This document provides an overview of an algorithms and data structures course. It includes the instructor's name and contact information. It outlines the topics that will be covered in the course, including fundamentals of algorithms, sorting and searching, stacks, queues, linked lists, trees, hashing, and graphs. It provides recommendations for reference books and describes the marking scheme for assignments, quizzes, exams, and projects.
This is the second lecture in the CS 6212 class. Covers asymptotic notation and data structures. Also outlines the coming lectures wherein we will study the various algorithm design techniques.
Hi:
This is the first slide of my class on analysis of algorithms based in Cormen's book.
In this slides, we define the following concepts:
1.- What is an algorithm?
2.- What problems are solved by algorithms?
3.- What subjects will be studied in this class?
4.- Cautionary tale about complexities
This is the second lecture in the CS 6212 class. Covers asymptotic notation and data structures. Also outlines the coming lectures wherein we will study the various algorithm design techniques.
Hi:
This is the first slide of my class on analysis of algorithms based in Cormen's book.
In this slides, we define the following concepts:
1.- What is an algorithm?
2.- What problems are solved by algorithms?
3.- What subjects will be studied in this class?
4.- Cautionary tale about complexities
This file contains the contents about dynamic programming, greedy approach, graph algorithm, spanning tree concepts, backtracking and branch and bound approach.
This file contains the contents about dynamic programming, greedy approach, graph algorithm, spanning tree concepts, backtracking and branch and bound approach.
Algorithm Design and Complexity - Course 1&2Traian Rebedea
Courses 1 & 2 for the Algorithm Design and Complexity course at the Faculty of Engineering in Foreign Languages - Politehnica University of Bucharest, Romania
Analysis and Design of Algorithms (ADA): An In-depth Exploration
Introduction:
The field of computer science is heavily reliant on algorithms to solve complex problems efficiently. The analysis and design of algorithms (ADA) is a fundamental area of study that focuses on understanding and creating efficient algorithms. This comprehensive overview will delve into the various aspects of ADA, including its importance, key concepts, techniques, and applications.
Importance of ADA:
Efficient algorithms play a critical role in various domains, including software development, data analysis, artificial intelligence, and optimization. ADA provides the tools and techniques necessary to design algorithms that are both correct and efficient. By analyzing the performance characteristics of algorithms, ADA enables computer scientists and engineers to develop solutions that save time, resources, and computational power.
Key Concepts in ADA:
Correctness: ADA emphasizes the importance of designing algorithms that produce correct outputs for all possible inputs. Techniques like mathematical proofs and induction are used to establish the correctness of algorithms.
Complexity Analysis: ADA seeks to analyze the efficiency of algorithms by examining their time and space complexity. Time complexity measures the amount of time required by an algorithm to execute, while space complexity measures the amount of memory consumed.
Asymptotic Notations: ADA employs asymptotic notations, such as Big O, Omega, and Theta, to express the growth rates of functions and classify the efficiency of algorithms. These notations allow for a concise comparison of algorithmic performance.
Algorithm Design Paradigms: ADA explores various design paradigms, including divide and conquer, dynamic programming, greedy algorithms, and backtracking. Each paradigm offers a systematic approach to solving problems efficiently.
Techniques in ADA:
Divide and Conquer: This technique involves breaking down a problem into smaller subproblems, solving them independently, and combining the solutions to obtain the final result. Well-known algorithms like Merge Sort and Quick Sort utilize the divide and conquer approach.
Dynamic Programming: Dynamic programming breaks down a complex problem into a series of overlapping subproblems and solves them in a bottom-up manner. This technique optimizes efficiency by storing and reusing intermediate results. The Fibonacci sequence calculation is a classic example of dynamic programming.
Greedy Algorithms: Greedy algorithms make locally optimal choices at each step, with the hope of achieving a global optimal solution. These algorithms are efficient but may not always yield the best overall solution. The Huffman coding algorithm for data compression is a widely used example of a greedy algorithm.
Backtracking: Backtracking involves searching for a solution to a problem by incrementally building a solution and undoing the choices that lead to dead-ends.
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
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.
Palestine last event orientationfvgnh .pptxRaedMohamed3
An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
Instructions for Submissions thorugh G- Classroom.pptxJheel Barad
This presentation provides a briefing on how to upload submissions and documents in Google Classroom. It was prepared as part of an orientation for new Sainik School in-service teacher trainees. As a training officer, my goal is to ensure that you are comfortable and proficient with this essential tool for managing assignments and fostering student engagement.
2. My Profile
Name:
Syed Muhammad Raza
Education:
MSc Wireless Communication
LTH – Sweden , 2007-2009
BS Computer Information Sciences
PIEAS – Pakistan , 2002-2006
Contact Information:
Email: (Preferred mean of communication)
smraza@comsats.edu.pk
**(In subject of your email mention your semester and name, otherwise it will not be
entertained)
3. Rules
Take it seriously & you will prosper
Take it lightly & you will doom
4. Course Overview
Fundamentals of Algorithms
Review of Programming
Sorting and Searching
Stacks
Queues, Priority Queues and Circular Queues
Linked List
Trees
Hashing
Graphs
and much more …..
6. Recommended Books
Schaum Programming with Cplusplus by Seymour Lipschutz
Sams Teach Yourself Data Structures and Algorithms in 24 Hours, Lafore Robert
Data structures and algorithms, Alfred V. Aho, John E. Hopcroft.
Standish, Thomas A., Data Structures, Algorithms and Software Principles in C,
Addison-Wesley 1995, ISBN: 0-201-59118-9
Data Structures & Algorithm Analysis in C++, Weiss Mark Allen
7. Marks Distribution
Lab 25% of 100 Marks (Semester Project & Presentation)
Theory 75% of 100 Marks
Sessional 1 = 10 Marks
Sessional 2 = 15 Marks
Final Paper = 50 Marks
Assignment = 15 Marks
Quiz = 10 Marks (Surprise)
Total = 100 Marks
9. Quiz 1
Marks:2
Time: 15mins
Explain Abstract Data Types?
Explain difference between abstraction and
encapsulation?
If you have to write a code for swapping int values,
arrays will be appropriate solution or 2D arrays?
11. Problem Solving
Problem solving is the process of transforming the
description of a problem into the solution of that problem
by using our knowledge of the problem domain and by
relying on our ability to select and use appropriate
problem-solving strategies, techniques, and tools.
12. Algorithms
An Algorithm is a step by step solution to a problem
Why bother writing an algorithm
For your own use in the future - Don't have to rethink the
problem.
So others can solve the problem, even if they know very
little about the principles behind how the solution was
derived.
13. Examples of Algorithms
Washing machine instructions
Instructions for a ready-to-assemble piece of furniture
A Classic: GCD - Greatest Common Divisor - The
Euclidean Algorithm
14. Washing Machine Instructions
Separate clothes into white clothes and colored clothes.
For white clothes:
Set water temperature knob to HOT.
Place white laundry in tub.
For colored clothes:
Set water temperature knob to COLD
Place colored laundry in tub.
Add 1 cup of powdered laundry detergent to the tub.
Close lid and press the start button.
15. Observations
There are a finite number of steps.
We are capable of doing each of the instructions.
When we have followed all of the steps, the washing
machine will wash the clothes and then will stop.
Are all of the clothes clean ?
Do we want the washing machine to run until all of the
clothes are clean ?
16. Refinement of the Definition
Our old definition:
An algorithm is a step by step solution to a problem.
Adding our observations:
An algorithm is a finite set of executable instructions that directs a
terminating activity.
17. Instructions for a Ready-to-Assemble Furniture
"Align the marks on side A with the grooves on Part F “
Why are these instructions typically so hard to follow ?
Lack of proper tools - instructions are not executable
Which side is A ? A & B look alike. Both line up with Part F -
Ambiguous instructions.
18. Final Definition
An algorithm is a finite set of unambiguous, executable
instructions that directs a terminating activity.
19. History of Algorithms
The study of algorithms began as a subject in mathematics.
The search for algorithms was a significant activity of early
mathematicians.
Goal: To find a single set of instructions that could be used to solve
any problem of a particular type.
20. GCD- Euclidean Algorithm
Assign M and N the value of the larger and the value of the smaller
of the two positive integer input values, respectively.
Divide M by N and call the remainder R.
If R is not 0, then assign M the value of N, assign N the value of R
and return to step 2, otherwise the greatest common divisor is the
value currently assigned to N.
21. Finding GCD of 24 & 9
MNR
24 9 6
963
630
So 3 is the GCD of 24 and 9.
Do we need to know the theory that Euclid used to come up with this
algorithm in order to use it ?
What intelligence is required to find the GCD using this algorithm ?
22. Idea Behind the Algorithms
Once an algorithm behind a task has been discovered
Don't need to understand the principles.
Task is reduced to following the instructions.
Intelligence is "encoded into the algorithm"
23. Algorithm Representation
Syntax and Semantics
Syntax refers to the representation itself.
Semantics refers to the concept represented.