Bubble sort, selection sort, and insertion sort are O(n^2) sorting algorithms discussed in the document. Bubble sort compares and swaps adjacent elements, selection sort finds the minimum element and swaps it into place each iteration, and insertion sort inserts each new element into the sorted portion of the array. Merge sort is more efficient at O(n log n) time by dividing the array into halves, sorting them, and merging the results. It is well-suited for large datasets that do not fit into memory.
In computer science a sorting algorithm is an algorithm that puts elements of a list in a certain order. The most-used orders are numerical order and lexicographical order. Efficient sorting is important for optimizing the use of other algorithms (such as search and merge algorithms) which require input data to be in sorted lists; it is also often useful for canonicalizing data and for producing human-readable output. More formally, the output must satisfy two conditions:
The output is in nondecreasing order (each element is no smaller than the previous element according to the desired total order);
The output is a permutation (reordering) of the input.
Further, the data is often taken to be in an array, which allows random access, rather than a list, which only allows sequential access, though often algorithms can be applied with suitable modification to either type of data.
[Greach 17] make concurrency groovy againAlonso Torres
Don’t get me wrong. Concurrency is already “groovy” in Groovy but with each passing day we usualy try to focus on even higher and higher levels of abstraction. From GPars to Rx we’re treating concurrency as a byproduct of our paradigms so usualy when things got ugly (as usualy happens with concurrency) we don’t have a “Plan B”.
In this talk I’ll try to ground some of the possible flavors of concurrency that we have available inside the Groovy language and the JVM ecosystem starting with threads and going through higher levels like Rx and even Akka.
In computer science a sorting algorithm is an algorithm that puts elements of a list in a certain order. The most-used orders are numerical order and lexicographical order. Efficient sorting is important for optimizing the use of other algorithms (such as search and merge algorithms) which require input data to be in sorted lists; it is also often useful for canonicalizing data and for producing human-readable output. More formally, the output must satisfy two conditions:
The output is in nondecreasing order (each element is no smaller than the previous element according to the desired total order);
The output is a permutation (reordering) of the input.
Further, the data is often taken to be in an array, which allows random access, rather than a list, which only allows sequential access, though often algorithms can be applied with suitable modification to either type of data.
[Greach 17] make concurrency groovy againAlonso Torres
Don’t get me wrong. Concurrency is already “groovy” in Groovy but with each passing day we usualy try to focus on even higher and higher levels of abstraction. From GPars to Rx we’re treating concurrency as a byproduct of our paradigms so usualy when things got ugly (as usualy happens with concurrency) we don’t have a “Plan B”.
In this talk I’ll try to ground some of the possible flavors of concurrency that we have available inside the Groovy language and the JVM ecosystem starting with threads and going through higher levels like Rx and even Akka.
Discrete time signals are handled by discrete systems. The discrete time signals are dissected with the aid of Discrete Time Fourier Series (DTFS), Discrete Time Fourier Transform (DTFT), discrete fourier transform (DFT) and z-transform. Copy the link given below and paste it in new browser window to get more information on Determination of DTFT:- http://www.transtutors.com/homework-help/digital-signal-processing/frequency-analysis-dtft/determine-dtft-example.aspx
This presentation educates you about Python - Multithreaded
Programming, Starting a New Thread, The Threading Module, Creating Thread Using Threading Module, Synchronizing Threads and Multithreaded Priority Queue.
For more topics stay tuned with Learnbay.
A Survey of Adaptive QuickSort AlgorithmsCSCJournals
In this paper, a survey of adaptive quicksort algorithms is presented. Adaptive quicksort algorithms improve on the worst case behavior of quicksort when the list of elements is sorted or nearly sorted. These algorithms take into consideration the already existing order in the input list to be sorted. A detailed description of each algorithm is provided. The paper provides an empirical study of these algorithms by comparing each algorithm in terms of the number of comparisons performed and the running times when used for sorting arrays of integers that are already sorted, sorted in reverse order, and generated randomly.
Multithreaded fundamentals
The thread class and runnable interface
Creating a thread
Creating multiple threads
Determining when a thread ends
Thread priorities
Synchronization
Using synchronized methods
The synchronized statement
Thread communication using notify(), wait() and notifyall()
Suspending , resuming and stopping threads
Discrete time signals are handled by discrete systems. The discrete time signals are dissected with the aid of Discrete Time Fourier Series (DTFS), Discrete Time Fourier Transform (DTFT), discrete fourier transform (DFT) and z-transform. Copy the link given below and paste it in new browser window to get more information on Determination of DTFT:- http://www.transtutors.com/homework-help/digital-signal-processing/frequency-analysis-dtft/determine-dtft-example.aspx
This presentation educates you about Python - Multithreaded
Programming, Starting a New Thread, The Threading Module, Creating Thread Using Threading Module, Synchronizing Threads and Multithreaded Priority Queue.
For more topics stay tuned with Learnbay.
A Survey of Adaptive QuickSort AlgorithmsCSCJournals
In this paper, a survey of adaptive quicksort algorithms is presented. Adaptive quicksort algorithms improve on the worst case behavior of quicksort when the list of elements is sorted or nearly sorted. These algorithms take into consideration the already existing order in the input list to be sorted. A detailed description of each algorithm is provided. The paper provides an empirical study of these algorithms by comparing each algorithm in terms of the number of comparisons performed and the running times when used for sorting arrays of integers that are already sorted, sorted in reverse order, and generated randomly.
Multithreaded fundamentals
The thread class and runnable interface
Creating a thread
Creating multiple threads
Determining when a thread ends
Thread priorities
Synchronization
Using synchronized methods
The synchronized statement
Thread communication using notify(), wait() and notifyall()
Suspending , resuming and stopping threads
In our thesis work, we try to find out the efficiency of several sorting algorithms and generate a comparative report according to performance, based on experimental data size and data order for all algorithm. To do this we have researched, analyzed about 9 different types of well-known sorting algorithms. We convert the algorithms to programmable code, compile and run with different set of data. We keep the sorting algorithm’s description as it is. We give focus on, how the algorithms work, considering their total operation count (assignment count, comparison count) and complexity based on our same data set for all algorithm. We write programming code for each sorting algorithm in C programming language. In our investigation, we have also worked with big and small data for different cases (ordered, pre-ordered, random, disordered) and put their result in different tables. We show the increasing ratio to compare the result. we also show the data in graphical chart for view comparative report in same window. We mark their efficiency with point and ranked them. At last we discussed their result of efficiency in a single table. We modify the merge sort and try to make an improved tri-merge sorting algorithm that is more efficient than marge sort. Theoretically if we divide and conquer with higher number its result is better, some paper exists on it, but to manage the algorithm, there cost lot of operations count. Like, if we consider quadratic divide-conquer, its manage complexly is huge than binary divide-conquer that why we generally use binary merge. We found trimerge is theoretically and practically true based on investigation data set. Tri-marge take some more compare operation for manage and sort when data remain 1 or 2 at last stage, whereas binary merge don’t need such compare. But for big data size tri-merge gain lot of operation count that give significant result that declare tri-merge is more efficient than merge sort algorithm. We also experiment with penta-merge algorithm which give more better result but algorithm and implementation is too complex.
We shall try to define the tri-merge algorithm so that it can be used to implement in any
programming language. It will help students, researchers to use the algorithm, as like we
got the various algorithm structure over the internet.
Google Algorithm Change History - 2k14-2k16.Saba SEO
For the organizations like "Saba SEO" to stick to the Google exercises and generate the relevant piece of content, it is important to know about the Google algorithm changes. The work of these algo(s) is to restrict companies and individual from spamming the content of the website with links that risen up the website traffic. In other words, these algorithms do justice with the sites in order to rank in the search engines. By the help of algorithms, if someone is doing spam for the sake of rankings can't take your place if you're on the right path. This infographic explains the 13 algorithms used by Google in between 2014 and 2016.
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.
This slide explains three (3) basic sorting algorithms with codes on github. Bubble sort, Selection sort and insertion sort.
visit https://github.com/EngrMikolo/BasicSortingAlgorithms to checkout the codes
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
Online aptitude test management system project report.pdfKamal Acharya
The purpose of on-line aptitude test system is to take online test in an efficient manner and no time wasting for checking the paper. The main objective of on-line aptitude test system is to efficiently evaluate the candidate thoroughly through a fully automated system that not only saves lot of time but also gives fast results. For students they give papers according to their convenience and time and there is no need of using extra thing like paper, pen etc. This can be used in educational institutions as well as in corporate world. Can be used anywhere any time as it is a web based application (user Location doesn’t matter). No restriction that examiner has to be present when the candidate takes the test.
Every time when lecturers/professors need to conduct examinations they have to sit down think about the questions and then create a whole new set of questions for each and every exam. In some cases the professor may want to give an open book online exam that is the student can take the exam any time anywhere, but the student might have to answer the questions in a limited time period. The professor may want to change the sequence of questions for every student. The problem that a student has is whenever a date for the exam is declared the student has to take it and there is no way he can take it at some other time. This project will create an interface for the examiner to create and store questions in a repository. It will also create an interface for the student to take examinations at his convenience and the questions and/or exams may be timed. Thereby creating an application which can be used by examiners and examinee’s simultaneously.
Examination System is very useful for Teachers/Professors. As in the teaching profession, you are responsible for writing question papers. In the conventional method, you write the question paper on paper, keep question papers separate from answers and all this information you have to keep in a locker to avoid unauthorized access. Using the Examination System you can create a question paper and everything will be written to a single exam file in encrypted format. You can set the General and Administrator password to avoid unauthorized access to your question paper. Every time you start the examination, the program shuffles all the questions and selects them randomly from the database, which reduces the chances of memorizing the questions.
Understanding Inductive Bias in Machine LearningSUTEJAS
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
6th International Conference on Machine Learning & Applications (CMLA 2024)ClaraZara1
6th International Conference on Machine Learning & Applications (CMLA 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of on Machine Learning & Applications.
A review on techniques and modelling methodologies used for checking electrom...nooriasukmaningtyas
The proper function of the integrated circuit (IC) in an inhibiting electromagnetic environment has always been a serious concern throughout the decades of revolution in the world of electronics, from disjunct devices to today’s integrated circuit technology, where billions of transistors are combined on a single chip. The automotive industry and smart vehicles in particular, are confronting design issues such as being prone to electromagnetic interference (EMI). Electronic control devices calculate incorrect outputs because of EMI and sensors give misleading values which can prove fatal in case of automotives. In this paper, the authors have non exhaustively tried to review research work concerned with the investigation of EMI in ICs and prediction of this EMI using various modelling methodologies and measurement setups.
ACEP Magazine edition 4th launched on 05.06.2024Rahul
This document provides information about the third edition of the magazine "Sthapatya" published by the Association of Civil Engineers (Practicing) Aurangabad. It includes messages from current and past presidents of ACEP, memories and photos from past ACEP events, information on life time achievement awards given by ACEP, and a technical article on concrete maintenance, repairs and strengthening. The document highlights activities of ACEP and provides a technical educational article for members.
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2. 2
Bubble sort
Compare each element (except the last one) with its
neighbor to the right
If they are out of order, swap them
This puts the largest element at the very end
The last element is now in the correct and final place
Compare each element (except the last two) with its
neighbor to the right
If they are out of order, swap them
This puts the second largest element next to last
The last two elements are now in their correct and final places
Compare each element (except the last three) with its
neighbor to the right
Continue as above until you have no unsorted elements on the left
4. 4
Code for bubble sort
public static void bubbleSort(int[] a) {
int outer, inner;
for (outer = a.length - 1; outer > 0; outer--) { // counting down
for (inner = 0; inner < outer; inner++) { // bubbling up
if (a[inner] > a[inner + 1]) { // if out of order...
int temp = a[inner]; // ...then swap
a[inner] = a[inner + 1];
a[inner + 1] = temp;
}
}
}
}
5. 5
Analysis of bubble sort
for (outer = a.length - 1; outer > 0; outer--) {
for (inner = 0; inner < outer; inner++) {
if (a[inner] > a[inner + 1]) {
// code for swap omitted
} } }
Let n = a.length = size of the array
The outer loop is executed n-1 times (call it n, that’s close enough)
Each time the outer loop is executed, the inner loop is executed
Inner loop executes n-1 times at first, linearly dropping to just once
On average, inner loop executes about n/2 times for each execution
of the outer loop
In the inner loop, the comparison is always done (constant time), the
swap might be done (also constant time)
Result is n * n/2 * k, that is, O(n2/2 + k) = O(n2
)
6. 6
Loop invariants
You run a loop in order to change things
Oddly enough, what is usually most important in understanding a
loop is finding an invariant: that is, a condition that doesn’t
change
In bubble sort, we put the largest elements at the end, and once
we put them there, we don’t move them again
The variable outer starts at the last index in the array and decreases to 0
Our invariant is: Every element to the right of outer is in the correct place
That is, for all j > outer, if i < j, then a[i] <= a[j]
When this is combined with the loop exit test, outer == 0, we know that
all elements of the array are in the correct place
7. 7
Selection sort
Given an array of length n,
Search elements 0 through n-1 and select the smallest
Swap it with the element in location 0
Search elements 1 through n-1 and select the smallest
Swap it with the element in location 1
Search elements 2 through n-1 and select the smallest
Swap it with the element in location 2
Search elements 3 through n-1 and select the smallest
Swap it with the element in location 3
Continue in this fashion until there’s nothing left to search
8. 8
Example and analysis of selection sort
The selection sort might swap an
array element with itself--this is
harmless, and not worth checking for
Analysis:
The outer loop executes n-1 times
The inner loop executes about n/2
times on average (from n to 2 times)
Work done in the inner loop is constant
(swap two array elements)
Time required is roughly (n-1)*(n/2)
You should recognize this as O(n2
)
7 2 8 5 4
2 7 8 5 4
2 4 8 5 7
2 4 5 8 7
2 4 5 7 8
9. 9
Code for selection sort
public static void selectionSort(int[] a) {
int outer, inner, min;
for (outer = 0; outer < a.length - 1; outer++) {
min = outer;
for (inner = outer + 1; inner < a.length; inner++) {
if (a[inner] < a[min]) {
min = inner;
}
// Invariant: for all i, if outer <= i <= inner, then a[min] <= a[i]
}
// a[min] is least among a[outer]..a[a.length - 1]
int temp = a[outer];
a[outer] = a[min];
a[min] = temp;
// Invariant: for all i <= outer, if i < j then a[i] <= a[j]
}
}
10. 10
Invariants for selection sort
For the inner loop:
This loop searches through the array, incrementing inner from its initial
value of outer+1 up to a.length-1
As the loop proceeds, min is set to the index of the smallest number
found so far
Our invariant is:
for all i such that outer <= i <= inner, a[min] <= a[i]
For the outer (enclosing) loop:
The loop counts up from outer = 0
Each time through the loop, the minimum remaining value is put in
a[outer]
Our invariant is:
for all i <= outer, if i < j then a[i] <= a[j]
11. 11
Insertion sort
The outer loop of insertion sort is:
for (outer = 1; outer < a.length; outer++) {...}
The invariant is that all the elements to the left of outer
are sorted with respect to one another
For all i < outer, j < outer, if i < j then a[i] <= a[j]
This does not mean they are all in their final correct place; the
remaining array elements may need to be inserted
When we increase outer, a[outer-1] becomes to its left; we must
keep the invariant true by inserting a[outer-1] into its proper
place
This means:
Finding the element’s proper place
Making room for the inserted element (by shifting over other
elements)
Inserting the element
12. 12
One step of insertion sort
3 4 7 12 14 14 20 21 33 38 10 55 9 23 28 16
sorted next to be inserted
3 4 7 55 9 23 28 16
10
temp
3833212014141210
sorted
less than 10
13. 13
Analysis of insertion sort
We run once through the outer loop, inserting each of n
elements; this is a factor of n
On average, there are n/2 elements already sorted
The inner loop looks at (and moves) half of these
This gives a second factor of n/4
Hence, the time required for an insertion sort of an array
of n elements is proportional to n2
/4
Discarding constants, we find that insertion sort is O(n2
)
14. Merge sort
30 24 7 12 14 4 20 21 33 38 10 55 9 23 28 16
33 38 10 55 9 23 28 1630 24 7 12 14 4 20 21
Split the array into
two or more parts
Sort each part individually
4 7 12 14 20 21 24 30 9 10 16 23 28 33 38 55
Merge
4 7 9 10 12 14 16 20 21 23 24 28 30 33 38 55
15. Why merge sort?
Merge sort isn’t an “in place” sort—it requires extra
storage
However, it doesn’t require this storage “all at once”
This means you can use merge sort to sort something
that doesn’t fit in memory—say, 300 million census
records—then much of the data must be kept on backup
media, such as a hard drive
Merge sort is a good way to do this
15
16. Using merge sort for large data sets
Very roughly, here’s how to sort large amounts of data:
Repeat:
Read in as much data as fits in memory
Sort it, using a fast sorting algorithm (quicksort may be a good choice)
Write out the sorted data to a new file
After all the data has been written into smaller, individually
sorted files:
Read in the initial portion of each sorted file into individual arrays
Start merging the arrays
Whenever an array becomes empty, read in more data from its file
Every so often, write the destination array to the (one) final output file
When you are done, you will have one (large) sorted
file
16
17. 17
Summary
Most of the sorting techniques we have discussed are O(n2
)
As we will see later, we can do much better than this with somewhat
more complicated sorting algorithms
Within O(n2
),
Bubble sort is very slow, and should probably never be used for anything
Selection sort is intermediate in speed
Insertion sort is usually faster than selection sort—in fact, for small arrays
(say, 10 or 20 elements), insertion sort is faster than more complicated
sorting algorithms
Merge sort, if done in memory, is O(n log n)
Selection sort and insertion sort are “good enough” for small arrays
Merge sort is good for sorting data that doesn’t fit in main memory