Karatsuba Multiplication: Difference between revisions

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* [[Algorithms#q23wLp|Algorithms | Divide and Conquer]]
* [[Algorithms#q23wLp|Algorithms | Divide and Conquer]]
=Time Complexity=
=Time Complexity=
In absence of Gauss optimization, the naive recursive algorithm makes 4 recursive calls (a=4), each call on half of the problem (b=2). Upon the exit from recursion, the combine phase  performs additions using a number of operations proportional to the size of the current problem, so the combine phase is O(n<sup>1</sup>) (d=1). a/b<sup>d</sup> = 4/2<sup>1</sup>, so we are in [[Master_Method#Case_3|Case 3]] of the master theorem.
In absence of Gauss optimization, the naive recursive algorithm makes 4 recursive calls (a=4), each call on half of the problem (b=2). Upon the exit from recursion, the combine phase  performs additions using a number of operations proportional to the size of the current problem, so the combine phase is O(n<sup>1</sup>) (d=1). a/b<sup>d</sup> = 4/2<sup>1</sup> = 2, so we are in [[Master_Method#Case_3|Case 3]] of the master theorem, and the running time is upper bound by O(n<sup>log<sub>b</sub>4</sup>)=O(n<sup>2</sub>), not better than the straightforward iterative algoritm.


=TODO=
=TODO=

Revision as of 18:38, 21 September 2021

Internal

Time Complexity

In absence of Gauss optimization, the naive recursive algorithm makes 4 recursive calls (a=4), each call on half of the problem (b=2). Upon the exit from recursion, the combine phase performs additions using a number of operations proportional to the size of the current problem, so the combine phase is O(n1) (d=1). a/bd = 4/21 = 2, so we are in Case 3 of the master theorem, and the running time is upper bound by O(nlogb4)=O(n2), not better than the straightforward iterative algoritm.

TODO

  • problem statement
  • naïve solution
  • Gauss trick
  • complexity
  • Karatsuba: explain the key idea – apply the master theorem to demonstrate that the complexity is still O(n2). The key insight for Karatsuba algorithm is the Gauss’ trick to reduce four sub-parts recursive multiplications to 3, and the complexity is ?

Link to Strassen.

Overview

Apply the Gauss' trick and end up with three recursive calls instead of four. This yields a O(n*logn) complexity. It if was four, the recursive complexity it would have been O(n2).


TODO

Playground

https://github.com/ovidiuf/playground/tree/master/learning/stanford-algorithms-specialization/01-karatsuba
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