Difference between revisions of "Interview Preparation Articles"
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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. | ||
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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. | ||
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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. | ||
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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 11:54, 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.
Contents
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/
