The document discusses minimum spanning trees and Kruskal's algorithm. It begins by defining minimum spanning trees and the cut property. It then explains Kruskal's algorithm, which finds a minimum spanning tree by iteratively adding the lightest edge that does not form a cycle. It uses disjoint sets to determine if an edge connects two vertices in different sets, and thus would not form a cycle.
This document discusses shortest path algorithms like Dijkstra's algorithm. It uses an example graph to demonstrate how Dijkstra's algorithm works to find the shortest path between vertices. The algorithm works by gradually filling in the shortest path lengths from the starting vertex to all other vertices. It begins by setting the shortest path to the starting vertex as 0 and all others as infinity. It then iteratively updates the shortest path lengths by exploring the neighbors of vertices whose shortest path is known.
The document discusses different methods for representing graphs in a computer, including adjacency matrices and adjacency lists. An adjacency matrix uses a 2D matrix to represent a graph, with rows and columns representing vertices and a 1 or 0 in the cell indicating whether an edge exists between those two vertices. While this allows directly checking for edges, it takes up more space than necessary for sparse graphs. Adjacency lists store only the relevant adjacent vertices for each vertex.
The document discusses two problems solved using dynamic programming: longest palindrome and team division. For longest palindrome, it defines a table to store the length of the longest palindrome between two indices, and a recurrence relation to consider if the characters at the indices are the same or different. For team division, the goal is to minimize the maximum sum of teams when dividing people into teams. It defines a table to store the minimum value for a given number of teams, and a recurrence relation to try dividing at each index and take the minimum.
The document provides information about a music video presentation for the song "Glowing Eyes" by the band Twenty One Pilots. It introduces the song, band, and album. It then discusses key members of Twenty One Pilots and their history. Several lyrics from "Glowing Eyes" are presented along with ideas for translating those lyrics visually through camera shots, costumes, props, and locations in the music video.
Version Uncontrolled - How to Manage Your Version Control (whitepaper)Revelation Technologies
This document discusses best practices for managing version control. It begins by describing the differences between centralized and distributed version control systems like SVN and Git. It then provides examples of common version control commands for creating repositories, committing changes, and branching/merging code. The document recommends adopting a structured workflow like feature branching or the Git flow model to improve collaboration. It stresses the importance of following naming conventions for branches.
- Dr. Shardul B. Bhatt is a Research Associate at IPCA Laboratories LTD with a Ph.D. in Chemistry from MS University of Baroda.
- His experience includes positions as a Senior Research Fellow, Temporary Lecturer in Analytical Chemistry, and R&D Officer.
- As a Research Associate, his responsibilities include analytical method development using techniques like UPLC, HPLC, GC, and ion chromatography as well as authoring SOPs and calibration activities.
Instagram Event Management&Diffusion Via PinterestVincent Tervooren
This document discusses managing Instagram content from Swatch-sponsored sports events. It recommends identifying an event hashtag and spreading its use among athletes and the public. Images posted with the hashtag can then be monitored on platforms like Statigram and pinned to a dedicated Pinterest board. This board's RSS feed allows the curated images to be automatically published and displayed on social media and event/brand websites, spreading visibility of the Swatch brand.
This is a Power Point Presentation that only consists of veggies and fruits with five possible translations. This is a material to teach vocabulary shared by a teacher I had at University. Hope you enjoy it!
This document discusses shortest path algorithms like Dijkstra's algorithm. It uses an example graph to demonstrate how Dijkstra's algorithm works to find the shortest path between vertices. The algorithm works by gradually filling in the shortest path lengths from the starting vertex to all other vertices. It begins by setting the shortest path to the starting vertex as 0 and all others as infinity. It then iteratively updates the shortest path lengths by exploring the neighbors of vertices whose shortest path is known.
The document discusses different methods for representing graphs in a computer, including adjacency matrices and adjacency lists. An adjacency matrix uses a 2D matrix to represent a graph, with rows and columns representing vertices and a 1 or 0 in the cell indicating whether an edge exists between those two vertices. While this allows directly checking for edges, it takes up more space than necessary for sparse graphs. Adjacency lists store only the relevant adjacent vertices for each vertex.
The document discusses two problems solved using dynamic programming: longest palindrome and team division. For longest palindrome, it defines a table to store the length of the longest palindrome between two indices, and a recurrence relation to consider if the characters at the indices are the same or different. For team division, the goal is to minimize the maximum sum of teams when dividing people into teams. It defines a table to store the minimum value for a given number of teams, and a recurrence relation to try dividing at each index and take the minimum.
The document provides information about a music video presentation for the song "Glowing Eyes" by the band Twenty One Pilots. It introduces the song, band, and album. It then discusses key members of Twenty One Pilots and their history. Several lyrics from "Glowing Eyes" are presented along with ideas for translating those lyrics visually through camera shots, costumes, props, and locations in the music video.
Version Uncontrolled - How to Manage Your Version Control (whitepaper)Revelation Technologies
This document discusses best practices for managing version control. It begins by describing the differences between centralized and distributed version control systems like SVN and Git. It then provides examples of common version control commands for creating repositories, committing changes, and branching/merging code. The document recommends adopting a structured workflow like feature branching or the Git flow model to improve collaboration. It stresses the importance of following naming conventions for branches.
- Dr. Shardul B. Bhatt is a Research Associate at IPCA Laboratories LTD with a Ph.D. in Chemistry from MS University of Baroda.
- His experience includes positions as a Senior Research Fellow, Temporary Lecturer in Analytical Chemistry, and R&D Officer.
- As a Research Associate, his responsibilities include analytical method development using techniques like UPLC, HPLC, GC, and ion chromatography as well as authoring SOPs and calibration activities.
Instagram Event Management&Diffusion Via PinterestVincent Tervooren
This document discusses managing Instagram content from Swatch-sponsored sports events. It recommends identifying an event hashtag and spreading its use among athletes and the public. Images posted with the hashtag can then be monitored on platforms like Statigram and pinned to a dedicated Pinterest board. This board's RSS feed allows the curated images to be automatically published and displayed on social media and event/brand websites, spreading visibility of the Swatch brand.
This is a Power Point Presentation that only consists of veggies and fruits with five possible translations. This is a material to teach vocabulary shared by a teacher I had at University. Hope you enjoy it!
JSP provides a scripting environment for Java code to generate dynamic web page content. Key elements include directives like <jsp:include> and <jsp:forward> for page composition, scriptlets for Java code, and expressions for output. The Expression Language (EL) offers a simpler way than scriptlets to access data and call methods. JSPs are compiled into servlets, so they can use Java classes and web technologies like MVC.
El Bugatti Veyron es un superdeportivo francés producido desde 2001 con un motor W16 de 1,001 caballos. Alcanzó un récord de velocidad en 2007 de 407 km/h. Se han producido varias versiones especiales como el Veyron Grand Sport, Super Sport y Sang Bleu con diferentes características estéticas y de rendimiento.
The document discusses bubble sort, a simple sorting algorithm where each pair of adjacent elements is compared and swapped if in the wrong order. This process is repeated, with the highest/lowest value "bubbling" to the top/bottom, until the list is fully sorted. Although simple, bubble sort is slow compared to other algorithms. It can be useful if data is usually sorted but with some out-of-order elements. Pseudocode and an example are provided to illustrate the bubble sort process.
Polyhedral compilation uses the polyhedral model to represent programs as systems of affine inequalities over iteration variables. This allows loop transformations like fusion, distribution, skewing and reversal to be expressed as affine mappings on the iteration space. The key aspects are representing the iteration domain, scheduling functions that determine the execution order of statements, and memory accesses in terms of iteration vectors. Loop transformations are specified by changing the scheduling functions to map iterations to new logical execution times while preserving semantics. This enables optimizations at the level of whole programs or subprograms.
The document provides an introduction to IT risk management concepts and the company's IT Risk Management program. It defines key risk management terminology like risk, likelihood, impact, gross risk, net risk, and target risk. It also outlines the IT risk management process, including roles like the IT risk manager and process owners. The training agenda covers an introduction to risk management terminology, an overview of the company's IT risk management process, and a question period.
This document discusses Amadeus, a global technology company that provides solutions to help travel agencies, airlines, hotels and other travel providers improve their business performance. It focuses on how digital technologies are revolutionizing the travel industry by enabling more distribution channels, products and expert travelers. The document also examines challenges like disintermediation and proposes how Amadeus is innovating through approaches like virtual and augmented reality, artificial intelligence, personalization and chatbots to help travel providers connect with customers throughout the travel journey from inspiration to fulfillment.
13. Indexing MTrees - Data Structures using C++ by Varsha Patilwidespreadpromotion
This document discusses various data structures and file organization techniques. It covers indexing techniques like B-trees and tries, as well as file organization methods like sequential and hashed indexes. Specific data structures covered include B-trees, B+-trees, tries, splay trees, red-black trees, and KD-trees. Operations for searching, inserting and deleting records in these tree structures are also outlined.
The document discusses the divide-and-conquer algorithm design paradigm. It explains that a problem is divided into smaller subproblems, the subproblems are solved independently, and then the solutions are combined. Recurrence equations can be used to analyze the running time of divide-and-conquer algorithms. The document provides examples of solving recurrences using methods like the recursion tree method and the master theorem.
This document describes Floyd's algorithm for solving the all-pairs shortest path problem in graphs. It begins with an introduction and problem statement. It then describes Dijkstra's algorithm as a greedy method for finding single-source shortest paths. It discusses graph representations and traversal methods. Finally, it provides pseudocode and analysis for Floyd's dynamic programming algorithm, which finds shortest paths between all pairs of vertices in O(n3) time.
Commercial company brochure beglinwoods architectsSimon Woods
Beglin Woods Architects Limited is a leading architectural firm in East Africa with over 20 staff. They have extensive experience designing various building types across multiple sectors. Their design process focuses on developing concepts quickly with client input while maintaining high quality, functionality, and budget constraints. They aim to provide first-class service and buildings that are aesthetically pleasing and economical.
dynamic programming complete by Mumtaz Ali (03154103173)Mumtaz Ali
The document discusses dynamic programming, including its meaning, definition, uses, techniques, and examples. Dynamic programming refers to breaking large problems down into smaller subproblems, solving each subproblem only once, and storing the results for future use. This avoids recomputing the same subproblems repeatedly. Examples covered include matrix chain multiplication, the Fibonacci sequence, and optimal substructure. The document provides details on formulating and solving dynamic programming problems through recursive definitions and storing results in tables.
The document discusses dynamic programming and covers the longest increasing subsequence problem. It begins by defining dynamic programming as breaking down a problem into smaller subproblems and using previously solved subproblems to solve larger ones. It then covers the longest increasing subsequence problem in detail, showing how to define a table, find the recurrence relation, and calculate the solution. The example problem is finding the longest increasing subsequence of the numbers 5, 2, 8, 6, 3, 6, 9, 7. The solution is shown to be the sequence 2, 3, 6, 9, which has a length of 4.
This document summarizes a seminar on algorithm verification and efficiency. It discusses verifying that algorithms are correct through mathematical proofs and analyzing computational efficiency by calculating the number of operations. Basic arithmetic algorithms like multiplication and finding the greatest common divisor are analyzed. The most efficient multiplication algorithm runs in O(log y) time while the naive approach is O(y). Euclid's algorithm for finding the greatest common divisor runs in O(log n) time.
Cassandra Community Webinar | Practice Makes Perfect: Extreme Cassandra Optim...DataStax
Ooyala has been using Apache Cassandra since version 0.4.Their data ingest volume has exploded since 0.4 and Cassandra has scaled along with it. In this webinar, Al will share lessons that he has learned across an array of topics from an operational perspective including how to manage, tune, and scale Cassandra in a production environment.
Speaker: Al Tobey, Tech Lead, Compute and Data Services at Ooyala
Al Tobey is Tech Lead of the Compute and Data services team at Ooyala. His team develops and operates Ooyala's internal big data platform, consisting of Apache Cassandra, Hadoop, and internally developed tools. When not in front of a computer, Al is a father, husband, and trombonist.
The document describes a final project to create a program that solves Sudoku puzzles using depth-first search (DFS). It begins by introducing Sudoku and stating the objective to implement an AI technique. It then describes the approach of using DFS to recursively try values in each grid position and backtrack if invalid. Test cases are used to evaluate the program. It was able to correctly solve sample puzzles, with the main challenge being checking the 3x3 subgrids. The conclusion states the program successfully implemented DFS to solve Sudoku puzzles.
Building scalable applications while scaling your infrastructure by rhommel l...NETWAYS
Rhommel Lamas discusses Puppet configuration at the company 3scale. Some key points include:
- 3scale has been running Puppet 3.0+ since July 2013 with 2 Puppet masters behind Nginx and Unicorn load balancers.
- Environments include production, preview, and staging. Puppet development uses Git and tools like Puppet-lint and Puppet-rspec.
- Other Puppet infrastructure at 3scale includes PuppetDB, the Foreman as an ENC, MCollective, and RabbitMQ.
JSP provides a scripting environment for Java code to generate dynamic web page content. Key elements include directives like <jsp:include> and <jsp:forward> for page composition, scriptlets for Java code, and expressions for output. The Expression Language (EL) offers a simpler way than scriptlets to access data and call methods. JSPs are compiled into servlets, so they can use Java classes and web technologies like MVC.
El Bugatti Veyron es un superdeportivo francés producido desde 2001 con un motor W16 de 1,001 caballos. Alcanzó un récord de velocidad en 2007 de 407 km/h. Se han producido varias versiones especiales como el Veyron Grand Sport, Super Sport y Sang Bleu con diferentes características estéticas y de rendimiento.
The document discusses bubble sort, a simple sorting algorithm where each pair of adjacent elements is compared and swapped if in the wrong order. This process is repeated, with the highest/lowest value "bubbling" to the top/bottom, until the list is fully sorted. Although simple, bubble sort is slow compared to other algorithms. It can be useful if data is usually sorted but with some out-of-order elements. Pseudocode and an example are provided to illustrate the bubble sort process.
Polyhedral compilation uses the polyhedral model to represent programs as systems of affine inequalities over iteration variables. This allows loop transformations like fusion, distribution, skewing and reversal to be expressed as affine mappings on the iteration space. The key aspects are representing the iteration domain, scheduling functions that determine the execution order of statements, and memory accesses in terms of iteration vectors. Loop transformations are specified by changing the scheduling functions to map iterations to new logical execution times while preserving semantics. This enables optimizations at the level of whole programs or subprograms.
The document provides an introduction to IT risk management concepts and the company's IT Risk Management program. It defines key risk management terminology like risk, likelihood, impact, gross risk, net risk, and target risk. It also outlines the IT risk management process, including roles like the IT risk manager and process owners. The training agenda covers an introduction to risk management terminology, an overview of the company's IT risk management process, and a question period.
This document discusses Amadeus, a global technology company that provides solutions to help travel agencies, airlines, hotels and other travel providers improve their business performance. It focuses on how digital technologies are revolutionizing the travel industry by enabling more distribution channels, products and expert travelers. The document also examines challenges like disintermediation and proposes how Amadeus is innovating through approaches like virtual and augmented reality, artificial intelligence, personalization and chatbots to help travel providers connect with customers throughout the travel journey from inspiration to fulfillment.
13. Indexing MTrees - Data Structures using C++ by Varsha Patilwidespreadpromotion
This document discusses various data structures and file organization techniques. It covers indexing techniques like B-trees and tries, as well as file organization methods like sequential and hashed indexes. Specific data structures covered include B-trees, B+-trees, tries, splay trees, red-black trees, and KD-trees. Operations for searching, inserting and deleting records in these tree structures are also outlined.
The document discusses the divide-and-conquer algorithm design paradigm. It explains that a problem is divided into smaller subproblems, the subproblems are solved independently, and then the solutions are combined. Recurrence equations can be used to analyze the running time of divide-and-conquer algorithms. The document provides examples of solving recurrences using methods like the recursion tree method and the master theorem.
This document describes Floyd's algorithm for solving the all-pairs shortest path problem in graphs. It begins with an introduction and problem statement. It then describes Dijkstra's algorithm as a greedy method for finding single-source shortest paths. It discusses graph representations and traversal methods. Finally, it provides pseudocode and analysis for Floyd's dynamic programming algorithm, which finds shortest paths between all pairs of vertices in O(n3) time.
Commercial company brochure beglinwoods architectsSimon Woods
Beglin Woods Architects Limited is a leading architectural firm in East Africa with over 20 staff. They have extensive experience designing various building types across multiple sectors. Their design process focuses on developing concepts quickly with client input while maintaining high quality, functionality, and budget constraints. They aim to provide first-class service and buildings that are aesthetically pleasing and economical.
dynamic programming complete by Mumtaz Ali (03154103173)Mumtaz Ali
The document discusses dynamic programming, including its meaning, definition, uses, techniques, and examples. Dynamic programming refers to breaking large problems down into smaller subproblems, solving each subproblem only once, and storing the results for future use. This avoids recomputing the same subproblems repeatedly. Examples covered include matrix chain multiplication, the Fibonacci sequence, and optimal substructure. The document provides details on formulating and solving dynamic programming problems through recursive definitions and storing results in tables.
The document discusses dynamic programming and covers the longest increasing subsequence problem. It begins by defining dynamic programming as breaking down a problem into smaller subproblems and using previously solved subproblems to solve larger ones. It then covers the longest increasing subsequence problem in detail, showing how to define a table, find the recurrence relation, and calculate the solution. The example problem is finding the longest increasing subsequence of the numbers 5, 2, 8, 6, 3, 6, 9, 7. The solution is shown to be the sequence 2, 3, 6, 9, which has a length of 4.
This document summarizes a seminar on algorithm verification and efficiency. It discusses verifying that algorithms are correct through mathematical proofs and analyzing computational efficiency by calculating the number of operations. Basic arithmetic algorithms like multiplication and finding the greatest common divisor are analyzed. The most efficient multiplication algorithm runs in O(log y) time while the naive approach is O(y). Euclid's algorithm for finding the greatest common divisor runs in O(log n) time.
Cassandra Community Webinar | Practice Makes Perfect: Extreme Cassandra Optim...DataStax
Ooyala has been using Apache Cassandra since version 0.4.Their data ingest volume has exploded since 0.4 and Cassandra has scaled along with it. In this webinar, Al will share lessons that he has learned across an array of topics from an operational perspective including how to manage, tune, and scale Cassandra in a production environment.
Speaker: Al Tobey, Tech Lead, Compute and Data Services at Ooyala
Al Tobey is Tech Lead of the Compute and Data services team at Ooyala. His team develops and operates Ooyala's internal big data platform, consisting of Apache Cassandra, Hadoop, and internally developed tools. When not in front of a computer, Al is a father, husband, and trombonist.
The document describes a final project to create a program that solves Sudoku puzzles using depth-first search (DFS). It begins by introducing Sudoku and stating the objective to implement an AI technique. It then describes the approach of using DFS to recursively try values in each grid position and backtrack if invalid. Test cases are used to evaluate the program. It was able to correctly solve sample puzzles, with the main challenge being checking the 3x3 subgrids. The conclusion states the program successfully implemented DFS to solve Sudoku puzzles.
Building scalable applications while scaling your infrastructure by rhommel l...NETWAYS
Rhommel Lamas discusses Puppet configuration at the company 3scale. Some key points include:
- 3scale has been running Puppet 3.0+ since July 2013 with 2 Puppet masters behind Nginx and Unicorn load balancers.
- Environments include production, preview, and staging. Puppet development uses Git and tools like Puppet-lint and Puppet-rspec.
- Other Puppet infrastructure at 3scale includes PuppetDB, the Foreman as an ENC, MCollective, and RabbitMQ.
The document describes dual-pivot quicksort, which uses two pivot elements rather than one. It summarizes previous research that found dual-pivot quicksort often improves upon classic quicksort by reducing the number of element comparisons and cache misses, though it increases the number of swaps. The document then focuses on Yaroslavskiy's dual-pivot partitioning algorithm, which efficiently arranges elements into three groups - less than the first pivot, between the pivots, and greater than the second pivot - through in-place swapping.
This document outlines the history of database monitoring from 1988 to the present. It describes early monitoring tools like Utlbstat/Utlestat from 1988-1990 that used ratios and averages. Patrol was one of the first database monitors introduced in 1993. M2 from 1994 introduced light-weight monitoring using direct memory access and sampling. Wait events became a key focus area from 1995 onward. Statspack was introduced in 1998 and provided more comprehensive monitoring than previous tools. Spotlight in 1999 made database problem diagnosis very easy without manuals. Later versions incorporated improved graphics, multi-dimensional views of top consumers, and sampling for faster problem identification.
What Quantum Computing is and is not? - Manuel Rudolph, Physicist.Ari Massoudi
This is the slides showed by Manuel Rudolph during his interview-webinar on Episteme Entrepreneur: https://www.episteme-entrepreneur.com/blog/manuel-rudolph-physics-quantum-computing-machine-learning-algorithms
What Quantum Computing is and is not?
A friendly introduction to quantum computing and quantum machine learning algorithms.
Curious about how a quantum computer actually computes and how scientists write algorithms for them?
Quantum computing has been touted with great potential in various fields. Some claims may be overly ambitious, and opinions on the feasibility of building practical machines may differ. Still, there is much to be optimistic about and to be excited for. In this session, Manuel will share his experience as a quantum algorithms researcher with a diverse scientific background-- from university to a quantum computing startup and back. He will begin with a friendly introduction to the foundations of quantum computing, and then talk about his research in quantum machine learning and generative modelling. Manuel's goal is to help develop the first practical algorithms capable of running on the earliest viable quantum computers.
Manuel Rudolph is PhD Candidate in Physics at EPFL (École Polytechnique Fédérale de Lausanne) | Working on Quantum Machine Learning Algorithms | Laboratory of Quantum Information and Computation led by Prof. Zoë Holmes.
Design World Webinar on Engineering tomorrow's robots and drones today. Challenges in robotics, robotics landscape, tools for rapid prototyping, sensors, simulation case study.
This document discusses brute force algorithms and provides an example problem of finding the maximum sum of consecutive elements in an array. It first explains that a brute force approach considers all possible cases to find the solution. For the example, it shows that one can consider all possible starting and ending indices to calculate the sum of each subarray, and take the maximum. This results in an O(n3) time complexity. However, by removing one of the loops, the time complexity can be improved to O(n2), making the algorithm efficient for the given constraints.
During his PhD program at the UCSD Coordinated Robotics Lab, Nick Morozovsky developed several dynamic robotic systems, including Switchblade, a balancing treaded platform, and SkySweeper, a novel cable-locomoting 3D-printed robot. In addition to describing the development process of these robots, and showing the prototypes, Nick will discuss trends in robotics technologies (microprocessors, 3D printing, etc.) that have made prototyping and developing robots faster and cheaper than ever.
A STEM-Maker Level 1 Lesson for System Fluency - Wheel and Axle
What is a Wheel and Axle?
Heavy loads are hard to move by simply pushing
or pulling on them because there are forces that
must be overcome in order for them to move.
One force is gravity, which is the attraction
between the earth and other objects.
This attraction causes the second force known as
friction, which means that the resistance of the
object, as it comes in contact with a surface, must
be overcome before it will move.
The wheel and axle can be used to help move
heavy objects because the surface area of the
wheel is less than the surface area of the load
and this makes it easier to overcome the forces of
gravity and friction.
EzPAARSE is open source software that analyses your locally gathered proxy logfiles and provides you with COUNTER-deduplicated, KBART-formatted and geolocalised reports of your users’ accesses to subscribed e-resources. Come and watch us demo it live to understand how it works and learn how to install it in your institution for producing your own enriched measures and indicators.
Implementing the Split-Apply-Combine model in Clojure and Incanter Tom Faulhaber
These are the slides from my talk to the Bay Area Clojure Group meeting in San Francisco on June 6, 2013.
The slides are not meant to stand alone, so they may not be completely useful if you did not attend.
Here is the description of the talk sent out in advance:
Tom Faulhaber will talk about interactive data analysis focusing on data organization and the split-apply-combine pattern. You'll find that split-apply-combine is a powerful tool that applies to many of the data problems that we look at in Clojure. This pattern is the basis of the popular plyr package developed by Hadley Wickham in the R language.
Tom will demonstrate some basic ideas of data analysis and show how they're implemented in the Incanter system. We'll discuss split-apply-combine and how it's used in Incanter today. Then, we'll discuss how to implement a full version of split-apply-combine in Clojure on top of Incanter's dataset type. Finally, we'll use our implementation to learn about some real data.
Using Bayesian Optimization to Tune Machine Learning ModelsScott Clark
1) Bayesian optimization can be used to efficiently tune the hyperparameters of machine learning models, requiring far fewer evaluations than standard random search or grid search methods to find good hyperparameters.
2) It builds a statistical model called a Gaussian process to model the objective function based on previous evaluations, and uses this to select the most promising hyperparameters to evaluate next in order to optimize an objective metric like accuracy.
3) SigOpt is a service that uses Bayesian optimization to tune machine learning models, outperforming expert humans on tasks like classifying images from CIFAR10 and reducing error rates more than standard methods.
Using Bayesian Optimization to Tune Machine Learning ModelsSigOpt
1. Tuning machine learning models is challenging due to the large number of non-intuitive hyperparameters.
2. Traditional tuning methods like grid search are computationally expensive and can find local optima rather than global optima.
3. Bayesian optimization uses Gaussian processes to build statistical models from prior evaluations to determine the most promising hyperparameters to test next, requiring far fewer evaluations than traditional methods to find better performing models.
James Birnie - Using Many Worlds of Compute Power with Quantum - Codemotion A...Codemotion
Quantum computers can use all of the possible pathways generated by quantum decisions to solve problems that will forever remain intractable to classical compute power. As the mega players vie for quantum supremacy and Rigetti announces its $1M "quantum advantage" prize, we live in exciting times. IBM-Q and Microsoft Q# are two ways you can learn to program quantum computers so that you're ready when the quantum revolution comes. I'll demonstrate some quantum solutions to problems that will forever be out of reach of classical, including organic chemistry and large number factorisation.
বাংলাদেশের অর্থনৈতিক সমীক্ষা ২০২৪ [Bangladesh Economic Review 2024 Bangla.pdf] কম্পিউটার , ট্যাব ও স্মার্ট ফোন ভার্সন সহ সম্পূর্ণ বাংলা ই-বুক বা pdf বই " সুচিপত্র ...বুকমার্ক মেনু 🔖 ও হাইপার লিংক মেনু 📝👆 যুক্ত ..
আমাদের সবার জন্য খুব খুব গুরুত্বপূর্ণ একটি বই ..বিসিএস, ব্যাংক, ইউনিভার্সিটি ভর্তি ও যে কোন প্রতিযোগিতা মূলক পরীক্ষার জন্য এর খুব ইম্পরট্যান্ট একটি বিষয় ...তাছাড়া বাংলাদেশের সাম্প্রতিক যে কোন ডাটা বা তথ্য এই বইতে পাবেন ...
তাই একজন নাগরিক হিসাবে এই তথ্য গুলো আপনার জানা প্রয়োজন ...।
বিসিএস ও ব্যাংক এর লিখিত পরীক্ষা ...+এছাড়া মাধ্যমিক ও উচ্চমাধ্যমিকের স্টুডেন্টদের জন্য অনেক কাজে আসবে ...
This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
A review of the growth of the Israel Genealogy Research Association Database Collection for the last 12 months. Our collection is now passed the 3 million mark and still growing. See which archives have contributed the most. See the different types of records we have, and which years have had records added. You can also see what we have for the future.
Walmart Business+ and Spark Good for Nonprofits.pdfTechSoup
"Learn about all the ways Walmart supports nonprofit organizations.
You will hear from Liz Willett, the Head of Nonprofits, and hear about what Walmart is doing to help nonprofits, including Walmart Business and Spark Good. Walmart Business+ is a new offer for nonprofits that offers discounts and also streamlines nonprofits order and expense tracking, saving time and money.
The webinar may also give some examples on how nonprofits can best leverage Walmart Business+.
The event will cover the following::
Walmart Business + (https://business.walmart.com/plus) is a new shopping experience for nonprofits, schools, and local business customers that connects an exclusive online shopping experience to stores. Benefits include free delivery and shipping, a 'Spend Analytics” feature, special discounts, deals and tax-exempt shopping.
Special TechSoup offer for a free 180 days membership, and up to $150 in discounts on eligible orders.
Spark Good (walmart.com/sparkgood) is a charitable platform that enables nonprofits to receive donations directly from customers and associates.
Answers about how you can do more with Walmart!"
Chapter wise All Notes of First year Basic Civil Engineering.pptxDenish Jangid
Chapter wise All Notes of First year Basic Civil Engineering
Syllabus
Chapter-1
Introduction to objective, scope and outcome the subject
Chapter 2
Introduction: Scope and Specialization of Civil Engineering, Role of civil Engineer in Society, Impact of infrastructural development on economy of country.
Chapter 3
Surveying: Object Principles & Types of Surveying; Site Plans, Plans & Maps; Scales & Unit of different Measurements.
Linear Measurements: Instruments used. Linear Measurement by Tape, Ranging out Survey Lines and overcoming Obstructions; Measurements on sloping ground; Tape corrections, conventional symbols. Angular Measurements: Instruments used; Introduction to Compass Surveying, Bearings and Longitude & Latitude of a Line, Introduction to total station.
Levelling: Instrument used Object of levelling, Methods of levelling in brief, and Contour maps.
Chapter 4
Buildings: Selection of site for Buildings, Layout of Building Plan, Types of buildings, Plinth area, carpet area, floor space index, Introduction to building byelaws, concept of sun light & ventilation. Components of Buildings & their functions, Basic concept of R.C.C., Introduction to types of foundation
Chapter 5
Transportation: Introduction to Transportation Engineering; Traffic and Road Safety: Types and Characteristics of Various Modes of Transportation; Various Road Traffic Signs, Causes of Accidents and Road Safety Measures.
Chapter 6
Environmental Engineering: Environmental Pollution, Environmental Acts and Regulations, Functional Concepts of Ecology, Basics of Species, Biodiversity, Ecosystem, Hydrological Cycle; Chemical Cycles: Carbon, Nitrogen & Phosphorus; Energy Flow in Ecosystems.
Water Pollution: Water Quality standards, Introduction to Treatment & Disposal of Waste Water. Reuse and Saving of Water, Rain Water Harvesting. Solid Waste Management: Classification of Solid Waste, Collection, Transportation and Disposal of Solid. Recycling of Solid Waste: Energy Recovery, Sanitary Landfill, On-Site Sanitation. Air & Noise Pollution: Primary and Secondary air pollutants, Harmful effects of Air Pollution, Control of Air Pollution. . Noise Pollution Harmful Effects of noise pollution, control of noise pollution, Global warming & Climate Change, Ozone depletion, Greenhouse effect
Text Books:
1. Palancharmy, Basic Civil Engineering, McGraw Hill publishers.
2. Satheesh Gopi, Basic Civil Engineering, Pearson Publishers.
3. Ketki Rangwala Dalal, Essentials of Civil Engineering, Charotar Publishing House.
4. BCP, Surveying volume 1
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...PECB
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
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Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
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How to Build a Module in Odoo 17 Using the Scaffold MethodCeline George
Odoo provides an option for creating a module by using a single line command. By using this command the user can make a whole structure of a module. It is very easy for a beginner to make a module. There is no need to make each file manually. This slide will show how to create a module using the scaffold method.
2. Topic
Topic today
− Minimum Spanning Tree
• Cut Property
• Prim Algorithm
• Kruskal Algorithm
POSCAT Seminar 1-2
16 July 2014
yougatup
3. Spanning Tree
Definition
A tree which “spans” whole vertices
i.e. a tree with n vertices
POSCAT Seminar 1-3
16 July 2014
yougatup
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1 2
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4. Spanning Tree
Definition
A tree which “spans” whole vertices
i.e. a tree with n vertices
POSCAT Seminar 1-4
16 July 2014
yougatup
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5. Minimum Spanning Tree
Problem
Find a spanning tree with minimum cost
POSCAT Seminar 1-5
16 July 2014
yougatup
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1 2
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6. Minimum Spanning Tree
Problem
Find a spanning tree with minimum cost
POSCAT Seminar 1-6
16 July 2014
yougatup
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1 2
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Is it MST ?
7. Minimum Spanning Tree
Problem
Find a spanning tree with minimum cost
POSCAT Seminar 1-7
16 July 2014
yougatup
1 2
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1 1
2 3
1 2
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Is it MST ?
8. Cut Property
Theorem
Suppose edge X are part of a MST of 𝐺 = (𝑉, 𝐸). Pick any subset of
nodes S for which X does not cross between S and 𝑉S, and let 𝑒
be the lightest edge across this partition. Then 𝑋 ∪ {𝑒} is part of
some MST
POSCAT Seminar 1-8
16 July 2014
yougatup
Proof ?
9. Cut Property
Theorem
Suppose X is a set of edges which are part of a MST of 𝐺 = (𝑉, 𝐸).
Pick any subset of nodes S for which X does not cross between S
and 𝑉S, and let 𝑒 be the lightest edge across this partition. Then
𝑋 ∪ {𝑒} is part of some MST
POSCAT Seminar 1-9
16 July 2014
yougatup
Assume 𝑒 ∉ 𝑇. Then we can construct
a different MST 𝑇′ containing 𝑋 ∪ 𝑒
by altering T slightly.
Compare the cost(𝑇′) and cost (𝑇)
10. Cut Property
Theorem
Suppose X is a set of edges which are part of a MST of 𝐺 = (𝑉, 𝐸).
Pick any subset of nodes S for which X does not cross between S
and 𝑉S, and let 𝑒 be the lightest edge across this partition. Then
𝑋 ∪ {𝑒} is part of some MST
POSCAT Seminar 1-10
16 July 2014
yougatup
by cut property, we can derive
beautiful greedy algorithm !
11. Kruskal Algorithm
Approach
For each iteration, choose minimum edge which doesn’t make
a cycle. Then it will make a MST by cut property.
POSCAT Seminar 1-11
16 July 2014
yougatup
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1 1
2 3
1 2
9
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12. Kruskal Algorithm
Approach
For each iteration, choose minimum edge which doesn’t make
a cycle. Then it will make a MST by cut property.
POSCAT Seminar 1-12
16 July 2014
yougatup
1 2
3
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5
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8
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1 1
2 3
1 2
9
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13. Kruskal Algorithm
Approach
For each iteration, choose minimum edge which doesn’t make
a cycle. Then it will make a MST by cut property.
POSCAT Seminar 1-13
16 July 2014
yougatup
1 2
3
4
6
5
7
8
3
1 1
2 3
1 2
9
3
14. Kruskal Algorithm
Approach
For each iteration, choose minimum edge which doesn’t make
a cycle. Then it will make a MST by cut property.
POSCAT Seminar 1-14
16 July 2014
yougatup
1 2
3
4
6
5
7
8
3
1 1
2 3
1 2
9
3
15. Kruskal Algorithm
Approach
For each iteration, choose minimum edge which doesn’t make
a cycle. Then it will make a MST by cut property.
POSCAT Seminar 1-15
16 July 2014
yougatup
1 2
3
4
6
5
7
8
3
1 1
2 3
1 2
9
3
16. Kruskal Algorithm
Approach
For each iteration, choose minimum edge which doesn’t make
a cycle. Then it will make a MST by cut property.
POSCAT Seminar 1-16
16 July 2014
yougatup
1 2
3
4
6
5
7
8
3
1 1
2 3
1 2
9
3
17. Kruskal Algorithm
Approach
For each iteration, choose minimum edge which doesn’t make
a cycle. Then it will make a MST by cut property.
POSCAT Seminar 1-17
16 July 2014
yougatup
1 2
3
4
6
5
7
8
3
1 1
2 3
1 2
9
3
?
18. Kruskal Algorithm
Approach
For each iteration, choose minimum edge which doesn’t make
a cycle. Then it will make a MST by cut property.
POSCAT Seminar 1-18
16 July 2014
yougatup
1 2
3
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1 1
2 3
1 2
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?
It make a cycle !
∴ we can’t select it
19. Kruskal Algorithm
Approach
For each iteration, choose minimum edge which doesn’t make
a cycle. Then it will make a MST by cut property.
POSCAT Seminar 1-19
16 July 2014
yougatup
1 2
3
4
6
5
7
8
3
1 1
2 3
1 2
9
3
20. Kruskal Algorithm
Approach
For each iteration, choose minimum edge which doesn’t make
a cycle. Then it will make a MST by cut property.
POSCAT Seminar 1-20
16 July 2014
yougatup
1 2
3
4
6
5
7
8
3
1 1
2 3
1 2
9
3
21. Kruskal Algorithm
Approach
For each iteration, choose minimum edge which doesn’t make
a cycle. Then it will make a MST by cut property.
POSCAT Seminar 1-21
16 July 2014
yougatup
1 2
3
4
6
5
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8
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1 1
2 3
1 2
9
3
22. Kruskal Algorithm
Approach
For each iteration, choose minimum edge which doesn’t make
a cycle. Then it will make a MST by cut property.
POSCAT Seminar 1-22
16 July 2014
yougatup
1 2
3
4
6
5
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8
3
1 1
2 3
1 2
9
3
23. Kruskal Algorithm
Approach
For each iteration, choose minimum edge which doesn’t make
a cycle. Then it will make a MST by cut property.
POSCAT Seminar 1-23
16 July 2014
yougatup
1 2
3
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5
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1 1
2 3
1 2
9
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Done ! we have no remaining edges
24. Kruskal Algorithm
Approach
For each iteration, choose minimum edge which doesn’t make
a cycle. Then it will make a MST by cut property.
POSCAT Seminar 1-24
16 July 2014
yougatup
1 2
3
4
6
5
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8
3
1 1
2 3
1 2
9
3
Done ! we have no remaining edges
25. Kruskal Algorithm
Question
How can we determine whether adding a edge makes a cycle or
not ?
POSCAT Seminar 1-25
16 July 2014
yougatup
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3
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1 1
2 3
1 2
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26. Kruskal Algorithm
Question
How can we determine whether adding a edge makes a cycle or
not ? by using Disjoint Set !
POSCAT Seminar 1-26
16 July 2014
yougatup
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1 2
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27. Kruskal Algorithm
Question
How can we determine whether adding a edge makes a cycle or
not ? by using Disjoint Set !
If a edge connects two vertices with different group, it will never
make a cycle.
POSCAT Seminar 1-27
16 July 2014
yougatup
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1 1
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28. Kruskal Algorithm
Question
How can we determine whether adding a edge makes a cycle or
not ? by using Disjoint Set !
If not, it will make a cycle !
POSCAT Seminar 1-28
16 July 2014
yougatup
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3
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1 1
2 3
1 2
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29. Kruskal Algorithm
Question
How can we determine whether adding a edge makes a cycle or
not ? by using Disjoint Set !
POSCAT Seminar 1-29
16 July 2014
yougatup
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3
4
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1 1
2 3
1 2
9
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1 2 3 4
5 6 7 8
30. Kruskal Algorithm
Question
How can we determine whether adding a edge makes a cycle or
not ? by using Disjoint Set !
POSCAT Seminar 1-30
16 July 2014
yougatup
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3
4
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5
7
8
3
1 1
2 3
1 2
9
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1 2 3 4
5 6 7 8
31. Kruskal Algorithm
Question
How can we determine whether adding a edge makes a cycle or
not ? by using Disjoint Set !
POSCAT Seminar 1-31
16 July 2014
yougatup
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3
4
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1 1
2 3
1 2
9
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1 23 4
5 6 7 8
32. Kruskal Algorithm
Question
How can we determine whether adding a edge makes a cycle or
not ? by using Disjoint Set !
POSCAT Seminar 1-32
16 July 2014
yougatup
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3
4
6
5
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1 1
2 3
1 2
9
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1 23 4
5 6 7 8
33. Kruskal Algorithm
Question
How can we determine whether adding a edge makes a cycle or
not ? by using Disjoint Set !
POSCAT Seminar 1-33
16 July 2014
yougatup
1 2
3
4
6
5
7
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3
1 1
2 3
1 2
9
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1
2
3 4
5 6 7 8
34. Kruskal Algorithm
Question
How can we determine whether adding a edge makes a cycle or
not ? by using Disjoint Set !
POSCAT Seminar 1-34
16 July 2014
yougatup
1 2
3
4
6
5
7
8
3
1 1
2 3
1 2
9
3
1
2
3 4
5 6 7 8
35. Kruskal Algorithm
Question
How can we determine whether adding a edge makes a cycle or
not ? by using Disjoint Set !
POSCAT Seminar 1-35
16 July 2014
yougatup
1 2
3
4
6
5
7
8
3
1 1
2 3
1 2
9
3
1
2
3 4
5
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7 8
36. Kruskal Algorithm
Question
How can we determine whether adding a edge makes a cycle or
not ? by using Disjoint Set !
POSCAT Seminar 1-36
16 July 2014
yougatup
1 2
3
4
6
5
7
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1 1
2 3
1 2
9
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1
2
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37. Kruskal Algorithm
Question
How can we determine whether adding a edge makes a cycle or
not ? by using Disjoint Set !
POSCAT Seminar 1-37
16 July 2014
yougatup
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3
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5
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1 1
2 3
1 2
9
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1
2
3
4
5
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38. Kruskal Algorithm
Question
How can we determine whether adding a edge makes a cycle or
not ? by using Disjoint Set !
POSCAT Seminar 1-38
16 July 2014
yougatup
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5
7
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1 1
2 3
1 2
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1
2
3
4
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39. Kruskal Algorithm
Question
How can we determine whether adding a edge makes a cycle or
not ? by using Disjoint Set !
POSCAT Seminar 1-39
16 July 2014
yougatup
1 2
3
4
6
5
7
8
3
1 1
2 3
1 2
9
3
1
2
3
4
5
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7 8
40. Kruskal Algorithm
Question
How can we determine whether adding a edge makes a cycle or
not ? by using Disjoint Set !
POSCAT Seminar 1-40
16 July 2014
yougatup
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3
4
6
5
7
8
3
1 1
2 3
1 2
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1
2
3
4
5
6
7 8
No !!
41. Kruskal Algorithm
Question
How can we determine whether adding a edge makes a cycle or
not ? by using Disjoint Set !
POSCAT Seminar 1-41
16 July 2014
yougatup
1 2
3
4
6
5
7
8
3
1 1
2 3
1 2
9
3
1
2
3
4
5
6
7 8
42. Kruskal Algorithm
Question
How can we determine whether adding a edge makes a cycle or
not ? by using Disjoint Set !
POSCAT Seminar 1-42
16 July 2014
yougatup
1 2
3
4
6
5
7
8
3
1 1
2 3
1 2
9
3
1
2
3
4 5
6 7
8
43. Kruskal Algorithm
Question
How can we determine whether adding a edge makes a cycle or
not ? by using Disjoint Set !
POSCAT Seminar 1-43
16 July 2014
yougatup
1 2
3
4
6
5
7
8
3
1 1
2 3
1 2
9
3
1
2
3
4 5
6 7
8
44. Kruskal Algorithm
Question
How can we determine whether adding a edge makes a cycle or
not ? by using Disjoint Set !
POSCAT Seminar 1-44
16 July 2014
yougatup
1 2
3
4
6
5
7
8
3
1 1
2 3
1 2
9
3
1
2
3
4 5
6 7
8
45. Kruskal Algorithm
Question
How can we determine whether adding a edge makes a cycle or
not ? by using Disjoint Set !
POSCAT Seminar 1-45
16 July 2014
yougatup
1 2
3
4
6
5
7
8
3
1 1
2 3
1 2
9
3
1
2
3
4 5
6 7
8
46. Kruskal Algorithm
Question
How can we determine whether adding a edge makes a cycle or
not ? by using Disjoint Set !
POSCAT Seminar 1-46
16 July 2014
yougatup
1 2
3
4
6
5
7
8
3
1 1
2 3
1 2
9
3
1
2
3
4 5
6 7
8
47. Kruskal Algorithm
Implementation
− We have to sort the set of edges via their costs with STL
− Union & Find
Quite simple !
POSCAT Seminar 1-47
16 July 2014
yougatup
48. Kruskal Algorithm
Implementation
− We have to sort the set of edges via their costs with STL
− Union & Find
Quite simple !
Analysis ?
POSCAT Seminar 1-48
16 July 2014
yougatup
49. Kruskal Algorithm
Implementation
− We have to sort the set of edges via their costs with STL
− Union & Find
Quite simple !
Analysis ? Sorting takes O(𝐸 log 𝐸)
Each iteration takes almost O(1)
because we use Union & Find
POSCAT Seminar 1-49
16 July 2014
yougatup
50. Kruskal Algorithm
Implementation
− We have to sort the set of edges via their costs with STL
− Union & Find
Quite simple !
Analysis ? Sorting takes O(𝐸 log 𝐸)
Each iteration takes almost O(1)
because we use Union & Find O(𝑬 𝐥𝐨𝐠 𝑬 )
POSCAT Seminar 1-50
16 July 2014
yougatup