Selection sort and insertion sort are both simple in-place comparison sorting algorithms. Selection sort works by iterating through the list and swapping elements to put the smallest remaining element in the sorted position at each step. Insertion sort maintains a sorted sub-list, scanning unsorted items and inserting them into the correct position within the sub-list. Both algorithms have Ο(n2) time complexity, making them inefficient for large data sets.
Searching and sorting
Types of Searching
1. Linear Searching
2. Binary Searching
Types of Sorting
1.Selection Sort
2. Insertion Sort
3.Bubble Sort
And the examples of Linear searching, Binary Searching
And also the examples of Selection sort, Insertion sort and Bubble sort and describing them in detail in this ppt
Searching and sorting
Types of Searching
1. Linear Searching
2. Binary Searching
Types of Sorting
1.Selection Sort
2. Insertion Sort
3.Bubble Sort
And the examples of Linear searching, Binary Searching
And also the examples of Selection sort, Insertion sort and Bubble sort and describing them in detail in this ppt
Sorting in data structures is a fundamental operation that is crucial for optimizing the efficiency of data retrieval and manipulation. By ordering data elements according to a defined sequence (numerical, lexicographical, etc.), sorting makes it possible to search for elements more quickly than would be possible in an unsorted structure, especially with algorithms like binary search that rely on a sorted array to operate effectively.
In addition, sorting is essential for tasks that require an ordered dataset, such as finding median values, generating frequency counts, or performing range queries. It also lays the groundwork for more complex operations, such as merging datasets, which requires sorted data to be carried out efficiently.
Binary search works on sorted arrays. Binary search begins by comparing an element in the middle of the array with the target value. If the target value matches the element, its position in the array is returned. If the target value is less than the element, the search continues in the lower half of the array.
Traversal is a process to visit all the nodes of a tree and may print their values too. Because, all nodes are connected via edges (links) we always start from the root (head) node. That is, we cannot randomly access a node in a tree.
Sorting in data structures is a fundamental operation that is crucial for optimizing the efficiency of data retrieval and manipulation. By ordering data elements according to a defined sequence (numerical, lexicographical, etc.), sorting makes it possible to search for elements more quickly than would be possible in an unsorted structure, especially with algorithms like binary search that rely on a sorted array to operate effectively.
In addition, sorting is essential for tasks that require an ordered dataset, such as finding median values, generating frequency counts, or performing range queries. It also lays the groundwork for more complex operations, such as merging datasets, which requires sorted data to be carried out efficiently.
Binary search works on sorted arrays. Binary search begins by comparing an element in the middle of the array with the target value. If the target value matches the element, its position in the array is returned. If the target value is less than the element, the search continues in the lower half of the array.
Traversal is a process to visit all the nodes of a tree and may print their values too. Because, all nodes are connected via edges (links) we always start from the root (head) node. That is, we cannot randomly access a node in a tree.
How can we Sort the arrays in using different sorting types, techniques and methods in Data Structure. (Bubble Sort, Merge Sort, Quick Sort, Heap Sort)
In this article, different types of sorting algorithms like the bubble sort, selection sort, etc are discussed. The working method, implementation using C language, and time complexity of different algorithms are also discussed.
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Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
Model Attribute Check Company Auto PropertyCeline George
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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.
2. SELECTION SORT
Selection sort is a simple sorting algorithm. This sorting algorithm is an in-place comparison-
based algorithm in which the list is divided into two parts, the sorted part at the left end and the unsorted
part at the right end. Initially, the sorted part is empty and the unsorted part is the entire list.
The smallest element is selected from the unsorted array and swapped with the leftmost element, and
that element becomes a part of the sorted array. This process continues moving
unsorted array boundary by one element to the right.
This algorithm is not suitable for large data sets as its average and worst case complexities are of Ο(n2),
where n is the number of items.
3. HOW SELECTION SORT WORKS?
Consider the following depicted array as an example.
For the first position in the sorted list, the whole list is scanned sequentially. The first position where 14
is stored presently, we search the whole list and find that 10 is the lowest value.
So we replace 14 with 10. After one iteration 10, which happens to be the minimum value in the list,
appears in the first position of the sorted list.
4. HOW SELECTION SORT WORKS?
For the second position, where 33 is residing, we start scanning the rest of the list in a linear manner.
We find that 14 is the second lowest value in the list and it should appear at the second place. We swap
these values.
After two iterations, two least values are positioned at the beginning in a sorted manner.
10. INSERTION SORT
This is an in-place comparison-based sorting algorithm. Here, a sub-list is maintained which is always
sorted. For example, the lower part of an array is maintained to be sorted. An element which is to be
'insert'ed in this sorted sub-list, has to find its appropriate place and then it has to be inserted there.
Hence the name, insertion sort.
The array is searched sequentially and unsorted items are moved and inserted into the sorted sub-list
(in the same array). This algorithm is not suitable for large data sets as its average and worst case
complexity are of Ο(n2), where n is the number of items.
11. HOW INSERTION SORT WORKS?
We take an unsorted array for our example.
Insertion sort compares the first two elements.
It finds that both 14 and 33 are already in ascending order. For now, 14 is in sorted sub-list.
12. HOW INSERTION SORT WORKS?
Insertion sort moves ahead and compares 33 with 27.
And finds that 33 is not in the correct position.
It swaps 33 with 27. It also checks with all the elements of sorted sub-list. Here we see that the sorted
sub-list has only one element 14, and 27 is greater than 14. Hence, the sorted sub-list remains sorted
after swapping.
13. HOW INSERTION SORT WORKS?
By now we have 14 and 27 in the sorted sub-list. Next, it compares 33 with 10.
These values are not in a sorted order.
So we swap them.
14. HOW INSERTION SORT WORKS?
However, swapping makes 27 and 10 unsorted.
Hence, we swap them too.
Again we find 14 and 10 in an unsorted order.
We swap them again. By the end of third iteration, we have a sorted sub-list of 4 items.
This process goes on until all the unsorted values are covered in a sorted sub-list.