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# 32.4 Summary

## Quicksort

What can we do to avoid the worst case runtime of $$\theta(N^2)$$ for Quicksort? 

1. Randomness: pick a random pivot point, shuffle items before sorting
2. Smarter Pivot Selection: constant time pivot pick (ex: pick a few and then choose the best out of them), linear time pivot pick like (ex: median)
3. Introspection: switch to a different sort if current sort hits recursion depth threshold

Quicksort vs. Mergesort

* Mergesort is faster than Quicksort L3S (leftmost pivot, 3-scan partition) and QuickSort PickTH (median pivot, Tony Hoare partition)
* Quicksort LTHS (leftmost pivot, Tony Hoare partition) is faster than Mergesort!
* Tony Hoare's partitioning:
  * two pointers, one at each end of the items array, walking towards each other
  * left pointer hates larger or equal items, right pointer hates smaller or equal items
  * swapping anything they don’t like; stop when the two pointers cross each other
  * new pivot = Item at right pointer.

## Quick Select

Quick select helps us quickly find the median with partitioning. Median of an length n array will be around index n /2. 

1. Initialize array with the leftmost item as the pivot. 
2. Partition around pivot. 
3. Partition the subproblem. Repeat the process.
4. Stop when the pivot is at the median index. 

Expected runtime: $$\theta(N)$$. Worst case runtime (when array is in sorted order): $$\theta(N^2)$$.

## Stability, Adaptiveness, and Optimization

Stability: A sort is stable if the order of equivalent elements is preserved.

Optimizations: 

* Adaptiveness - sort that exploits the existing order of the array.
* Switch to Insertion Sort - when a subproblem reaches size 15 or lower
* Exploit restrictions on set of keys
* Switch from QuickSort - if the recursion goes too deep