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
this is a briefer overview about the Big O Notation. Big O Notaion are useful to check the Effeciency of an algorithm and to check its limitation at higher value. with big o notation some examples are also shown about its cases and some functions in c++ are also described.
this is a briefer overview about the Big O Notation. Big O Notaion are useful to check the Effeciency of an algorithm and to check its limitation at higher value. with big o notation some examples are also shown about its cases and some functions in c++ are also described.
Ch-2 final exam documet compler design elementsMAHERMOHAMED27
The "Project Risk Management" course transformed me from a passive observer of risk to a proactive risk management champion. Here are some key learnings that will forever change my approach to projects:
The Proactive Mindset: I transitioned from simply reacting to problems to anticipating and mitigating them. The course emphasized the importance of proactive risk identification through techniques like brainstorming, SWOT analysis, and FMEA (Failure Mode and Effect Analysis). This allows for early intervention and prevents minor issues from snowballing into major roadblocks.
Risk Assessment and Prioritization: I learned to assess the likelihood and impact of each identified risk. The course introduced qualitative and quantitative risk analysis methods, allowing me to prioritize risks based on their potential severity. This empowers me to focus resources on the most critical threats to project success.
Developing Response Strategies: The course equipped me with a toolbox of risk response strategies. I learned about risk avoidance, mitigation, transference, and acceptance strategies, allowing me to choose the most appropriate approach for each risk. For example, I can now advocate for additional training to mitigate a knowledge gap risk or build buffer time into the schedule to address potential delays.
Communication and Monitoring: The course highlighted the importance of clear communication regarding risks. I learned to effectively communicate risks to stakeholders, ensuring everyone is aware of potential challenges and mitigation plans. Additionally, I gained valuable insights into risk monitoring and tracking, allowing for continuous evaluation and adaptation as the project progresses.
In essence, "Project Risk Management" equipped me with the knowledge and tools to navigate the inevitable uncertainties of projects. By embracing a proactive approach, I can now lead projects with greater confidence, increasing the chances of achieving successful outcomes.
Unit 1: Fundamentals of the Analysis of Algorithmic Efficiency, Units for Measuring Running Time, PROPERTIES OF AN ALGORITHM, Growth of Functions, Algorithm - Analysis, Asymptotic Notations, Recurrence Relation and problems
Stack
operations performed on stack
stack applications
Infix to postfix conversion
Infix to prefix conversion
Postfix to infix conversion
Prefix to infix conversion
algorithm to push an element in a stack
algorithm to pop an element from a stack
DEFINITION OF DATA STRUCTURES & ALGORITHM
OVERVIEW OF DATA STRUCTURES
TYPES OF DATA STRUCTURE
LINEAR DATA STRUCTURE
NON-LINEAR DATA STRUCTURE
ABSTRACT DATA TYPE.
Sorting
NEED FOR SORTING
Insertion Sort
Illustration of Insertion Sort
Insertion Sort algorithm
code for Insertion Sort
advantages & disadvantages of Insertion Sort
best case and worst case of Insertion Sort
Selection sort
Illustration of Selection sort
Selection sort algorithm
code for Selection sort
worst case for selection Sort
queue
operations performed on queue
queue applications
Example to enqueue
Algorithm To enqueue() / add () An Element (ITEM )In The Queue
Example to dequeue
Algorithm To dequeue() / remove() An Element (ITEM )In The Queue
PRIORITY Queue
PRIORITY Queue Representation
linked list
singly linked list
insertion in singly linked list
DELETION IN SINGLY LINKED LIST
Searching a singly linked list
Doubly Linked List
insertion from Doubly linked list
DELETION from Doubly LINKED LIST
Searching a doubly linked list
Circular linked list
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...
Complexity Analysis
1. Chapter 2Chapter 2
Complexity analysisComplexity analysis
01/29/18 BY MS. SHAISTA QADIR 1
PRESENTED BY
Shaista Qadir
Lecturer king khalid university
2. ContentsContents
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
01/29/18 BY MS. SHAISTA QADIR 2
3. algorithm and itsalgorithm and its
propertiesproperties
01/29/18 BY MS. SHAISTA QADIR 3
An algorithm is a step by step procedure for calculating a
function.
An algorithm can exist for a day to day task, a simple
program or a complex calculation.
To write an algorithm for a problem one should know
◦ The input to be supplied for the program.
◦ The operation to be performed on the input to perform the
task.
◦ The output to be obtained.
◦ Any assumptions or initializations to be made for solving
the problem.
4. algorithm and itsalgorithm and its
propertiesproperties
01/29/18 BY MS. SHAISTA QADIR 4
An algorithm for a problem has the following properties:
Finiteness: An algorithm should have a finite number of steps
and should terminate after these finite number of steps.
Definiteness: Every step in the algorithm should be very clear.
The steps shall be explained in only one way and executed
without any confusion.
Effectiveness: The instructions of the algorithm are realizable.
The steps in the algorithm shall be executed in finite amount of
time.
Input: An algorithm can accept no input or it can accept one to
many inputs.
Output: The algorithm should produce a minimum of one
output.
5. Computational ComplexityComputational Complexity
01/29/18 BY MS. SHAISTA QADIR 5
The same problem can frequently be solved with
algorithms that differ in efficiency.
To compare the efficiency of algorithms, a measure of
the degree of difficulty of an algorithm called
computational complexity was developed by Juris
Hartmanis and Richard E.Stearns.
Computational complexity indicates how much effort is
needed to apply an algorithm or how costly it is.
Computational Complexity is measured in terms of two
efficiencies to design good algorithms.
◦ Space efficiency
◦ Time efficiency
6. Computational ComplexityComputational Complexity
(( SpaCe effiCienCySpaCe effiCienCy ))
01/29/18 BY MS. SHAISTA QADIR 6
These efficiency measures helps us to understand which
algorithm is better than other.
Space Efficiency:
◦ Space Efficiency is on how efficiently the algorithm
consumes memory space.
◦ Space required will depend upon
program size (constant)
data size (constant and dynamic)
◦ These days machines come with large memory sizes to
solve this problem.
7. Computational ComplexityComputational Complexity
( SpaCe effiCienCy )( SpaCe effiCienCy )
01/29/18 BY MS. SHAISTA QADIR 7
Two space Components
◦ Fixed static part (Cp): space for numbers, constants,
size of input, output and program .
◦ Variable dynamic part (Sp): space for variables whose
size is problem dependent and dynamic.
Total space requirement for an algorithm is the sum of
fixed and dynamic part.
◦ S(P)= Cp+Sp
8. Computational ComplexityComputational Complexity
( illuStration )( illuStration )
01/29/18 BY MS. SHAISTA QADIR 8
Illustration: Find the total space requirement for the
following pieces of codes (neglect program space
requirement)
◦ i) code to find difference between two numbers.
int x,y,dif;
dif = x-y;
Here there are three static variables x, y, dif and no
dynamic variables. Space allotted for int variable is 4
bytes.
Therefore, S(P)= Cp+Sp
S(P)= 3x4+0 =12 bytes
9. Computational ComplexityComputational Complexity
( illuStration )( illuStration )
01/29/18 BY MS. SHAISTA QADIR 9
◦ ii) code to add numbers of array.
int add(int x[], int n)
{ int total = 0, i;
for(i=0; i<n; i++)
total = total + x[i];
return total; }
Here there are three static variables, int n, total, i and one
dynamic variable int x which depends upon the value n.
Space allotted for int variable is 4 bytes.
◦ Therefore, S(P)= Cp+Sp
◦ S(P)= (3x4)+(nx4)
◦ S(P) =12 + (nx4) bytes
10. Computational ComplexityComputational Complexity
( time effiCenCy )( time effiCenCy )
01/29/18 BY MS. SHAISTA QADIR 10
Time Efficiency
◦ is determined based on the amount of time required to run
a program.
◦ It depends upon the number of instructions to be executed
which in turn is dependent on the amount of data. It is
hardware independent.
Time Efficiency/ Time Complexity analysis is essential to know
◦ If the algorithm is “fast enough” for my needs
◦ How longer will the algorithm take if I increase the amount
of data it must process?
◦ Which is the right algorithm, among the given a set of
algorithms that accomplish the same thing.
11. Computational ComplexityComputational Complexity
( illustration )( illustration )
01/29/18 BY MS. SHAISTA QADIR 11
Consider the piece of code
for (int i=1; i<= n ; i++)
for int j=1 ; j <= n; j++)
{
cout << i; 1
p = p + i; 1 2n (2n)n
}
The 1st loop runs n times, 2nd loop runs n times, the
third and fourth statements run 1 unit each.
So the total run-time is equal to 2n2
12. Complexity analysis andComplexity analysis and
asymptotiC notationsasymptotiC notations
01/29/18 BY MS. SHAISTA QADIR 12
To analyze the computational complexity of algorithms
in terms of time, computer scientists used several
asymptotic notations and important notations among
them are:
Big-oh-notation (O)
Omega-notation (Ω)
Theta-notation (Θ)
Asymptotic Notations provides a formula that associates
n, the problem size, with t, the processing time required
to solve the problem.
13. Complexity analysis and asymptotiCComplexity analysis and asymptotiC
notationsnotations
(Worst Case analyses )(Worst Case analyses )
01/29/18 BY MS. SHAISTA QADIR 13
Big-oh-notation (O)
Big-O, “bounded above by”
Let T(n) be a measure of the time required to execute an
algorithm of problem size n.
then for some positive values of constants c and N, the
Big-Oh (O) of a function f(n) on n is given by
T(n)=O(f(n))
T(n)<=c(f(n)) where n>N
It gives the maximum value of T(n) or the WORST case
for any possible input.
14. Complexity analysis and asymptotiCComplexity analysis and asymptotiC
notationsnotations
( Best( Best Case analysis )Case analysis )
01/29/18 BY MS. SHAISTA QADIR 14
Omega-notation (Ω)
Omega, “bounded below by”
Let T(n) be a measure of the time required to execute an
algorithm of problem size n.
then for some positive values of constants c and N, the
Omega (Ω) of a function f(n) on n is given by
T(n)= Ω(f(n))
T(n)>=c(f(n)) where n>N
It gives the minimum value of T(n) or the BEST case for
any possible input.
15. Complexity analysis and asymptotiCComplexity analysis and asymptotiC
notationsnotations
( aVeraGe Case analysis )( aVeraGe Case analysis )
01/29/18 BY MS. SHAISTA QADIR 15
Theta-notation (Θ)
Theta, “bounded above and below”
Let T(n) be a measure of the time required to execute an
algorithm of problem size n.
then for some positive values of constants c1, c2 and N,
the Theta (Θ) of a function f(n) on n is given by
T(n)= Θ(f(n))
c (f(n)) <=T(n)<= c1(c2(f(n)) where n>N
It gives the expected value of T(n) or the AVERAGE case
for any possible input.
16. Complexity AnAlysis .Complexity AnAlysis .
01/29/18 BY MS. SHAISTA QADIR 16
Big O or the Order of n measurement or the Worst Case
is used mostly in the estimation of performance of a
piece of code against the amount of data.
For the piece of code,
for (int i=1; i<= n ; i++)
for int j=1 ; j <= n; j++)
{ cout << i;
p = p + i;
} T(n)=2n2
17. Complexity AnAlysisComplexity AnAlysis
( exAmple )( exAmple )
01/29/18 BY MS. SHAISTA QADIR 17
To find Big O from T(n)= 2n2
1. Discarding constant terms produces :
Here no constants 2n2
2. Clearing coefficients : n2
3. Picking the most significant term: n2
T(n)=O(n2 )
18. Complexity AnAlysisComplexity AnAlysis
( exAmple s)( exAmple s)
01/29/18 BY MS. SHAISTA QADIR 18
Example:1
Time =2n3
+2n+100
Step:1 Discarding constant terms produces: 2n3
+2n
Step: 2 Clearing coefficients : n3
+n
Step: 3 Picking the most significant term: n3
Time = O(n3
)
Example:2
Time =3n3
+150
Step:1 Discarding constant terms produces: 3n3
Step: 2 Clearing coefficients : n3
Step: 3 Picking the most significant term: n3
Time =O(n3
)