Introduction to Algorithms
01 Jan 2005-
About: The article was published on 2005-01-01 and is currently open access. It has received 19250 citations till now. The article focuses on the topics: Internal sort.
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20 Apr 2009
TL;DR: This beginning graduate textbook describes both recent achievements and classical results of computational complexity theory and can be used as a reference for self-study for anyone interested in complexity.
Abstract: This beginning graduate textbook describes both recent achievements and classical results of computational complexity theory. Requiring essentially no background apart from mathematical maturity, the book can be used as a reference for self-study for anyone interested in complexity, including physicists, mathematicians, and other scientists, as well as a textbook for a variety of courses and seminars. More than 300 exercises are included with a selected hint set.
2,965 citations
TL;DR: This extended abstract describes a recent algorithm, called, CoSaMP, that accomplishes the data recovery task and was the first known method to offer near-optimal guarantees on resource usage.
Abstract: Compressive sampling (CoSa) is a new paradigm for developing data sampling technologies It is based on the principle that many types of vector-space data are compressible, which is a term of art in mathematical signal processing The key ideas are that randomized dimension reduction preserves the information in a compressible signal and that it is possible to develop hardware devices that implement this dimension reduction efficiently The main computational challenge in CoSa is to reconstruct a compressible signal from the reduced representation acquired by the sampling device This extended abstract describes a recent algorithm, called, CoSaMP, that accomplishes the data recovery task It was the first known method to offer near-optimal guarantees on resource usage
2,928 citations
TL;DR: This work proposes a new k-mer counting algorithm and associated implementation, called Jellyfish, which is fast and memory efficient, based on a multithreaded, lock-free hash table optimized for counting k-mers up to 31 bases in length.
Abstract: Motivation: Counting the number of occurrences of every k-mer (substring of length k) in a long string is a central subproblem in many applications, including genome assembly, error correction of sequencing reads, fast multiple sequence alignment and repeat detection. Recently, the deep sequence coverage generated by next-generation sequencing technologies has caused the amount of sequence to be processed during a genome project to grow rapidly, and has rendered current k-mer counting tools too slow and memory intensive. At the same time, large multicore computers have become commonplace in research facilities allowing for a new parallel computational paradigm.
Results: We propose a new k-mer counting algorithm and associated implementation, called Jellyfish, which is fast and memory efficient. It is based on a multithreaded, lock-free hash table optimized for counting k-mers up to 31 bases in length. Due to their flexibility, suffix arrays have been the data structure of choice for solving many string problems. For the task of k-mer counting, important in many biological applications, Jellyfish offers a much faster and more memory-efficient solution.
Availability: The Jellyfish software is written in C++ and is GPL licensed. It is available for download at http://www.cbcb.umd.edu/software/jellyfish.
Contact: [email protected]
Supplementary information:Supplementary data are available at Bioinformatics online.
2,779 citations
TL;DR: A computationally efficient framework for part-based modeling and recognition of objects, motivated by the pictorial structure models introduced by Fischler and Elschlager, that allows for qualitative descriptions of visual appearance and is suitable for generic recognition problems.
Abstract: In this paper we present a computationally efficient framework for part-based modeling and recognition of objects. Our work is motivated by the pictorial structure models introduced by Fischler and Elschlager. The basic idea is to represent an object by a collection of parts arranged in a deformable configuration. The appearance of each part is modeled separately, and the deformable configuration is represented by spring-like connections between pairs of parts. These models allow for qualitative descriptions of visual appearance, and are suitable for generic recognition problems. We address the problem of using pictorial structure models to find instances of an object in an image as well as the problem of learning an object model from training examples, presenting efficient algorithms in both cases. We demonstrate the techniques by learning models that represent faces and human bodies and using the resulting models to locate the corresponding objects in novel images.
2,514 citations
TL;DR: This paper describes Meep, a popular free implementation of the finite-difference time-domain (FDTD) method for simulating electromagnetism, and focuses on aspects of implementing a full-featured FDTD package that go beyond standard textbook descriptions of the algorithm.
2,489 citations