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Algorithms Lecture 6 Advanced Dynamic Programming Fa 13 It is a very sad thing that nowadays there is so little useless information Oscar Wilde A Few Maxims for the Instruction Of The Over Educated 1894 Ninety percent of science fiction is crud But then ninety percent of everything is crud and it s the ten percent that isn t crud that is important Theodore Sturgeon s Law 1953 6 Advanced Dynamic Programming Dynamic programming is a powerful technique for efficiently solving recursive problems but it s 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 na ve iterative Fibonacci algorithm is a recursive problem computing a power of a matrix that can be solved more efficiently by dynamic programming techniques in 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 sequence 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 if i m 2 if i m 2 j Half i j Half i 1 j if i m 2 and Edit i j Edit i 1 j 1 if i m 2 and Edit i j Edit i j 1 1 Half i j 1 Half i 1 j 1 otherwise Because there there may be more than

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