I have published code that shows examples of the Linear Search Algorithm.

The linear search algorithm iterates through each item in our data structure in search for a specific value. If the current item matches, we can return, else we must continue to the next item.

In the worse case, this requires that we search through all items because in a unsorted structure, we cannot say whether an untesetd value is the value we are searching for.

Other Blogs that have covered this topic:

Dream.In.Code

Coding Bot

- Learn Duality in Linear Programming (0.750)
- Simple Linear Regression (0.750)
- Examples of the Binary Search Algorithm (0.250)
- The PageRank Algorithm (0.250)
- Permutation Problems (0.250)

It just occurred to me that if you want to elitiname the bias (happening when there is more than one line with the minimal random value) by using the iterative process I described above, you can actually do that in one pass over the file.Instead of keeping 1 line and 1 number (the minimum so far) in memory, keep 1 line and an array of numbers in memory. The length of the array is the maximum number of iterations, it can also be an unbounded linked list. Don’t expect many iterations to be necessary, especially if RAND_MAX is big.So, when random() returns the same value as the minimal value so far, call random() twice again. Store the minimum of the two values in the second item of the array. If the first call returns the smallest of the two values, keep the old line. If the second call returns the smallest of the two values, keep the new one. If both are the same, call random() twice again, and store it in the third value of the array. And so on.For later lines, continue to compare the random() return value with the first item in the array first. When it is smaller, keep that line and drop all the further values in the array.

really beautiful blog. I am also working on different strategies about how we can use social networking sites for

Thanks for sharing this idea interesting blog, Please continue this great work.

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