Algorithms

Algorithms

Sorting

Given: compare(a,b) to compare 2 numbers. Goal: Sort a list of numbers from smallest to largest.

\(\displaystyle O(n^2)\) Algorithms

Insertion Sort

Basic Idea:

• Maintain a subarray which is always sorted

Algorithm:

• Grab a number from the unsorted portion and insert it into the sorted portion
• Repeat until no more numbers are in the unsorted subarray

Selection Sort

Basic Idea:

• Maintain a subarray which is always sorted.
• Every number in the sorted subarray will be smaller than the smallest number in the unsorted subarray.

Algorithm:

• Find the smallest number in the unsorted portion and insert it into the sorted portion
• Repeat until no more numbers are in the unsorted subarray.

Quicksort

Basic Idea:

• Divide an conquer.
• Very fast. Average case O(nlogn).
• Good for multithreaded systems.

Algorithm:

Selection

Given: compare(a,b) to compare 2 numbers. A number k. Goal: Return the k'th largest number.

Median finding

Reference

• See CLRS
• Idea: Reinterpret your data as a 2D array of size 5 x (n/5)
• Find the median of each column of 5 elements
• Sort the columns by their medians
• Now you can eliminate the upper left (1/4) of elements and the lower right (1/4) of elements
• Recursively iterate on the remaining (1/2) of elements
• Each iteration takes O(n). Consecutive iterations are on n/2 data so we have \(\displaystyle O(n) + O(n/2) + ... = O(n)\)
• Worst Case \(\displaystyle O(n)\)

Quickselect

• Worst Case \(\displaystyle O(n^2)\)
• Average Case \(\displaystyle O(n)\)

Notes

• Using a good \(\displaystyle O(n)\) pivot finding algorithm (See finding the median in O(n)) will reduce the worst case to \(\displaystyle O(n)\)

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