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Computer Education for all provides complete lectures series on Data Structure and Applications which covers Introduction to Data Structure and its Types including all Steps involves in Data Structures:-
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Computer Education For all

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Gate Lectures by Ravindrababu Ravula

Introduction to big-O notation.
Sources:
1/ Algorithms by Dasgupta, Papadimitriou & Vazirani [https://code.google.com/p/eclipselu/downloads/detail?name=algorithms.pdf]
2/ http://pages.cs.wisc.edu/~paton/readings/Complexity/#bigO
3/ http://bigocheatsheet.com/
LinkedIn: https://www.linkedin.com/in/michael-sambol-076471ba

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Michael Sambol

In this video big-oh, big-omega and theta are discussed

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Gate Lectures by Ravindrababu Ravula

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Gate Lectures by Ravindrababu Ravula

An algorithm analysis example:
What is the running time of the following code ?
Easy Algorithm Analysis Tutorial:
https://www.udemy.com/algorithm-analysis/
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►Videos:
Solve Big O: https://youtu.be/JVuHSouGKhk
Solve Theta: https://youtu.be/Vzqaz4MDGvc
Solve Big Omega: https://youtu.be/P2qHss2-aSQ
Summations and Loops (Algorithm Analysis): https://youtu.be/sKB1gj1NQi0
►Recurrence Relation Made Easy: https://www.udemy.com/recurrence-relation-made-easy/learn/v4/overview
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►Summation Formulas:
http://everythingcomputerscience.com/discrete_mathematics/Summations.html
http://polysum.tripod.com/
https://en.wikipedia.org/wiki/Summation
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►RESOURCES:
http://www.cs.cmu.edu/~rweba/algf09/solverecurrencesSF.pdf
Helpful Books:
Algorithm Analysis Books:
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Discrete Mathematics Workbooks:
►(1) Practice Problems In Discrete Mathematics - https://www.amazon.com/gp/product/0130458031/ref=as_li_tl?ie=UTF8&tag=everythingc06-20&camp=1789&creative=9325&linkCode=as2&creativeASIN=0130458031&linkId=5ec571a3f11c8356c4a977dd95945e21
►(2)Discrete Mathematics Workbook - https://www.amazon.com/gp/product/0130463272/ref=as_li_tl?ie=UTF8&tag=everythingc06-20&camp=1789&creative=9325&linkCode=as2&creativeASIN=0130463272&linkId=722a147e4912843adb18019b7a08a7e4

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randerson112358

Space and Time Complexity of an Algorithm
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Lecture By: Mr. Arnab Chakraborty, Tutorials Point India Private Limited.

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Tutorials Point (India) Pvt. Ltd.

Analysis and Design of Algorithms By Prof. Sibi Shaji, Dept. of Computer Science, Garden City College, Bangalore

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Garden City University

Reliability Design
System Design
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Suitable for Academics and Industry

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Abdul Bari

Description

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Gate Lectures by Ravindrababu Ravula

Time complexity, Running Time analysis of Algorithms, Asymptotic Time complexity, GATE exam preparation videos for computer science, expression for running time of a Program or algorithm as a function of input size. We define a hypothetical model machine where our algorithm may execute and try to calculate the growth function for running time. Then we classify these functions into broad sets using big-oh or other asymptotic notations.
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Gate Instructors

This video lecture is produced by S. Saurabh. He is B.Tech from IIT and MS from USA.
hashing in data structure
hash table
hash function
hashing in dbms
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To learn C programming from IITian S.Saurabh visit
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saurabhschool

Time Complexity Analysis is a basic function that every computer science student should know about. This fundamental concept is often used to define the usefulness of algorithms.
Complexity analysis of an algorithm is defined as the rate at which an algorithm needs resources to complete as a function of its input. Complexity analysis is extensively used to compare and analyze algorithms and data structures.
Algorithms are often expressed in terms of order complexity (like O(n), O(n^2), etc...). The term with the largest degree is used to predict how much time is required for an algorithm to complete processing.
Time complexity is a function of time needed for an algorithm to complete as it's input changes, and space complexity is the space required for an algorithm as a function of it's input.
References:
https://www.hackerearth.com/practice/basic-programming/complexity-analysis/time-and-space-complexity/tutorial/
https://ocw.mit.edu/courses/civil-and-environmental-engineering/1-204-computer-algorithms-in-systems-engineering-spring-2010/lecture-notes/MIT1_204S10_lec05.pdf
https://www.cs.utexas.edu/users/djimenez/utsa/cs1723/lecture2.html
https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-006-introduction-to-algorithms-fall-2011/lecture-videos/lecture-23-computational-complexity/
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Gaurav Sen

Recursion tree method for solving recurrences running time example
An algorithm analysis example:
What is the running time of the following code ?
Easy Algorithm Analysis Tutorial:
https://www.udemy.com/algorithm-analysis/
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►Videos:
Solve Big O: https://youtu.be/JVuHSouGKhk
Solve Theta: https://youtu.be/Vzqaz4MDGvc
Solve Big Omega: https://youtu.be/P2qHss2-aSQ
►Recurrence Relation Made Easy: https://www.udemy.com/recurrence-relation-made-easy/learn/v4/overview
►Visit my Website:
http://everythingcomputerscience.com/
►Summation Formulas:
http://everythingcomputerscience.com/discrete_mathematics/Summations.html
http://polysum.tripod.com/
https://en.wikipedia.org/wiki/Summation
►Support this channel on Patreon: https://www.patreon.com/randerson112358
►RESOURCES:
https://web.mst.edu/~ercal/253/SLIDES/PDF/Lec-04.pdf
https://www.youtube.com/watch?v=4p1WqREIJq8
https://www.youtube.com/watch?v=sLNPd_nPGIc
Helpful Books:
Algorithm Analysis Books:
►https://www.amazon.com/gp/product/0262033844/ref=as_li_tl?ie=UTF8&tag=randerson112358-20&camp=1789&creative=9325&linkCode=as2&creativeASIN=0262033844&linkId=e0756ab4063fe56fe882b41f61612794
Discrete Mathematics Workbooks:
►(1) Practice Problems In Discrete Mathematics - https://www.amazon.com/gp/product/0130458031/ref=as_li_tl?ie=UTF8&tag=everythingc06-20&camp=1789&creative=9325&linkCode=as2&creativeASIN=0130458031&linkId=5ec571a3f11c8356c4a977dd95945e21
►(2)Discrete Mathematics Workbook - https://www.amazon.com/gp/product/0130463272/ref=as_li_tl?ie=UTF8&tag=everythingc06-20&camp=1789&creative=9325&linkCode=as2&creativeASIN=0130463272&linkId=722a147e4912843adb18019b7a08a7e4

Views: 39837
randerson112358

Step by step instructions showing how to run Bellman-Ford on a graph.
The theory behind Bellman-Ford: https://www.youtube.com/watch?v=9PHkk0UavIM.
Sources:
1. Algorithms by Dasgupta, Papadimitriou & Vazirani [https://code.google.com/p/eclipselu/downloads/detail?name=algorithms.pdf]
LinkedIn: https://www.linkedin.com/in/michael-sambol-076471ba

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Michael Sambol

Plz like share and subscribe

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University Academy- Formerly-IP University CSE/IT

This video briefly explains time complexity and space complexity using basic counting methods and Big O notation.

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EducationAboutStuff

Quick Sort Algorithm Explained
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Views: 163365
Abdul Bari

Selection sort algorithm analysis
Easy Algorithm Analysis Tutorial:
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►Videos:
Recurrence Relation Proof by Induction: https://youtu.be/t_3ACuzEe_8
Recurrence Relation Run Time by Induction:
https://youtu.be/XWykCejG1Rk
Solve Big O: https://youtu.be/JVuHSouGKhk
Solve Theta: https://youtu.be/Vzqaz4MDGvc
Solve Big Omega: https://youtu.be/P2qHss2-aSQ
►Recurrence Relation Made Easy: https://www.udemy.com/recurrence-relation-made-easy/learn/v4/overview
►Visit my Website:
http://everythingcomputerscience.com/
►Summation Formulas:
http://everythingcomputerscience.com/discrete_mathematics/Summations.html
http://polysum.tripod.com/
https://en.wikipedia.org/wiki/Summation
►Support this channel on Patreon: https://www.patreon.com/randerson112358
►RESOURCES:
http://www.cs.cornell.edu/courses/cs211/2005sp/Lectures/L14-BigO/L14-15-Complexity.4up.pdf
https://math.stackexchange.com/questions/417853/selection-sort-algorithm-analysis
https://www.khanacademy.org/computing/computer-science/algorithms/sorting-algorithms/a/analysis-of-selection-sort
Helpful Books:
Algorithm Analysis Books:
►https://www.amazon.com/gp/product/0262033844/ref=as_li_tl?ie=UTF8&tag=randerson112358-20&camp=1789&creative=9325&linkCode=as2&creativeASIN=0262033844&linkId=e0756ab4063fe56fe882b41f61612794
Discrete Mathematics Workbooks:
►(1) Practice Problems In Discrete Mathematics - https://www.amazon.com/gp/product/0130458031/ref=as_li_tl?ie=UTF8&tag=everythingc06-20&camp=1789&creative=9325&linkCode=as2&creativeASIN=0130458031&linkId=5ec571a3f11c8356c4a977dd95945e21
►(2)Discrete Mathematics Workbook - https://www.amazon.com/gp/product/0130463272/ref=as_li_tl?ie=UTF8&tag=everythingc06-20&camp=1789&creative=9325&linkCode=as2&creativeASIN=0130463272&linkId=722a147e4912843adb18019b7a08a7e4

Views: 957
randerson112358

backtracking general method in daa with example

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Education 4u

what is knapsack problem?
how to apply greedy method
Example problem
Second Object profit/weight=1.66
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Views: 146365
Abdul Bari

Floyd-Warshall All Pairs Shortest Path Problem
Dynamic Programming
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Views: 137529
Abdul Bari

Explanation for the article: http://www.geeksforgeeks.org/greedy-algorithms-set-1-activity-selection-problem/
This video is contributed by Illuminati.

Views: 143776
GeeksforGeeks

This video tutorial explains you insertion sort algorithm and analysis in detail with example.
From this tutorial you can learn how to sort an array using insertion sort, how to write insertion sort algorithm, how to calculate the complexity of insertion sort and how to write a code using c programming.
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Slide Hunt

This is one of the important Graph traversal technique. DFS is based on stack data structure.
Analysis:
The time complexity of DFS using Adjacency list is O(V + E) where V & E are the vertices and edges of the graph respectively.

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Go GATE IIT

P NP NP-Hard NP-Complete
P NP NP-Hard NP-Complete in Hindi Follow:
https://youtu.be/infJ4Q0oAZA
P NP NP-Hard NP-Complete,
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Studies Studio

MIT 6.046J Design and Analysis of Algorithms, Spring 2015
View the complete course: http://ocw.mit.edu/6-046JS15
Instructor: Erik Demaine
In this lecture, Professor Demaine covers different algorithmic solutions for the All-Pairs Shortest Paths problem.
License: Creative Commons BY-NC-SA
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MIT OpenCourseWare

Multistage Graph Problem
Solved using Dynamic Programming
Forward Method
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Views: 65017
Abdul Bari

A sorting algorithm which uses the Heap data structure and is done with the Max-Heapify function. This sorting algorithm can be used for arranging the data into both ascending and descending format.
Please like and subscribe if you want more CS Tutorials! :)

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Mifta Sintaha

Dijkstra's Algorithm for Shortest Path Problem with Example in Hind/English for students of IP University Delhi and Other Universities, Engineering, MCA, BCA, B.Sc, M.Sc Colleges.

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Easy Engineering Classes

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نحن نقدر آرائكم و تعليقاتكم.
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Hossam Magdy Balaha

Take the Full Course of Datawarehouse
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decision tree with solved example
K mean clustering algorithm
Introduction to data mining and architecture
Naive bayes classifier
Apriori Algorithm
Agglomerative clustering algorithmn
KDD in data mining
ETL process
FP TREE Algorithm
Decision tree

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Institute Academy

Prim's algorithm for Minimum Spanning Tree in (Hindi, English) with Example for students of IP University Delhi and Other Universities, Engineering, MCA, BCA, B.Sc, M.Sc Colleges.

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Easy Engineering Classes

QUANTITATIVE METHODS TIME SERIES ANALYSIS

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Adhir Hurjunlal

Given a bag which can only take certain weight W. Given list of items with their weights and price. How do you fill this bag to maximize value of items in the bag.
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https://github.com/mission-peace/interview/blob/master/src/com/interview/dynamic/Knapsack01.java
https://github.com/mission-peace/interview/wiki

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Tushar Roy - Coding Made Simple

Asymptotic Time complexity, Running Time analysis of Algorithms-Asymptotic Time complexity-Part 2-GATE exam preparation videos for computer science, asymptotic analysis of algorithms, algorithm efficiency analysis, how to compute the running time of an algorithm, complexity of algorithm, finding running time of an algorithm,
notes on running time of algorithms, analysing algorithm,
growth rate algorithms
find maximum element in array
Time complexity Analysis of iterative programs
Time complexity of a triple-nested loop
time complexity of algorithms
for loop time complexity examples
time complexity of while loop
for (int i = 1; i less than equal to n; ++i)
for (int j = i; j less than equal to n; ++j)
for (int k = j; k less than equal to n; ++k)
// statement
The statement here is executed exactly n(n+1)(n+2)6 times.
1) O(1): Time complexity of a function (or set of statements) is considered as O(1) if it doesn’t contain loop, recursion and call to any other non-constant time function.
// set of non-recursive and non-loop statements
For example swap() function has O(1) time complexity.
A loop or recursion that runs a constant number of times is also considered as O(1). For example the following loop is O(1).
// Here c is a constant
for (int i = 1; i less than equal to c; i++) {
// some O(1) expressions
}
2) O(n): Time Complexity of a loop is considered as O(n) if the loop variables is incremented / decremented by a constant amount. For example following functions have O(n) time complexity.
// Here c is a positive integer constant
for (int i = 1; i less than equal to n; i += c) {
// some O(1) expressions
}
for (int i = n; i Greater Than 0; i -= c) {
// some O(1) expressions
}
3) O(nc): Time complexity of nested loops is equal to the number of times the innermost statement is executed. For example the following sample loops have O(n2) time complexity
for (int i = 1; i less than equal to n; i += c) {
for (int j = 1; j less than equal to n; j += c) {
// some O(1) expressions
}
}
for (int i = n; i Greater Than 0; i += c) {
for (int j = i+1; j less than equal to n; j += c) {
// some O(1) expressions
}
For example Selection sort and Insertion Sort have O(n2) time complexity.
4) O(Logn) Time Complexity of a loop is considered as O(Logn) if the loop variables is divided / multiplied by a constant amount.
for (int i = 1; i less than equal to n; i *= c) {
// some O(1) expressions
}
for (int i = n; i Greater Than 0; i /= c) {
// some O(1) expressions
}
For example Binary Search(refer iterative implementation) has O(Logn) time complexity.
5) O(LogLogn) Time Complexity of a loop is considered as O(LogLogn) if the loop variables is reduced / increased exponentially by a constant amount.
// Here c is a constant greater than 1
for (int i = 2; i less than equal to n; i = pow(i, c)) {
// some O(1) expressions
}
//Here fun is sqrt or cuberoot or any other constant root
for (int i = n; i Greater Than 0; i = fun(i)) {
// some O(1) expressions
}
How to combine time complexities of consecutive loops?
When there are consecutive loops, we calculate time complexity as sum of time complexities of individual loops.
for (int i = 1; i less than equal to m; i += c) {
// some O(1) expressions
}
for (int i = 1; i less than equal to n; i += c) {
// some O(1) expressions
}
Time complexity of above code is O(m) + O(n) which is O(m+n)
If m == n, the time complexity becomes O(2n) which is O(n).
How to calculate time complexity when there are many if, else statements inside loops?
As discussed here, worst case time complexity is the most useful among best, average and worst. Therefore we need to consider worst case. We evaluate the situation when values in if-else conditions cause maximum number of statements to be executed.
For example consider the linear search function where we consider the case when element is present at the end or not present at all.
When the code is too complex to consider all if-else cases, we can get an upper bound by ignoring if else and other complex control statements.
How to calculate time complexity of recursive functions?
Time complexity of a recursive function can be written as a mathematical recurrence relation. To calculate time complexity, we must know how to solve recurrences.
analysis of algorithms and computational complexity
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analyzing algorithm
big 0 notation

Views: 21515
Gate Instructors

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© 2018 Journal of emerging markets finance

Dr. Ralph-Christian Ohr has been working in several innovation, division and product management functions for international, technology-based companies. His interest is aimed at organizational and personal capabilities for high innovation performance. He authors the Integrative Innovation Blog. The Biggest Mistakes in Managing a Portfolio. The Biggest Mistakes in Financial Planning Series. by Harvey Jacobson, CHFC, MBA, CLU. Investors who have remained consistent with their risk profiles through volatile markets have seen a substantial recovery in their portfolios since March 2009. Those who are truly behind are those who panicked and are now left with the decision of how to recover their losses. They can, but it is a much slower recovery. This article published originally April 13, 2010, Los Angeles Daily News. Managing an agile portfolio. When the right people on the right teams have the right context, they naturally do the right thing. Set the right context.