The Challenge is to create faster Find methods for large unique ascending vectors.

Methods exist that are 1000 times faster than Find.

Methods have applications where data files are loaded for multiple searches(Rubik's Mini Cube, DNA).

Input: [a,val]

• a: vector [N large,1] of type uint32 that is unique and sorted ascending
• val: The value to find in the "a" vector. Val will exist in "a".

Output: [ptr]

• ptr: The index in array "a" where a(ptr) is val

Score: Time in msec to find 200 random values in a 12,000,000 long vector

The basic find(a==val,1,'first') takes approximately 14 seconds (12M, 200 cases).

High performance find_fast can find 200 values in 4 to 12 milli-seconds for a 12M vector.

Find time increases linearly with the array size. High performance methods don't appear to incur any increased processing time.

Hints:

• There are multiple methods that significantly outperform find.
• Bisect searches
• Predictive searches
• Combining Bisect/Predictive with a Linear Chaser

A static goal vector find time could be enhanced by a Pre-Index array. Not applicable to this challenge.