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Look ma' I know my algorithms!
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Lucia Escanellas
October 24, 2014
Programming
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430
Look ma' I know my algorithms!
RubyConf Argentina 2014
Lucia Escanellas
October 24, 2014
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Transcript
Look ma’, I know my algorithms!
Lucia Escanellas raviolicode
Attributions https://flic.kr/p/6DDvQP https://flic.kr/p/qv5Zp https://flic.kr/p/6SaZsP https://flic.kr/p/edauSN https://flic.kr/p/4uNfK8 https://flic.kr/p/o9ggdk https://flic.kr/p/6kfuHz https://flic.kr/p/5kBtbS
Speed Speed
Zen Elegance Elegance
Toolbox
Theory Theory
This example Not so common
FROM >30HS TO 18 S
WHY USE ORDERS? ALGORITHMS ARE POWERFUL AVOID TRAPS IN RUBY
WHY USE ORDERS? ALGORITHMS ARE POWERFUL AVOID TRAPS IN RUBY
WHY USING ORDERS? ALGORITHMS ARE POWERFUL AVOID TRAPS IN RUBY
Let’s have a look at the PROBLEM
Ordered array How many pairs (a,b) where a ≠ b
-100 <= a + b <= 100
Array: [-100, 1, 100]
Array: [-100, 1, 100] (-100, 1), (-100, 100), (1, 100)
Array: [-100, 1, 100] (-100, 1), (-100, 100), (1, 100)
-100 + 1 = 99 YES
Array: [-100, 1, 100] (-100, 1), (-100, 100), (1, 100)
-100 + 100 = 0 YES
Array: [-100, 1, 100] (-100, 1), (-100, 100), (1, 100)
1 + 100 = 101 NO
Array: [-100, 1, 100] (-100, 1), (-100, 100), (1, 100)
Result: 2
First solution Combinations of 2 elements Filter by: -100 <=
a + b <= 100
def count! combinations = @numbers.combination(2).to_a! ! combinations! .map{ |a,b| a
+ b }! .select do |sum|! sum.abs <= THRESHOLD! end.size! end
10K takes 10s BUT 100M takes 30hs
Time to buy a NEW LAPTOP!
Big O notation How WELL an algorithm SCALES as the
DATA involved INCREASES
Calc Array size (length=N) Count elements one by one: O(N)
Calc Array size (length=N) Count elements one by one: O(N)
Length stored in variable: O(1)
Graphical Math Properties Order Mathematical Properties
Remember: f < g => O(f + g) = O(g)
O(K . f) = O(f) O(1) < O(ln N) < O(N) < O(N2) < O(eN)
Ex: Binary Search Find 7 in [1, 2, 3, 4,
5, 6, 7, 8] 1. element in the middle is 5 2. 5 == 7 ? NO 3. 5 < 7 ? YES => Find 7 in [6, 7, 8] Step 1
! Find 7 in [0, 1, 2, 3, 4, 5,
6, 7, 8] 1. element in the middle is 7 2. 7 == 7 ? YES! FOUND IT!! Step 2
Ex: Binary Search Worst case: ceil ( Log2 N )
23 = 8 ONLY 3 steps
Typical examples Access to a Hash O(1) Binary search O(log
N) Sequential search O(N) Traverse a matrix NxN O(N2)
DON’T JUST BELIEVE ME fooplot.com
BUT raviolicode, I’m getting BORED
I WANT CONCURRENCY I WANT CONCURRENCY
wait… was it Concurrency? or Parallelism?
None
None
None
None
None
None
GIL+CPU-bound NO I/O OPERATIONS concurrency = OVERHEAD
NOT what I was expecting
Parallelism... Parallelism
None
What do we REALLY get? O(N2 / cores) = O(N
2 ) jRubyGo Scala
NO Spoilers O(N2) O(N.log(N)) O(N)
THINKING algorithms is as IMPORTANT as ANY OTHER technique
BYE.
Wait! It's still slow. Wait! It’s still SLOW
Given [1,2,3,4,5] Take 1, then print [5,4,3,2] Take 2, then
print [5,4,3] and so on…
What’s the ORDER of this code? @nums.each_with_index do |a,i|! !
puts @nums.slice(i+1,N).reverse! .inspect! end
What’s the ORDER of this code? @nums.each_with_index do |a,i|! !
puts @nums.slice(i+1,N).reverse! .inspect! end Looks like O(N)
What’s the ORDER of this code? @nums.each_with_index do |a,i|! !
puts @nums.slice(i+1,N).reverse! .inspect! end Behaves like O(N2)
Let’s Look at the DOCS Ruby-Doc.org ! #reverse
O(N) hidden! O(N)!
What’s the ORDER of this code? @nums.each_with_index do |a,i|! !
puts @nums.slice(i+1,N).reverse! .inspect! end O(N2)!
Leaky abstractions LEAKY ABSTRACTIONS
All Non-trivial abstractions are LEAKY to some degree
ABSTRACTIONS DO NOT really SIMPLIFY as they were meant to
Knowing THE ALGORITHMS Behind everyday methods PAYS OFF
Thanks :) Thanks :)