Difference between revisions of "Interview Preparation Articles"

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=== Big O notation ===
 
=== Big O notation ===
 
Big O notation is a mathematical way of representing an approximate time required for an expression to complete by checking for its major dependencies.
 
Big O notation is a mathematical way of representing an approximate time required for an expression to complete by checking for its major dependencies.
For a example, f(n) = n^5 + n
+
For a example, f(n) = n^5 + n.
 
As we go on increasing the value of n the dependency on the second half equation on right-hand side will reduce.
 
As we go on increasing the value of n the dependency on the second half equation on right-hand side will reduce.
 
so, we can also say that f(n) = n^5 or complexity is O(n^5) for n equal to infinity.
 
so, we can also say that f(n) = n^5 or complexity is O(n^5) for n equal to infinity.
 
Note: For comparison of big O complexity for different algorithms, refer following site:  http://bigocheatsheet.com/
 
Note: For comparison of big O complexity for different algorithms, refer following site:  http://bigocheatsheet.com/

Revision as of 11:53, 25 November 2016

Interview preparation requires proficient knowledge of C/C++. This article has just been recently written (July 2013) and will be elaborated soon; the articles in RED are waiting to be written. The hope is that this article will be "one-stop-shop" for most common C/C++ interview questions.


Frequently Asked Topics


C++ Object Oriented Topics


Operating Systems Topics

FreeRTOS Tutorial

FreeRTOS is a real-time OS that has many ports for various different controllers. This is a great system to learn about because it gives you the fundamental knowledge of an operating system while making it incredibly easy to learn the material. Here's a must-read tutorial :

Other OS Topics


Miscellaneous Topics

Bit Fiddling

Others


Sorting Topics

Big O notation

Big O notation is a mathematical way of representing an approximate time required for an expression to complete by checking for its major dependencies. For a example, f(n) = n^5 + n. As we go on increasing the value of n the dependency on the second half equation on right-hand side will reduce. so, we can also say that f(n) = n^5 or complexity is O(n^5) for n equal to infinity. Note: For comparison of big O complexity for different algorithms, refer following site: http://bigocheatsheet.com/