COMP36111 - Iceweasel. This course is officially announced in the syllabus . Timetable news: There will be NO lectures in the week 18th-22nd October (to allow time to work on the exercise sheets). The course resumes on Tuesday 26th October (Reading week is 1st-5th November). An introductory course on algorithms and data structures, including understanding basic complexity measures, e.g. the course COMP26120 .
This unit provides an advanced course in algorithms, assuming the student already knows algorithms for common computational tasks, and can reason about the correctness of algorithms and understand the basics of computing the complexity of algorithms and comparing algorithmic performance. The course focuses on the range of algorithms available for computational tasks, considering the fundamental division of tractable tasks, with linear or polynomial-time algorithms, and tasks that appear to be intractable, in that the only algorithms available are exponential-time in the worst case.
Part 1: (1 lecture) Part 2: ESMAJ - Iceweasel. A Fast String Scanning Algorithm with Small Startup Overhead - Iceweasel. Algorithm for Incremental String Search - Iceweasel. I gave a talk on profiling python code at the 2012 Utah Open Source Conference. Here are the slides and the accompanying code. There are three parts to this profiling talk:Standard Lib Tools - cProfile, PstatsThird Party Tools - line_profiler, mem_profilerCommercial Tools - New Relic This is Part 1 of that talk.
It covers:cProfile module - usagePstats module - usageRunSnakeRun - GUI viewerWhy Profiling:Identify the bottle-necks.Optimize intelligently. In God we trust, everyone else bring data cProfile is a profiling module that is included in the Python's standard library. Basic Usage: The sample code I'm profiling is finding the lowest common multiplier of two numbers. lcm.py # lcm.py - ver1 def lcm(arg1, arg2): i = max(arg1, arg2) while i < (arg1 * arg2): if i % min(arg1,arg2) == 0: return i i += max(arg1,arg2) return(arg1 * arg2) lcm(21498497, 3890120) Let's run the profiler. $ python -m cProfile -o shorten.prof shorten.py # saves the output to shorten.prof $ ls shorten.py shorten.prof.
String searching algorithm - Wikipedia, the free encyclopedia - Iceweasel. In practice, how the string is encoded can affect the feasible string search algorithms. In particular if a variable width encoding is in use then it is slow (time proportional to N) to find the Nth character. This will significantly slow down many of the more advanced search algorithms. A possible solution is to search for the sequence of code units instead, but doing so may produce false matches unless the encoding is specifically designed to avoid it.
Basic classification[edit] The various algorithms can be classified by the number of patterns each uses. Single pattern algorithms[edit] Let m be the length of the pattern and let n be the length of the searchable text. 1Asymptotic times are expressed using O, Ω, and Θ notation The Boyer–Moore string search algorithm has been the standard benchmark for the practical string search literature.[1] Algorithms using a finite set of patterns[edit] Algorithms using an infinite number of patterns[edit] Other classification[edit] Stubs[edit] time, and all. Approximate string matching - Wikipedia, the free encyclopedia - Iceweasel. Fuzzy Mediawiki search for "angry emoticon": "Did you mean: andré emotions" Overview[edit] The closeness of a match is measured in terms of the number of primitive operations necessary to convert the string into an exact match.
This number is called the edit distance between the string and the pattern. The usual primitive operations are:[1] insertion: cot → coatdeletion: coat → cotsubstitution: coat → cost These three operations may be generalized as forms of substitution by adding a NULL character (here symbolized by *) wherever a character has been deleted or inserted: insertion: co*t → coatdeletion: coat → co*tsubstitution: coat → cost Some approximate matchers also treat transposition, in which the positions of two letters in the string are swapped, to be a primitive operation. Different approximate matchers impose different constraints. Problem formulation and algorithms[edit] One possible definition of the approximate string matching problem is the following: Given a pattern string.
Salmela: Improved Algorithms for String Searching Problems, ISBN 978-951-22-9888-4 - Iceweasel. Dissertation for the degree of Doctor of Science in Technology to be presented with due permission of the Faculty of Information and Natural Sciences for public examination and debate in Auditorium T2 at Helsinki University of Technology (Espoo, Finland) on the 1st of June, 2009, at 12 noon. Dissertation in PDF format (ISBN 978-951-22-9888-4) [1023 KB] Dissertation is also available in print (ISBN 978-951-22-9887-7) Abstract We present improved practically efficient algorithms for several string searching problems, where we search for a short string called the pattern in a longer string called the text.
We are mainly interested in the online problem, where the text is not preprocessed, but we also present a light indexing approach to speed up exact searching of a single pattern. In addition to exact string matching, we develop algorithms for several other variations of the string matching problem. We also propose an alphabet sampling technique to speed up exact string matching. Text Algorithms - Iceweasel. Preface The design of algorithms that process strings and texts goes back at least twenty five years. In particular, the last ten of those years have produced an explosion of new results. This progress is due in part to the human genome effort, to which string algorithms can make an important contribution.
While text algorithms can be viewed as part of the general field of algorithmic research, it has developed into a respectable subfield on its own. One measure of the vibrance of this new subfield is the ongoing success of a conference devoted to its study. Following the remarkable progress in this new field, Maxime Crochemore and Wojciech Rytter embarked on the right project at the right time---writing a textbook on text algorithms. About ten years ago, in a workshop that preceded the conference (called Combinatorial Algorithms on Words) I gave a lecture entitled ``Open Problems in Stringology'' [Ga85a]. Zvi Galil Acknowledgements Maxime CrochemoreWojciech Rytter. Algorithmique du texte - Iceweasel. Présentation Il s'agit du premier ouvrage en Français d'algorithmique spécialisée sur le traitement du texte. Il suppose une connaissance de base des méthodes de conception de programmes et d'évaluation de leurs performances.
Il peut être utilisé dans un cours d'algorithmique classique. Chaque chapitre est assorti des références bibliographiques principales et d'une liste d'exercices. Il présente les bases techniques utilisées dans les domaines de la recherche documentaire, de l'indexation pour les moteurs de recherche et des logiciels systèmes. Le livre s'adresse aux étudiants des seconds et troisièmes cycles universitaires d'informatique, à ceux des classes préparatoires aux grandes écoles, et aux élèves-ingénieurs en informatique.