Medians (Part IV)

I’ve visited the median problem a couple of times already. The median is the value in the middle of a sorted list of values. If the list is already sorted, that’s not much of a problem. If it isn’t, then we must use some efficient way to do it.

the Dutch Flag Problem

While preparing my lecture notes on sorting, I rediscovered the Dutch flag problem proposed by Edsger W. Dijkstra quite a while ago. This problem is relevant in the context of sorting, especially for variants of Quicksort, where you want to create not two but three partitions.

Like many problems, the Dutch flag problem has a very simple statement. Say you have an array with three types of value, how can you arrange them so that all the items of the first type is at the beginning of the array, the items of the third at the end (and, of course, leaving the second type between the two)?

Medians (Part I)

In a previous installment, about filtering noise, we discussed how to use a moving average to even out irregularities in a sequence. Averaging over a window is tricky. First, the window size must make sense in terms of the speed at which the signal changes (in samples), and the average is (overly) sensitive to outliers.

One way to limit the influence of the outliers for denoising is to use the median. However, computing the median is usually more tricky than computing the average, or mean, and this first post (in a series of three, in the next few weeks), discusses how to compute the median efficiently using the selection algorithm.

Shellsort

The game is different whether we consider external data structures—that live partially or totally on disk or over the network—or internal data structures residing entirely in local memory. For one thing, none of the courses I had on data structures I had really prepared me to deal adequately with (large) external data structures; it was always assumed that the computer’s memory was somehow spacious enough to hold whatever data you needed.

However, when you’re dealing with files, you can’t really assume that you can access random locations to read, write, or exchange data at low cost, and you have to rethink your algorithms (or change plans altogether) to take the cost of slow external media into account. Sometimes an algorithm that doesn’t look all that useful in main memory suddenly makes more sense in external memory because of the way it scans its data. One of these algorithms is the Shellsort.

Foiled!

QuickSort, due to Hoare, is one of the best comparison-based sorting algorithms around. There are a number of variations, but the vanilla QuickSort does a great job… most of the times.

Indeed, there are occasions where QuickSort leaves its expected run-time to reach run-time, its worst case. But what does it take to push QuickSort into its worst behaviour?

Sorting Lists (part II)

Last week I showed you the radix sort on simple linked lists. This week, I will present a version of QuickSort modified to sort simply linked lists.

Sorting Linked Lists (part I)

The sorting algorithms we were taught in class were typically simplified versions of the algorithms that assumed that the data remained in memory and in contiguous memory locations, also known as an array. What we often have, however, are linked lists. Generalizing algorithms like the QuickSort to lists is not very hard in fact if we use a modification of the basic algorithm.

For lists with numerical keys (or values), there might be a simpler algorithm that scans the list in only one direction, so that simply linked lists are a perfectly cromulent data structure, that is, the radix sort.

The 10 (classes of) Algorithms Every Programmer Must Know About

In Tunnels of Doom!, I wrote that the disjoint sets algorithm is one of the very few algorithms every programmer should know. That got me thinking. Should? What about must? If everyone must know about disjoint sets, what other algorithms must every programmer know about?

I made a “top ten” list of algorithms and data structures every programmer must know about.