This document presents an approximation algorithm for the vertex cover problem in graphs. It begins with definitions of the vertex cover problem and shows that finding an optimal solution is NP-complete. It then presents a 2-approximation algorithm that finds a vertex cover of size at most twice the optimal. The time complexity of the algorithm is O(V+E). Applications of the vertex cover problem and some open questions are also discussed.
This is a short presentation on Vertex Cover Problem for beginners in the field of Graph Theory...
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The Bellman–Ford algorithm is an algorithm that computes shortest paths from a single source vertex to all of the other vertices in a weighted digraph.
This is a short presentation on Vertex Cover Problem for beginners in the field of Graph Theory...
Download the presentation for a better experience...
The Bellman–Ford algorithm is an algorithm that computes shortest paths from a single source vertex to all of the other vertices in a weighted digraph.
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.
P, NP, NP-Complete, and NP-Hard
Reductionism in Algorithms
NP-Completeness and Cooks Theorem
NP-Complete and NP-Hard Problems
Travelling Salesman Problem (TSP)
Travelling Salesman Problem (TSP) - Approximation Algorithms
PRIMES is in P - (A hope for NP problems in P)
Millennium Problems
Conclusions
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.
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.
P, NP, NP-Complete, and NP-Hard
Reductionism in Algorithms
NP-Completeness and Cooks Theorem
NP-Complete and NP-Hard Problems
Travelling Salesman Problem (TSP)
Travelling Salesman Problem (TSP) - Approximation Algorithms
PRIMES is in P - (A hope for NP problems in P)
Millennium Problems
Conclusions
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.
The knapsack problem or rucksack problem is a problem in combinatorial optimization: Given a set of items, each with a weight and a value, determine the count of each item to include in a collection so that the total weight is less than or equal to a given limit and the total value is as large as possible. It derives its name from the problem faced by someone who is constrained by a fixed-size knapsack and must fill it with the most useful items.
http://java90.blogspot.com/2012/02/knapsack-problem-in-java.html
NP completeness. Classes P and NP are two frequently studied classes of problems in computer science. Class P is the set of all problems that can be solved by a deterministic Turing machine in polynomial time.
Dynamic Programming is one of the most interesting design techniques. The concise idea is to avoid recomputations. Matrix Chain Multiplication and All Pairs Shortest Paths are two interesting applications of this design technique
Euclid in a Taxicab: Sparse Blind Deconvolution with Smoothed l_1/l_2 Regular...Laurent Duval
The l1/l2 ratio regularization function has shown good performance for retrieving sparse signals in a number of recent works, in the context of blind deconvolution. Indeed, it benefits from a scale invariance property much desirable in the blind context. However, the l1/l2 function raises some difficulties when solving the nonconvex and nonsmooth minimization problems resulting from the use of such a penalty term in current restoration methods. In this paper, we propose a new penalty based on a smooth approximation to the l1/l2 function. In addition, we develop a proximal-based algorithm to solve variational problems involving this function and we derive theoretical convergence results. We demonstrate the effectiveness of our method through a comparison with a recent alternating optimization strategy dealing with the exact l1/l2 term, on an application to seismic data blind deconvolution.
Seminar giving an overview of quantum programming languages, given in the Programming Languages Group at the University of Waterloo in 2006. [Updated with name change]
Discrete Logarithmic Problem- Basis of Elliptic Curve CryptosystemsNIT Sikkim
ECC was developed in 1985 independently by Neal Koblitz and Victor Miller. Both men saw the application of the elliptic curve discrete log problem (ECDLP) as a replacement for the conventional discrete log problem (DLP) which is used in DSA, and the integer factorization problem found in RSA. For both problems, sub-exponential solutions have been generated; the
same which cannot be said for ECDLP . In addition to offering increased security for a smaller key size, operations of adding and doubling can be optimized successfully on a mobile
platform . ECC offers a viable replacement to the most common public-key cryptography algorithms on mobile devices.
Lecture 1 from https://irdta.eu/deeplearn/2022su/
Covers concepts from Part 1 of my new book, https://meyn.ece.ufl.edu/2021/08/01/control-systems-and-reinforcement-learning/
Exact Matrix Completion via Convex Optimization Slide (PPT)Joonyoung Yi
Slide of the paper "Exact Matrix Completion via Convex Optimization" of Emmanuel J. Candès and Benjamin Recht. We presented this slide in KAIST CS592 Class, April 2018.
- Code: https://github.com/JoonyoungYi/MCCO-numpy
- Abstract of the paper: We consider a problem of considerable practical interest: the recovery of a data matrix from a sampling of its entries. Suppose that we observe m entries selected uniformly at random from a matrix M. Can we complete the matrix and recover the entries that we have not seen? We show that one can perfectly recover most low-rank matrices from what appears to be an incomplete set of entries. We prove that if the number m of sampled entries obeys
𝑚≥𝐶𝑛1.2𝑟log𝑛
for some positive numerical constant C, then with very high probability, most n×n matrices of rank r can be perfectly recovered by solving a simple convex optimization program. This program finds the matrix with minimum nuclear norm that fits the data. The condition above assumes that the rank is not too large. However, if one replaces the 1.2 exponent with 1.25, then the result holds for all values of the rank. Similar results hold for arbitrary rectangular matrices as well. Our results are connected with the recent literature on compressed sensing, and show that objects other than signals and images can be perfectly reconstructed from very limited information.
Normal Labour/ Stages of Labour/ Mechanism of LabourWasim Ak
Normal labor is also termed spontaneous labor, defined as the natural physiological process through which the fetus, placenta, and membranes are expelled from the uterus through the birth canal at term (37 to 42 weeks
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdfTechSoup
In this webinar you will learn how your organization can access TechSoup's wide variety of product discount and donation programs. From hardware to software, we'll give you a tour of the tools available to help your nonprofit with productivity, collaboration, financial management, donor tracking, security, and more.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
6. Approximation
Algorithm:
The
Vertex-cover
Problem
Sudipta Saha
Shubha
1205014
Repon Kumar
Roy 1205002
Motivational
Problem
Formal Definition of Vertex Cover Problem
A vertex cover of an undirected graph
G = (V , E) is a subset V ∈ V such that
if (u, v) is an edge of G, then either
u ∈ V or v ∈ V (or both).
The vertex-cover problem is to find a
vertex cover of minimum size in a given
undirected graph. We call such a vertex
cover an optimal vertex cover.
24. Approximation
Algorithm:
The
Vertex-cover
Problem
Sudipta Saha
Shubha
1205014
Repon Kumar
Roy 1205002
The
Approximate
Algorithm
Applications
Some
Questions
Proof of 2-Approximation
We say that an algorithm for a problem has an
approximation ratio of ρ(n) if, for any input of
size n, the cost X of the solution produced by
the algorithm is within a factor of ρ(n) of the
cost X∗
of an optimal solution:
max( X
X∗ , X∗
X )≤ ρ(n).