The document discusses various algorithms design approaches and patterns including divide and conquer, greedy algorithms, dynamic programming, backtracking, and branch and bound. It provides examples of each along with pseudocode. Specific algorithms discussed include binary search, merge sort, knapsack problem, shortest path problems, and the traveling salesman problem. The document is authored by Ashwin Shiv, a second year computer science student at NIT Delhi.
I.INFORMED SEARCH IN ARTIFICIAL INTELLIGENCE II. HEURISTIC FUNCTION IN AI III...vikas dhakane
Artificial Intelligence: Introduction, Typical Applications. State Space Search: Depth Bounded
DFS, Depth First Iterative Deepening. Heuristic Search: Heuristic Functions, Best First Search,
Hill Climbing, Variable Neighborhood Descent, Beam Search, Tabu Search. Optimal Search: A
*
algorithm, Iterative Deepening A*
, Recursive Best First Search, Pruning the CLOSED and OPEN
Lists
BackTracking Algorithm: Technique and ExamplesFahim Ferdous
This slides gives a strong overview of backtracking algorithm. How it came and general approaches of the techniques. Also some well-known problem and solution of backtracking algorithm.
I.INFORMED SEARCH IN ARTIFICIAL INTELLIGENCE II. HEURISTIC FUNCTION IN AI III...vikas dhakane
Artificial Intelligence: Introduction, Typical Applications. State Space Search: Depth Bounded
DFS, Depth First Iterative Deepening. Heuristic Search: Heuristic Functions, Best First Search,
Hill Climbing, Variable Neighborhood Descent, Beam Search, Tabu Search. Optimal Search: A
*
algorithm, Iterative Deepening A*
, Recursive Best First Search, Pruning the CLOSED and OPEN
Lists
BackTracking Algorithm: Technique and ExamplesFahim Ferdous
This slides gives a strong overview of backtracking algorithm. How it came and general approaches of the techniques. Also some well-known problem and solution of backtracking algorithm.
Artificial Intelligence: Introduction, Typical Applications. State Space Search: Depth Bounded
DFS, Depth First Iterative Deepening. Heuristic Search: Heuristic Functions, Best First Search,
Hill Climbing, Variable Neighborhood Descent, Beam Search, Tabu Search. Optimal Search: A
*
algorithm, Iterative Deepening A*
, Recursive Best First Search, Pruning the CLOSED and OPEN
Lists
Artificial Intelligence: Introduction, Typical Applications. State Space Search: Depth Bounded
DFS, Depth First Iterative Deepening. Heuristic Search: Heuristic Functions, Best First Search,
Hill Climbing, Variable Neighborhood Descent, Beam Search, Tabu Search. Optimal Search: A
*
algorithm, Iterative Deepening A*
, Recursive Best First Search, Pruning the CLOSED and OPEN
Lists
Artificial Intelligence lecture notes. AI summarized notes on uncertainty and handling it through fuzzy logic, tipping problem scenarios are seen in it, for reading and may be for self-learning, I think.
Discussed Elements of Dynamic Programming, covered all the points from Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, Clifford Stein,‖
Introduction to Algorithms‖, Third Edition, Prentice-Hall, 2011.
Case study of Divide and Conquer approach contains information about-merge sort and quick sort algorithms, closest pair of points, binary search, la-russe multiplicaton, min-max problems and also strassen multiplication.
Artificial Intelligence: Introduction, Typical Applications. State Space Search: Depth Bounded
DFS, Depth First Iterative Deepening. Heuristic Search: Heuristic Functions, Best First Search,
Hill Climbing, Variable Neighborhood Descent, Beam Search, Tabu Search. Optimal Search: A
*
algorithm, Iterative Deepening A*
, Recursive Best First Search, Pruning the CLOSED and OPEN
Lists
Artificial Intelligence: Introduction, Typical Applications. State Space Search: Depth Bounded
DFS, Depth First Iterative Deepening. Heuristic Search: Heuristic Functions, Best First Search,
Hill Climbing, Variable Neighborhood Descent, Beam Search, Tabu Search. Optimal Search: A
*
algorithm, Iterative Deepening A*
, Recursive Best First Search, Pruning the CLOSED and OPEN
Lists
Artificial Intelligence lecture notes. AI summarized notes on uncertainty and handling it through fuzzy logic, tipping problem scenarios are seen in it, for reading and may be for self-learning, I think.
Discussed Elements of Dynamic Programming, covered all the points from Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, Clifford Stein,‖
Introduction to Algorithms‖, Third Edition, Prentice-Hall, 2011.
Case study of Divide and Conquer approach contains information about-merge sort and quick sort algorithms, closest pair of points, binary search, la-russe multiplicaton, min-max problems and also strassen multiplication.
This presentation contains information about the divide and conquer algorithm. It includes discussion regarding its part, technique, skill, advantages and implementation issues.
Design and Analysis of Algorithm help to design the algorithms for solving different types of problems in Computer Science. It also helps to design and analyze the logic of how the program will work before developing the actual code for a program.
Optimization problems can be divided into two categories, depending on whether the variables are continuous or discrete:
An optimization problem with discrete variables is known as a discrete optimization, in which an object such as an integer, permutation or graph must be found from a countable set.
A problem with continuous variables is known as a continuous optimization, in which an optimal value from a continuous function must be found. They can include constrained problems and multimodal problems.
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
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
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/
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.
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
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
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.
3. DIVIDE AND CONQUER -3 Part TOP
DOWN APPROCH
TOP down Approach to design Algorithms
1) Divide : Divide the problem into sub problem that is similar to original problem
2) Conquer Solve the problem recursively
3) Combine : Combine the solution of sub problem to create the solution of
original problem
4. APPLICATION OF D&C ALOGORATHIM
Binary Search
Finding Max and Min
Merge Sort
Quick Sort
Strassen Matrix Multiplication
Convex hull
5. DIVIDE and CONQUER – APPROCH
ALGO DANDC (P)
{
if small (p) then return S(p)
else
{
divide P in smaller p1, p2...pk where k >=1
apply DANDC to each sub problem Recursively
return combine ( DANDC(p1), DANDC(p1) ……………… DANDC(pk))
}
}
7. Greedy Algorithms
A greedy algorithm is an algorithm that constructs an object X one step at a time,
at each step choosing the locally best option.
In some cases, greedy algorithms construct the globally best object by repeatedly
choosing the locally best option.
8. Greedy Algorithms – Advantages
Greedy algorithms have several advantages over other algorithmic approaches:
● Simplicity: Greedy algorithms are often easier to describe and code up than other
algorithms.
● Efficiency: Greedy algorithms can often be implemented more efficiently than other
algorithms.
9. Greedy Algorithms – DIS Advantages
Greedy algorithms have several drawbacks:
● Hard to design: Once you have found the right greedy approach, designing greedy
algorithms can be easy. However, finding the right approach can be hard.
● Hard to verify: Showing a greedy algorithm is correct often requires a nuanced
argument.
10. Greedy Algorithms – APPROCH
ALGO GREDDY ( a, n)
{
solution = 0;
for i to n do
{
x = select (a) ;
if feasible (solution , x ) ;
then solution Union (solution , x) ;
}
return solution;
}
11. Applications OF Greedy Algorithms
Activity Selection
Huffman coding
Job Sequencing
Knap Snap
Minimum Spanning tree
Single Source shortest Path
Bellman ford Algorithm
Dijkstra’s Algorithm
13. Dynamic Programming
Used to solve optimization problems.
1. Breaks down the complex problems into simpler subproblems.
2. Find optimal solution to these subproblems.
3. Store the results of these subproblems so that it can be reused later.
4. Finally Calculates the result of complex sub-problem.
14. Dynamic Programming– Advantages
Time Complexity will be always less because it reduces the line code
It speeds up the processing as we use previously calculated intermediate values.
15. Dynamic Programming– Disadvantages
Space Complexity would be more as dividing problem in sub problem and storing
intermediate results in consuming memory.
16. APPLICATION OF Dynamic Programming
Cutting a rod into pieces to Maximize Profit
Matrix Chain Multiplication
Longest Common Subsequence
Optimal Binary Search Tree
Travelling Salesman Problem
0/1 Knapsack Problem
18. BackTracking
Backtracing uses bruit force approach to solve the problem.
The Algorithmic Approach – Backtracking systematically try and search
possibilities to find the solution.
Brute force approach say for any given problem generate all possible solution and
pick a desire solution.
Generate depth first search to generate state space tree.
19. BackTracking– Advantages
Comparison with the Dynamic Programming, Backtracking Approach is more effective
in some cases.
Backtracking Algorithm is the best option for solving tactical problem.
Also Backtracking is effective for constraint satisfaction problem.
In greedy Algorithm, getting the Global Optimal Solution is a long procedure and
depends on user statements but in Backtracking It Can Easily getable.
Backtracking technique is simple to implement and easy to code.
Different states are stored into stack so that the data or Info can be usable anytime.
The accuracy is granted.
20. BackTracking– DisAdvantages
– Backtracking Approach is not efficient for solving strategic Problem.
– The overall runtime of Backtracking Algorithm is normally slow
– To solve Large Problem Sometime it needs to take the help of other techniques like
Branch and bound.
– Need Large amount of memory space for storing different state function in the
stack for big problem.
– Thrashing is one of the main problem of Backtracking. – The Basic Approach
Detects the conflicts too late.
21. BackTracking– APPROCH
Backtrack (v1,Vi)
{
If (V1,……., Vi) is a Solution Then
Return (V1,…, Vi)
For each v DO
If (V1,…….,Vi) is acceptable vector THEN
Solution = try (V1,…,Vi, V);
If Solution != () Then
RETURN Solution ;
}
}
}
22. APPLICATION OF BackTracking
N Queen Problem
Sum of subset problem
Graph Coloring problem
Hamiltonian Circuit Problem
Maze Problem
24. Branch and Bound
Branch and bound is an algorithm design paradigm which is generally used for solving
combinatorial optimization problems.
These problems are typically exponential in terms of time complexity and may require exploring all
possible permutations in worst case.
The Branch and Bound Algorithm technique solves these problems relatively quickly.
BFS (Brute-Force)We can perform a Breadth-first search on the state space tree. This always
finds a goal state nearest to the root. But no matter what the initial state is, the algorithm attempts
the same sequence of moves like Depth-first-search DFS.
Branch and Bound. The search for an answer node can often be speeded by using an “intelligent”
ranking function, also called an approximate cost function to avoid searching in sub-trees that do
not contain an answer node. It is similar to the backtracking technique but uses BFS-like search
25. Branch and Bound– APPROCH
FIFO Branch Bound ( First IN First OUT)
LIFO Branch Bound ( Last IN First OUT)
LC Branch Bound ( Least Cost )
26. APPLICATION OF Branch and Bound
0/1 Knapsack Problem using Least Cost Branch and Bound Algorithm
Travelling Salesman Problem using Branch and Bound
27. 0/1 Knapsack Problem using Dynamic
Programming
• In these kinds of problems,we are given some items with weights and profits.
• The 0/1 denotes that either you will not pick the item(0) or you can pick the item completely(1).
• Cannot take a fractional amount of an item taken or take an item more than once.
• It cannot be solved by the Greedy Approach because it is enable to fill the knapsack to capacity.
• In simple words,0/1 Knapsack Problem means that we want to pack n items in a bag(knapsack):
1. The ith item is worth pi(profit) and weight wi kg.
2. Take as valuable a load as possible, but cannot exceed W pounds.
3. pi,wi,W are integers.
28. Why Dynamic Programming to solve 0/1
Knapsack Problem?
It is a very helpful problem in combinatorics.
Recomputation is avoided as it stores the result of previous subproblem so that it
can be used again in solving another subproblem.
29. Algorithm
Knapsack(n,cw)
1. int M[][] = new int[n + 1][cw + 1];//Build a memoization matrix in bottom up manner.
2. for i<-0 to n
3. for w<-0 to cw
4. if (i == 0 || w == 0) then
5. M[i][w] = 0;
6. else if(gwt[i - 1]<= w) then
7. M[i][w] = max(pro[i - 1] + M[i - 1][w - gwt[i - 1]], M[i - 1][w])
8. else
9. M[i][w] = M[i - 1][w]
10. return M[n][cw]
30. Code in Java to solve 0-1 Knapsack problem Solution
using Dynamic Programming to get Maximum Profit
My Java
Program
0/1 Knapsack Problem using Dynamic Programming