuous improvement based on user feedback.
9. **Multichannel Engagement Strategies**:
- Utilization of multiple channels for user engagement (email, push notifications, SMS, etc.).
- Targeted marketing campaigns and promotions.
- Cross-promotion with other Jio services and partnerships.
- Integration with social media platforms.
10. **Data Analytics and Iterative Improvement**:
- Role of data analytics in understanding user behavior and preferences.
- A/B testing and experimentation to optimize engagement strategies.
- Iterative improvement based on data-driven insights.
End-to-end pipeline agility - Berlin Buzzwords 2024Lars Albertsson
We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
Codeless Generative AI Pipelines
(GenAI with Milvus)
https://ml.dssconf.pl/user.html#!/lecture/DSSML24-041a/rate
Discover the potential of real-time streaming in the context of GenAI as we delve into the intricacies of Apache NiFi and its capabilities. Learn how this tool can significantly simplify the data engineering workflow for GenAI applications, allowing you to focus on the creative aspects rather than the technical complexities. I will guide you through practical examples and use cases, showing the impact of automation on prompt building. From data ingestion to transformation and delivery, witness how Apache NiFi streamlines the entire pipeline, ensuring a smooth and hassle-free experience.
Timothy Spann
https://www.youtube.com/@FLaNK-Stack
https://medium.com/@tspann
https://www.datainmotion.dev/
milvus, unstructured data, vector database, zilliz, cloud, vectors, python, deep learning, generative ai, genai, nifi, kafka, flink, streaming, iot, edge
Predictably Improve Your B2B Tech Company's Performance by Leveraging DataKiwi Creative
Harness the power of AI-backed reports, benchmarking and data analysis to predict trends and detect anomalies in your marketing efforts.
Peter Caputa, CEO at Databox, reveals how you can discover the strategies and tools to increase your growth rate (and margins!).
From metrics to track to data habits to pick up, enhance your reporting for powerful insights to improve your B2B tech company's marketing.
- - -
This is the webinar recording from the June 2024 HubSpot User Group (HUG) for B2B Technology USA.
Watch the video recording at https://youtu.be/5vjwGfPN9lw
Sign up for future HUG events at https://events.hubspot.com/b2b-technology-usa/
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...Aggregage
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
2. Content Lecture 2 Part 1
Performance
Introduction
Recursive
Examples
Recursion vs. Iteration
Primitive Recursion Function
Peano-Hilbert Curve
Turtle Graphics
23/10/2018 Lecture 2 Algorithm Part 1 2
3. Performance
Performance in computer system plays a significant rule
The performance is general presented in the O-Notation
(invented 1894 by Paul Bachmann) called Big-O-Notation
Big-O-Notation describes the limiting behavior of a
function when the argument tends towards a particular
value or infinity
Big-O-Notation is nowadays mostly used to express the
worst case or average case running time or memory usage
of an algorithm in a way that is independent of computer
architecture
This helps to compare the general effectiveness of an
algorithm
23/10/2018 Lecture 2 Algorithm Part 1 3
4. Performance
Definition
Be n the size of the data or any other problem related size
f(n) = O(g(n)) for n ϵ N,
if M, n0 ϵ N (element of the set N) exist such that
|f(n)| ≤ M|g(n)| for all n ≥ n0
Simpler:
f(n) = O(g(n)) for n ∞
With other words: g(n) is a upper or lower bound for the
function f(n).
23/10/2018 Lecture 2 Algorithm Part 1 4
5. Performance
The formal definition of Big-O-Notation is not used
directly
The Big-O-Notation for a function f(x) is derived by the
following rules:
If f(x) is a sum of several terms the one with the largest
growth rate is kept and all others are ignored
If f(x) is a product of several factors, any constants
(independent of x) are ignored
23/10/2018 Lecture 2 Algorithm Part 1 5
6. Performance
Example
f(x) = 7x3 – 3x2 + 11
The function is the sum of three terms: 7x3, -3x2, 11
The one with the largest growth rate it the one with the
largest exponent: 7x3
This term is a product of 7 and x3
Because the factor 7 doesn’t depend on x it can be ignored
As a result you got: f(x) = O(g(x)) = O(x3) with g(x) = x3
23/10/2018 Lecture 2 Algorithm Part 1 6
7. Performance
List of standard Big-O Notations for comparing algorithm:
O(1)
Constant effort, independent from n
O(n)
Linear effort
23/10/2018 Lecture 2 Algorithm Part 1 7
13. Algorithm
A set of instructions done sequentially
A list of well-defined instructions to complete a given task
To perform a specified task in a specific order
It is an effective method to solve a problem expressed as a
finite sequence of steps
It is not limited to finite (nondeterministic algorithm)
In computer systems an algorithm is defined as an instance
of logic written in software in order to intend the computer
machine to do something
23/10/2018 Lecture 2 Algorithm Part 1 13
14. Algorithm
It is important to define the algorithm rigorously that
means that all possible circumstances for the given task
should be handled
There is an initial state
The introductions are done as a series of steps
The criteria for each step must be clear and computable
The order of the steps performed is always critical to the
algorithm
The flow of control is from the top to the bottom (top-
down) that means from a start state to an end state
Termination might be given to the algorithm but some
algorithm could also run forever without termination
23/10/2018 Lecture 2 Algorithm Part 1 14
15. Algorithm
All algorithms are classified in 6 classes:
Recursion vs. Iteration
Logical
Serial(Sequential)/Parallel/Distributed
Deterministic/Non-deterministic
Exact/Approximate
Quantum
23/10/2018 Lecture 2 Algorithm Part 1 15
16. Algorithm
Recursion vs. Iteration
Recursive algorithm makes references to itself repeatedly until a
finale state is reached
Iterative algorithms use repetitive constructs like loops and
sometimes additional data structures
More details later
Logical
Algorithm = logic + control
The logic component defines the axioms that are used in the
computation, the control components determines the way in
which deduction is applied to the axioms
Example The programming language Prolog
23/10/2018 Lecture 2 Algorithm Part 1 16
17. Algorithm
Serial(Sequential)/Parallel/Distributed
Serial Algorithm performing tasks serial that means one step by
one step; Each step is a single operation
Parallel Algorithm performing multiple operations in each step
Distributed Algorithm performing tasks distributed
Examples
Serial: Calculating the sum of n numbers
Parallel: Network
Distributed: GUI, E-mail
23/10/2018 Lecture 2 Algorithm Part 1 17
18. Algorithm
Deterministic/Non-deterministic
Deterministic algorithm solve the problem with exact decision
at every step
Non-deterministic solve problem by guessing through the use
of heuristics
Example Shopping List
Buy all items in any order nondeterministic algorithm
Buy all items in a given order deterministic algorithm
Exact/Approximate
Exact algorithms reach an exact solution
Approximation algorithms searching for an approximation
close to the true solution
23/10/2018 Lecture 2 Algorithm Part 1 18
19. Algorithm
Example: To find an approximate solutions to optimization
problems like storehouses for shops
Quantum
Quantum algorithm running on a realistic model of quantum
computation
It is a step-by-step procedure, where each of the steps can be
performed on a quantum computer
It might be able to solve some problems faster than classical
algorithms
Example: Shor's algorithm for integer factorization
23/10/2018 Lecture 2 Algorithm Part 1 19
20. Recursion
Many problems, models and phenomenon have a self-reflecting
form in which the own structure is contained in different
variants
This can be a mathematical formula as well as a natural
phenomenon
If this structure is adopted in a mathematical definition, an
algorithm or a data structure than this is called a recursion
23/10/2018 Lecture 2 Algorithm Part 1 20
21. Recursion
Definition
Recursion is the process of repeating items in a self-similar way.
Recursion definitions are only reasonable if something is only
defined by himself in a simpler form
The limit will be a trivial case
This case needs no recursion anymore
A common joke is the following "definition" of recursion
(Catb.org. Retrieved 2010-04-07.):
Recursion
See "Recursion"
23/10/2018 Lecture 2 Algorithm Part 1 21
22. Recursion
Examples
Language
A child couldn't sleep, so her mother told a story about a little frog,
who couldn't sleep, so the frog's mother told a story about a little bear,
who couldn't sleep, so bear's mother told a story about a little weasel
...who fell asleep.
...and the little bear fell asleep;
...and the little frog fell asleep;
...and the child fell asleep.
23/10/2018 Lecture 2 Algorithm Part 1 22
23. Mathematical examples
Factorial
The Factorial of a number if defined as n! = n*(n-1)*(n-2)*…*1
Therefore:
F(n) = n! for n > 0 is defined: F(1) = 1
F(n) = n * F(n-1) for n > 1
Program code in C/C++
int factorial(int number) {
if (number <= 1) //trivial case
return number;
return number * (factorial(number - 1)); //recursive call
}
23/10/2018 Lecture 2 Algorithm Part 1 23
25. Recursion
Because of recursion it is possible that more than one
incarnation of the procedure exists at one time
It is important that there is finiteness in the recursion (a
trivial case)
For example a query decided if there is another recursion
call or not
Otherwise you will have an endless recursive algorithm
calling itself again and again
In the example it was the case that number <= 1
23/10/2018 Lecture 2 Algorithm Part 1 25
26. Recursion
Definition
The depth of a recursion is the number of recursion calls.
Example
For factorial the depth of F(n) is n.
depth(F(n)) = n
Because in every step you call the recursion only one time.
Therefore depth(F(5)) = 5
23/10/2018 Lecture 2 Algorithm Part 1 26
27. Recursion
There are two ways to implement a recursion:
Starting from an initial state and deriving new states
which every use of the recursion rules
Starting from a complex state and simplifying successive
through using the recursion rules until a trivial state is
reached which needs no use of recursion (see Faculty)
How to build a recursion depends mainly on:
How readable and understandable the alternative
variants are
Performance and memory issues
23/10/2018 Lecture 2 Algorithm Part 1 27
28. Mathematical examples
Greatest Common Divisor
The Greatest Common Divisor gcd(x,y) of the integer x
and y s is the product of all common prime number factors
of x and y
To calculate the gcd you can use the following formula:
gcd(x1, y1) = gcd(y1, x1 % y1) =gcd(x2, y2) =
= gcd(y2, x2 % y2) = … = gcd(xk, 0)
The final gcd(xk, 0) = xk is the Greatest common divisor
23/10/2018 Lecture 2 Algorithm Part 1 28
29. Mathematical examples
Implementation in C
int mygcd(int x, int y) {
if (y == 0) //trivial case
return x;
else
return mygcd(y, x % y); //recursive call
}
gcd(34, 16) = gcd(16, 2) = gcd(2, 0) = 2
gcd(127, 36)=gcd(36, 19)=gcd(19, 17)=gcd(17, 2)=gcd(2, 1)=gcd(1, 0)=1
23/10/2018 Lecture 2 Algorithm Part 1 29
30. Mathematical examples
Fibonacci sequence
Fibonacci sequence is one of the classical example of
recursion
Leonardo Fibonacci was an Italian mathematician (around
1180 – 1240)
The Fibonacci sequence can be found also in nature
(plants)
The sequence is defined as:
F(0) = 0 (base case)
F(1) = 1 (base case)
F(n) = F(n-1) + F(n-2) (recursion) for all n > 1 with n ϵ N
23/10/2018 Lecture 2 Algorithm Part 1 30
32. Mathematical examples
Ackermann function
Wilhelm Ackermann was a German mathematician (1896 –
1962)
The Ackermann function is used as a benchmark of the
ability of a compiler to optimize recursion
The function is defined as:
𝐴 𝑚, 𝑛 =
𝑛 + 1 𝑖𝑓 𝑚 = 0
𝐴 𝑚 − 1, 1 𝑖𝑓 𝑚 > 0 𝑎𝑛𝑑 𝑛 = 0
𝐴 𝑚 − 1, 𝐴 𝑚, 𝑛 − 1 𝑖𝑓 𝑚 > 0 𝑎𝑛𝑑 𝑛 > 0
23/10/2018 Lecture 2 Algorithm Part 1 32
33. Mathematical examples
Implementation in C
int ackermann(int m, int n) {
if (m == 0) //trivial case
return n + 1;
else if (n == 0) //recursive call
return ackermann(m – 1, 1);
else //recursive call
return ackermann(m – 1, ackermann(m, n – 1));
}
23/10/2018 Lecture 2 Algorithm Part 1 33
34. Recursion vs. Iteration
Use of recursion in an algorithm has both advantages and
disadvantages
The main advantage is usually simplicity
The main disadvantage is often that the algorithm may require
large amounts of memory if the depth of the recursion is very
high
Many problems are solved more elegant and efficient if they are
implemented by using iteration
This is especially the case for tail recursions which can be
replaced immediately by a loop, because no nested case exists
which has to be represented by a recursion
The recursive call happens only at the end of the algorithm
Recursion and iteration are not really contrasts because every
recursion can also be implemented as iteration
23/10/2018 Lecture 2 Algorithm Part 1 34
35. Recursion vs. Iteration
Tail recursion
int tail_recursion(…) {
if (simple_case) //trivial case
/*do something */;
else
/*do something */;
tail_recursion(…); // only recursive call
}
23/10/2018 Lecture 2 Algorithm Part 1 35
36. Recursion vs. Iteration
Examples
The factorial algorithm is more efficient if you use an iterative
implementation
int factorial_iterative(int number) {
int result = 1;
while (number > 0) {
result *= number;
number--;
}
return number;
}
Whereas the Ackermann function is an example where it is more
efficient and simpler to implement it with recursion
23/10/2018 Lecture 2 Algorithm Part 1 36
37. Primitive Recursion Function
Definition
The primitive recursive functions are among the number-
theoretic functions, which are functions from the natural
numbers (non negative integers) {0, 1, 2 , ...} to the natural
numbers
These functions take n arguments for some natural
number n and are called n-ary
Functions in number theory are primitive recursive
Important also in proof theory (proof by induction)
23/10/2018 Lecture 2 Algorithm Part 1 37
38. Primitive Recursion Function
The basic primitive recursive functions are given by these
axioms:
Constant function: The 0-ary constant function 0 is
primitive recursive
Successor function: The 1-ary successor function S,
which returns the successor of its argument, is primitive
recursive. That is, S(k) = k + 1
Projection function: For every n ≥ 1 and each i with 1 ≤
i ≤ n, the n-ary projection function Pi
n, which returns
its ith argument, is primitive recursive
23/10/2018 Lecture 2 Algorithm Part 1 38
40. Peano-Hilbert curve
The Peano-Hilbert curves were discovered by Peano and
Hilbert in 1890/1891
They convert against a function which maps the interval
[0,1] of the real numbers surjective on the area [0,1] x [0,1]
and is in the same time constant
That means through repeatedly execute the function rules
you will reach every point in a square
23/10/2018 Lecture 2 Algorithm Part 1 40
41. Peano-Hilbert curve
The Peano-Hilbert Curve can be expressed by a rewrite system (L-
system):
Alphabet: L, R
Constants: F, +, −
Axiom: L
Production rules:
L → +RF−LFL−FR+
R → −LF+RFR+FL−
F: draw forward
+: turn left 90°
-: turn right 90°
23/10/2018 Lecture 2 Algorithm Part 1 41
42. Peano-Hilbert curve
First order: L +F-F-F+ R -F+F+F-
Second order: +RF-LFL-FR+
+-F+F+F-F-+F-F-F+F+F-F-F+-F-F+F+F-+
Used in computer science
For example to map the range of IP addresses used by computers into a
picture
23/10/2018 Lecture 2 Algorithm Part 1 42
43. Turtle Graphics
Turtle Graphics are connected to the program language Logo
(1967)
There is no absolute position in a coordination system
All introductions are relative to the actual position
A simple form would be:
Alphabet: X, Y
Constants: F, +, −
Production rules:
Initial Value: FX
X → X+YF+
Y → -FX-Y
23/10/2018 Lecture 2 Algorithm Part 1 43
F: draw forward
+: turn left 90°
-: turn right 90°
44. Turtle Graphics
Level 1: FX = F+F+
Level 2: FX = FX+YF+ = F+F++-F-F+
23/10/2018 Lecture 2 Algorithm Part 1 44