06-sparsedynprog - Algorithms Lecture 6: Advanced Dynamic...

Info iconThis preview shows pages 1–2. Sign up to view the full content.

View Full Document Right Arrow Icon

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
This is the end of the preview. Sign up to access the rest of the document.

Unformatted text preview: Algorithms Lecture 6: Advanced Dynamic Programming Tricks [ Fa10 ] Ninety percent of science fiction is crud. But then, ninety percent of everything is crud, and its the ten percent that isnt crud that is important. [Theodore] Sturgeons Law (1953) 6 Advanced Dynamic Programming Tricks ? Dynamic programming is a powerful technique for efficiently solving recursive problems, but its hardly the end of the story. In many cases, once we have a basic dynamic programming algorithm in place, we can make further improvements to bring down the running time or the space usage. We saw one example in the Fibonacci number algorithm. Buried inside the nave iterative Fibonacci algorithm is a recursive problemcomputing a power of a matrixthat can be solved more efficiently by dynamic programming techniquesin this case, repeated squaring. 6.1 Saving Space: Divide and Conquer Just as we did for the Fibonacci recurrence, we can reduce the space complexity of our edit distance algorithm from O ( mn ) to O ( m + n ) by only storing the current and previous rows of the memoization table. This sliding window technique provides an easy space improvement for most (but not all) dynamic programming algorithm. Unfortunately, this technique seems to be useful only if we are interested in the cost of the optimal edit sequence, not if we want the optimal edit sequence itself. By throwing away most of the table, we apparently lose the ability to walk backward through the table to recover the optimal sequence. Fortunately for memory-misers, in 1975 Dan Hirshberg discovered a simple divide-and-conquer strategy that allows us to compute the optimal edit sequence in O ( mn ) time, using just O ( m + n ) space. The trick is to record not just the edit distance for each pair of prefixes, but also a single position in the middle of the optimal editing sequence for that prefix. Specifically, any optimal editing sequence that transforms A [ 1 .. m ] into B [ 1 .. n ] can be split into two smaller editing sequences, one transforming A [ 1 .. m / 2 ] into B [ 1 .. h ] for some integer h , the other transforming A [ m / 2 + 1 .. m ] into B [ h + 1 .. n ] . To compute this breakpoint h , we define a second function Half ( i , j ) such that some optimal edit se- quence from A [ 1.. i ] into B [ 1.. j ] contains an optimal edit sequence from A [ 1.. m / 2 ] to B [ 1.. Half ( i , j )] . We can define this function recursively as follows: Half ( i , j ) = if i < m / 2 j if i = m / 2 Half ( i- 1, j ) if i > m / 2 and Edit ( i , j ) = Edit ( i- 1, j ) + 1 Half ( i , j- 1 ) if i > m / 2 and Edit ( i , j ) = Edit ( i , j- 1 ) + 1 Half ( i- 1, j- 1 ) otherwise (Because there there may be more than one optimal edit sequence, this is not the only correct definition.) A simple inductive argument implies that Half ( m , n ) is indeed the correct value of h . We can easily modify our earlier algorithm so that it computes Half...
View Full Document

Page1 / 6

06-sparsedynprog - Algorithms Lecture 6: Advanced Dynamic...

This preview shows document pages 1 - 2. Sign up to view the full document.

View Full Document Right Arrow Icon
Ask a homework question - tutors are online