this is an presentation about Big O Notation. Big O notaion is very useful to check the limitation and effeciecy of an algorithm in its worst cases.in these slides the examples about O(1),O(n),O(n^2) and O(n!) with some example algorithms in C++.
PPT on Analysis Of Algorithms.
The ppt includes Algorithms,notations,analysis,analysis of algorithms,theta notation, big oh notation, omega notation, notation graphs
a. Concept and Definition✓
b. Inserting and Deleting nodes ✓
c. Linked implementation of a stack (PUSH/POP) ✓
d. Linked implementation of a queue (Insert/Remove) ✓
e. Circular List
• Stack as a circular list (PUSH/POP) ✓
• Queue as a circular list (Insert/Remove) ✓
f. Doubly Linked List (Insert/Remove) ✓
For more course related material:
https://github.com/ashim888/dataStructureAndAlgorithm/
Personal blog
www.ashimlamichhane.com.np
linear search and binary search, Class lecture of Data Structure and Algorithms and Python.
Stack, Queue, Tree, Python, Python Code, Computer Science, Data, Data Analysis, Machine Learning, Artificial Intellegence, Deep Learning, Programming, Information Technology, Psuedocide, Tree, pseudocode, Binary Tree, Binary Search Tree, implementation, Binary search, linear search, Binary search operation, real-life example of binary search, linear search operation, real-life example of linear search, example bubble sort, sorting, insertion sort example, stack implementation, queue implementation, binary tree implementation, priority queue, binary heap, binary heap implementation, object-oriented programming, def, in BST, Binary search tree, Red-Black tree, Splay Tree, Problem-solving using Binary tree, problem-solving using BST, inorder, preorder, postorder
1. Introduction to time and space complexity.
2. Different types of asymptotic notations and their limit definitions.
3. Growth of functions and types of time complexities.
4. Space and time complexity analysis of various algorithms.
The little Oh (o) notation is a method of expressing the an upper bound on the growth rate of an algorithm’s
running time which may or may not be asymptotically tight therefore little oh(o) is also called a loose upper
bound we use little oh (o) notations to denote upper bound that is asymptotically not tight.
This presentation contains information about the divide and conquer algorithm. It includes discussion regarding its part, technique, skill, advantages and implementation issues.
Dynamic Programming :
Dynamic programming is a technique for solving problems by breaking them down into smaller subproblems, solving each subproblem once, and storing the solution to each subproblem so that it can be reused in the future. Some characteristics of dynamic programming include:
Optimal substructure: Dynamic programming problems typically have an optimal substructure, meaning that the optimal solution to the problem can be obtained by solving the subproblems optimally and combining their solutions.
Overlapping subproblems: Dynamic programming problems often involve overlapping subproblems, meaning that the same subproblems are solved multiple times. To avoid solving the same subproblem multiple times, dynamic programming algorithms store the solutions to the subproblems in a table or array, so that they can be reused later.
Bottom-up approach: Dynamic programming algorithms usually solve problems using a bottom-up approach, meaning that they start by solving the smallest subproblems and work their way up to the larger ones.
Efficiency: Dynamic programming algorithms can be very efficient, especially when the subproblems overlap significantly. By storing the solutions to the subproblems and reusing them, dynamic programming algorithms can avoid redundant computations and achieve good time and space complexity.
Applicability: Dynamic programming is applicable to a wide range of problems, including optimization problems, decision problems, and problems that involve sequential decisions. It is often used to solve problems in computer science, operations research, and economics.
Algorithm Design Techniques
Iterative techniques, Divide and Conquer, Dynamic Programming, Greedy Algorithms.
PPT on Analysis Of Algorithms.
The ppt includes Algorithms,notations,analysis,analysis of algorithms,theta notation, big oh notation, omega notation, notation graphs
a. Concept and Definition✓
b. Inserting and Deleting nodes ✓
c. Linked implementation of a stack (PUSH/POP) ✓
d. Linked implementation of a queue (Insert/Remove) ✓
e. Circular List
• Stack as a circular list (PUSH/POP) ✓
• Queue as a circular list (Insert/Remove) ✓
f. Doubly Linked List (Insert/Remove) ✓
For more course related material:
https://github.com/ashim888/dataStructureAndAlgorithm/
Personal blog
www.ashimlamichhane.com.np
linear search and binary search, Class lecture of Data Structure and Algorithms and Python.
Stack, Queue, Tree, Python, Python Code, Computer Science, Data, Data Analysis, Machine Learning, Artificial Intellegence, Deep Learning, Programming, Information Technology, Psuedocide, Tree, pseudocode, Binary Tree, Binary Search Tree, implementation, Binary search, linear search, Binary search operation, real-life example of binary search, linear search operation, real-life example of linear search, example bubble sort, sorting, insertion sort example, stack implementation, queue implementation, binary tree implementation, priority queue, binary heap, binary heap implementation, object-oriented programming, def, in BST, Binary search tree, Red-Black tree, Splay Tree, Problem-solving using Binary tree, problem-solving using BST, inorder, preorder, postorder
1. Introduction to time and space complexity.
2. Different types of asymptotic notations and their limit definitions.
3. Growth of functions and types of time complexities.
4. Space and time complexity analysis of various algorithms.
The little Oh (o) notation is a method of expressing the an upper bound on the growth rate of an algorithm’s
running time which may or may not be asymptotically tight therefore little oh(o) is also called a loose upper
bound we use little oh (o) notations to denote upper bound that is asymptotically not tight.
This presentation contains information about the divide and conquer algorithm. It includes discussion regarding its part, technique, skill, advantages and implementation issues.
Dynamic Programming :
Dynamic programming is a technique for solving problems by breaking them down into smaller subproblems, solving each subproblem once, and storing the solution to each subproblem so that it can be reused in the future. Some characteristics of dynamic programming include:
Optimal substructure: Dynamic programming problems typically have an optimal substructure, meaning that the optimal solution to the problem can be obtained by solving the subproblems optimally and combining their solutions.
Overlapping subproblems: Dynamic programming problems often involve overlapping subproblems, meaning that the same subproblems are solved multiple times. To avoid solving the same subproblem multiple times, dynamic programming algorithms store the solutions to the subproblems in a table or array, so that they can be reused later.
Bottom-up approach: Dynamic programming algorithms usually solve problems using a bottom-up approach, meaning that they start by solving the smallest subproblems and work their way up to the larger ones.
Efficiency: Dynamic programming algorithms can be very efficient, especially when the subproblems overlap significantly. By storing the solutions to the subproblems and reusing them, dynamic programming algorithms can avoid redundant computations and achieve good time and space complexity.
Applicability: Dynamic programming is applicable to a wide range of problems, including optimization problems, decision problems, and problems that involve sequential decisions. It is often used to solve problems in computer science, operations research, and economics.
Algorithm Design Techniques
Iterative techniques, Divide and Conquer, Dynamic Programming, Greedy Algorithms.
Big O notation is used in Computer Science to describe the performance or complexity of an algorithm. Big O specifically describes the worst-case scenario, and can be used to describe the execution time required or the space used (e.g. in memory or on disk) by an algorithm.
For further information
https://github.com/ashim888/dataStructureAndAlgorithm
References:
https://www.khanacademy.org/computing/computer-science/algorithms/asymptotic-notation/a/asymptotic-notation
http://web.mit.edu/16.070/www/lecture/big_o.pdf
https://rob-bell.net/2009/06/a-beginners-guide-to-big-o-notation/
https://justin.abrah.ms/computer-science/big-o-notation-explained.html
TIME EXECUTION OF DIFFERENT SORTED ALGORITHMSTanya Makkar
what is Algorithm and classification and its complexity
Time Complexity
Time Space trade-off
Asymptotic time complexity of algorithm and its notation
Why do we need to classify running time of algorithm into growth rates?
Big O-h notation and example
Big omega notation and example
Big theta notation and its example
best among the 3 notation
finding complexity f(n) for certain cases
1. Average case
2.Best case
3.Worst case
Searching
Sorting
complexity of Sorting
Conclusion
Need for Speed: Removing speed bumps from your Symfony projects ⚡️Łukasz Chruściel
No one wants their application to drag like a car stuck in the slow lane! Yet it’s all too common to encounter bumpy, pothole-filled solutions that slow the speed of any application. Symfony apps are not an exception.
In this talk, I will take you for a spin around the performance racetrack. We’ll explore common pitfalls - those hidden potholes on your application that can cause unexpected slowdowns. Learn how to spot these performance bumps early, and more importantly, how to navigate around them to keep your application running at top speed.
We will focus in particular on tuning your engine at the application level, making the right adjustments to ensure that your system responds like a well-oiled, high-performance race car.
Atelier - Innover avec l’IA Générative et les graphes de connaissancesNeo4j
Atelier - Innover avec l’IA Générative et les graphes de connaissances
Allez au-delà du battage médiatique autour de l’IA et découvrez des techniques pratiques pour utiliser l’IA de manière responsable à travers les données de votre organisation. Explorez comment utiliser les graphes de connaissances pour augmenter la précision, la transparence et la capacité d’explication dans les systèmes d’IA générative. Vous partirez avec une expérience pratique combinant les relations entre les données et les LLM pour apporter du contexte spécifique à votre domaine et améliorer votre raisonnement.
Amenez votre ordinateur portable et nous vous guiderons sur la mise en place de votre propre pile d’IA générative, en vous fournissant des exemples pratiques et codés pour démarrer en quelques minutes.
Custom Healthcare Software for Managing Chronic Conditions and Remote Patient...Mind IT Systems
Healthcare providers often struggle with the complexities of chronic conditions and remote patient monitoring, as each patient requires personalized care and ongoing monitoring. Off-the-shelf solutions may not meet these diverse needs, leading to inefficiencies and gaps in care. It’s here, custom healthcare software offers a tailored solution, ensuring improved care and effectiveness.
A Study of Variable-Role-based Feature Enrichment in Neural Models of CodeAftab Hussain
Understanding variable roles in code has been found to be helpful by students
in learning programming -- could variable roles help deep neural models in
performing coding tasks? We do an exploratory study.
- These are slides of the talk given at InteNSE'23: The 1st International Workshop on Interpretability and Robustness in Neural Software Engineering, co-located with the 45th International Conference on Software Engineering, ICSE 2023, Melbourne Australia
Utilocate offers a comprehensive solution for locate ticket management by automating and streamlining the entire process. By integrating with Geospatial Information Systems (GIS), it provides accurate mapping and visualization of utility locations, enhancing decision-making and reducing the risk of errors. The system's advanced data analytics tools help identify trends, predict potential issues, and optimize resource allocation, making the locate ticket management process smarter and more efficient. Additionally, automated ticket management ensures consistency and reduces human error, while real-time notifications keep all relevant personnel informed and ready to respond promptly.
The system's ability to streamline workflows and automate ticket routing significantly reduces the time taken to process each ticket, making the process faster and more efficient. Mobile access allows field technicians to update ticket information on the go, ensuring that the latest information is always available and accelerating the locate process. Overall, Utilocate not only enhances the efficiency and accuracy of locate ticket management but also improves safety by minimizing the risk of utility damage through precise and timely locates.
Quarkus Hidden and Forbidden ExtensionsMax Andersen
Quarkus has a vast extension ecosystem and is known for its subsonic and subatomic feature set. Some of these features are not as well known, and some extensions are less talked about, but that does not make them less interesting - quite the opposite.
Come join this talk to see some tips and tricks for using Quarkus and some of the lesser known features, extensions and development techniques.
Top Features to Include in Your Winzo Clone App for Business Growth (4).pptxrickgrimesss22
Discover the essential features to incorporate in your Winzo clone app to boost business growth, enhance user engagement, and drive revenue. Learn how to create a compelling gaming experience that stands out in the competitive market.
AI Fusion Buddy Review: Brand New, Groundbreaking Gemini-Powered AI AppGoogle
AI Fusion Buddy Review: Brand New, Groundbreaking Gemini-Powered AI App
👉👉 Click Here To Get More Info 👇👇
https://sumonreview.com/ai-fusion-buddy-review
AI Fusion Buddy Review: Key Features
✅Create Stunning AI App Suite Fully Powered By Google's Latest AI technology, Gemini
✅Use Gemini to Build high-converting Converting Sales Video Scripts, ad copies, Trending Articles, blogs, etc.100% unique!
✅Create Ultra-HD graphics with a single keyword or phrase that commands 10x eyeballs!
✅Fully automated AI articles bulk generation!
✅Auto-post or schedule stunning AI content across all your accounts at once—WordPress, Facebook, LinkedIn, Blogger, and more.
✅With one keyword or URL, generate complete websites, landing pages, and more…
✅Automatically create & sell AI content, graphics, websites, landing pages, & all that gets you paid non-stop 24*7.
✅Pre-built High-Converting 100+ website Templates and 2000+ graphic templates logos, banners, and thumbnail images in Trending Niches.
✅Say goodbye to wasting time logging into multiple Chat GPT & AI Apps once & for all!
✅Save over $5000 per year and kick out dependency on third parties completely!
✅Brand New App: Not available anywhere else!
✅ Beginner-friendly!
✅ZERO upfront cost or any extra expenses
✅Risk-Free: 30-Day Money-Back Guarantee!
✅Commercial License included!
See My Other Reviews Article:
(1) AI Genie Review: https://sumonreview.com/ai-genie-review
(2) SocioWave Review: https://sumonreview.com/sociowave-review
(3) AI Partner & Profit Review: https://sumonreview.com/ai-partner-profit-review
(4) AI Ebook Suite Review: https://sumonreview.com/ai-ebook-suite-review
#AIFusionBuddyReview,
#AIFusionBuddyFeatures,
#AIFusionBuddyPricing,
#AIFusionBuddyProsandCons,
#AIFusionBuddyTutorial,
#AIFusionBuddyUserExperience
#AIFusionBuddyforBeginners,
#AIFusionBuddyBenefits,
#AIFusionBuddyComparison,
#AIFusionBuddyInstallation,
#AIFusionBuddyRefundPolicy,
#AIFusionBuddyDemo,
#AIFusionBuddyMaintenanceFees,
#AIFusionBuddyNewbieFriendly,
#WhatIsAIFusionBuddy?,
#HowDoesAIFusionBuddyWorks
Navigating the Metaverse: A Journey into Virtual Evolution"Donna Lenk
Join us for an exploration of the Metaverse's evolution, where innovation meets imagination. Discover new dimensions of virtual events, engage with thought-provoking discussions, and witness the transformative power of digital realms."
AI Genie Review: World’s First Open AI WordPress Website CreatorGoogle
AI Genie Review: World’s First Open AI WordPress Website Creator
👉👉 Click Here To Get More Info 👇👇
https://sumonreview.com/ai-genie-review
AI Genie Review: Key Features
✅Creates Limitless Real-Time Unique Content, auto-publishing Posts, Pages & Images directly from Chat GPT & Open AI on WordPress in any Niche
✅First & Only Google Bard Approved Software That Publishes 100% Original, SEO Friendly Content using Open AI
✅Publish Automated Posts and Pages using AI Genie directly on Your website
✅50 DFY Websites Included Without Adding Any Images, Content Or Doing Anything Yourself
✅Integrated Chat GPT Bot gives Instant Answers on Your Website to Visitors
✅Just Enter the title, and your Content for Pages and Posts will be ready on your website
✅Automatically insert visually appealing images into posts based on keywords and titles.
✅Choose the temperature of the content and control its randomness.
✅Control the length of the content to be generated.
✅Never Worry About Paying Huge Money Monthly To Top Content Creation Platforms
✅100% Easy-to-Use, Newbie-Friendly Technology
✅30-Days Money-Back Guarantee
See My Other Reviews Article:
(1) TubeTrivia AI Review: https://sumonreview.com/tubetrivia-ai-review
(2) SocioWave Review: https://sumonreview.com/sociowave-review
(3) AI Partner & Profit Review: https://sumonreview.com/ai-partner-profit-review
(4) AI Ebook Suite Review: https://sumonreview.com/ai-ebook-suite-review
#AIGenieApp #AIGenieBonus #AIGenieBonuses #AIGenieDemo #AIGenieDownload #AIGenieLegit #AIGenieLiveDemo #AIGenieOTO #AIGeniePreview #AIGenieReview #AIGenieReviewandBonus #AIGenieScamorLegit #AIGenieSoftware #AIGenieUpgrades #AIGenieUpsells #HowDoesAlGenie #HowtoBuyAIGenie #HowtoMakeMoneywithAIGenie #MakeMoneyOnline #MakeMoneywithAIGenie
GraphSummit Paris - The art of the possible with Graph TechnologyNeo4j
Sudhir Hasbe, Chief Product Officer, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
Code reviews are vital for ensuring good code quality. They serve as one of our last lines of defense against bugs and subpar code reaching production.
Yet, they often turn into annoying tasks riddled with frustration, hostility, unclear feedback and lack of standards. How can we improve this crucial process?
In this session we will cover:
- The Art of Effective Code Reviews
- Streamlining the Review Process
- Elevating Reviews with Automated Tools
By the end of this presentation, you'll have the knowledge on how to organize and improve your code review proces
E-commerce Application Development Company.pdfHornet Dynamics
Your business can reach new heights with our assistance as we design solutions that are specifically appropriate for your goals and vision. Our eCommerce application solutions can digitally coordinate all retail operations processes to meet the demands of the marketplace while maintaining business continuity.
May Marketo Masterclass, London MUG May 22 2024.pdfAdele Miller
Can't make Adobe Summit in Vegas? No sweat because the EMEA Marketo Engage Champions are coming to London to share their Summit sessions, insights and more!
This is a MUG with a twist you don't want to miss.
Software Engineering, Software Consulting, Tech Lead, Spring Boot, Spring Cloud, Spring Core, Spring JDBC, Spring Transaction, Spring MVC, OpenShift Cloud Platform, Kafka, REST, SOAP, LLD & HLD.
Graspan: A Big Data System for Big Code AnalysisAftab Hussain
We built a disk-based parallel graph system, Graspan, that uses a novel edge-pair centric computation model to compute dynamic transitive closures on very large program graphs.
We implement context-sensitive pointer/alias and dataflow analyses on Graspan. An evaluation of these analyses on large codebases such as Linux shows that their Graspan implementations scale to millions of lines of code and are much simpler than their original implementations.
These analyses were used to augment the existing checkers; these augmented checkers found 132 new NULL pointer bugs and 1308 unnecessary NULL tests in Linux 4.4.0-rc5, PostgreSQL 8.3.9, and Apache httpd 2.2.18.
- Accepted in ASPLOS ‘17, Xi’an, China.
- Featured in the tutorial, Systemized Program Analyses: A Big Data Perspective on Static Analysis Scalability, ASPLOS ‘17.
- Invited for presentation at SoCal PLS ‘16.
- Invited for poster presentation at PLDI SRC ‘16.
1. Big O Notation
Hamza Mushtaq
DCS&IT
Session 2016-2020
Islamia University Bahwalpur
A Brief Overview
2. Time Complexity
In the time complexity analysis we define the time as
a function of the problem size and try to estimate the
growth of the execution time with the growth in
problem size.
3. Memory Space
• The space require by the program to save input data and results in memory
(RAM).
4. History of Big-O-Notation
• Big O notation (with a capital letter O, not a zero), also called Landau's
symbol, is a symbolism used in complexity theory, computer science,
and mathematics to describe the basic behavior of functions. Basically, it
tells you how fast a function grows or declines.
• Landau's symbol comes from the name of the German mathematician
Edmund Landau who invented the notation.
• The letter O is used because the rate of growth of a function is also
called its order.
6. It describes
• Efficiency of an Algorithm
• Time Factor of an Algorithm
• Space Complexity of an Algorithm
It is represented by O(n) where n is the number of operations.
7. O(1)
void print(int index,int values[]){
cout<<“the value at index”<<index<< “ is ”<<value[index];
}
You can directly access the index element of the
array, so no matter how big the array gets, the
time it takes to access the element won’t change.
8. O(n)
void print(int index, int values[]){
for(int x=0;x<index;x++)cout<<index<<“ .”<<value[index]<<endl;
}
As you increase the size of the array, the time for this
algorithm will increase proportionally. An array of 50 will
take twice the time as an array of 25 and half the time of
an array of 100.
9. O(n2)
• int CompareToAllNumbers (int array1[ ] ,int index1,int array2[ ],int index2){
• for (int x = 0; x < index1; x++)
• {bool isMin;
• for (int y = 0; y < index2; y++)
• { if( array[x] > array[y])
• isMin = false;
• }if(isMin)
• break; }
• return array[x];}
If you add one element to array, you need to go through the entire
rest of the array one additional time. As the input grows, the
time required to run the algorithm will grow quite quickly.
10. O(n!)
• Travelling salesman problem
in this problem as the number of cities increases
the runtime also increases accordingly by a factor
of cities factorial.
11.
12. int CompareSmallestNumber (int array[ ])
{ int x, curMin;
curMin = array[0];
for (x = 1; x < 10; x++)
{ if( array[x] < curMin)
curMin = array[x];}
return curMin;}
int CompareToAllNumbers (int array[ ])
{bool is Min;
int x, y;
for (int x = 0; x < 10; x++)
{ isMin = true;
for (int y = 0; y < 10; y++)
{ if( array[x] > array[y])
isMin = false;
}if(isMin)
break; }
return array[x];}
We are having two different algorithms
to find the smallest element in the array
13. • Uses array to store elements
• Compares a temporary variable in the array
• The Big O notation for it is O(n)
This algorithm is efficient and effective as its
O is small as compare to the 2nd algorithm
And hence it gives the best result in less time.
• Uses array to store elements
• Compares the array with array itself
• The big O notation for it is O(n2)
This algorithm is less effective than the other
Beacause this algo has a high Big O and hence
It is more time taking and less effective.
Worst Best
15. Best case:
When the algorithm takes less
run time w.r.t the given inputs/data.
Best case also defines as Big Ω.
Average case:
When the algorithm takes
average run time w.r.t to the input /data.
This is also known as BigΘ.
Worst case:
When the algorithm takes higher runtime
as compared to the given inputs/data.
Worst case is also called as Big O.