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This text details the concept of parametrized matching (p-match) in string processing, defined by three key conditions. It explores algorithms for finding all locations where parameterized matches occur using KMP-like techniques. The discussion includes a bijective relationship, reduction to m-matching, and efficient handling of suffixes through failure links in an automaton framework. The overall time complexity of the given algorithms is addressed, along with the innovative approach of splitting text into overlapping segments for improved efficiency.
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Parametrized MatchingAmir, Farach, Muthukrishnan Orgad Keller
Parametrized Match Relation • Definition: Two strings over the alphabet , parametrized match (p-match) if the following 3 conditions apply : Orgad Keller - Algorithms 2 - Recitation 9
Conditions Orgad Keller - Algorithms 2 - Recitation 9
Example • We can see it as a bijection : Orgad Keller - Algorithms 2 - Recitation 9
Parametrized Matching • Input: • Output: All locations where p-matches . Orgad Keller - Algorithms 2 - Recitation 9
Observation • We can reduce the problem, to the same problem with (m-match). • Given we’ll define : Orgad Keller - Algorithms 2 - Recitation 9
Observation • Now is over and is over and . • We get the algorithm for p-match: • Create • Find all the places appears in (using KMP) • Find all the places m-matches in (We’ll show later how) • Return Orgad Keller - Algorithms 2 - Recitation 9
Exercise • Why is that enough? • In other words: Prove there is a p-match at location iff . • We are left with the question: How do we solve step 3 efficiently? Orgad Keller - Algorithms 2 - Recitation 9
M-match • Is m-match transitive? • We can use KMP-like automaton method • For each index in pattern, we want to find the longest suffix that m-matches the prefix. • For instance: Orgad Keller - Algorithms 2 - Recitation 9
Failure Links • Where to link the failure link from ? • In KMP it is simple: If then link to . Otherwise go back again and repeat. • In our case: • If never appeared before, i.e. We link if . • Otherwise, we link if such that , it holds that . Orgad Keller - Algorithms 2 - Recitation 9
Failure Links • Can we do this efficiently? • We’ll build an array : • So, if , we know hasn’t appeared before. Otherwise, we’ll know exactly where it had appeared last. Orgad Keller - Algorithms 2 - Recitation 9
Building the Array • We’ll hold a Balanced Binary Search Tree for the symbols of the alphabet. Initially it will be empty. • We’ll go over the pattern. For each symbol, if it isn’t in the tree, we’ll add it with it’s index and update . Otherwise, we know exactly where it had last appeared, so we’ll update and then update the symbol in the tree with the new index. • Time: where . Orgad Keller - Algorithms 2 - Recitation 9
The Matching Itself • We go forward in the automaton if either • and . • We’ll hold and update a balanced BST as we go over the text as well. • Time: • So overall algorithm time is • Can we improve this further? Orgad Keller - Algorithms 2 - Recitation 9
The Trick • We’ll split the text into overlapping segments of size like this: • So every match in the text must appear in whole in one of the segments. • We’ll run the algorithm for each such segment. Time: where . • Overall for all segments: Orgad Keller - Algorithms 2 - Recitation 9