Introduction to Mathematical Programming: Applications and Algorithms (Non-InfoTrac Version with CD-ROM)
Focusing on deterministic models, this book is designed for the first half of an operations research course. A subset of Winston’s best-selling Operations Research, Introduction to Mathematical Programing offers self-contained chapters that make it flexible enough for one- or two-semester courses ranging from advanced beginning to intermediate in level. Appropriate for undergraduate majors, MBAs, and graduate students, it emphasizes model-formulations and model-building skills as well as interpretation of computer software output. LINDO, GINO, and LINGO software packages are available with the book in Windows, Macintosh, or DOS versions. Linear algebra prerequisite.
Bring your data to life and add meaning to your information with Maps Made Easy Using SAS. Abundant real-world examples and a tutorial approach help new users create maps easily and quickly. You will learn the basic mapping components, including map and response data sets as well as simple SAS/GRAPH statements. With in-depth examples you will move on to more advanced mapping techniques, such as annotating maps and producing customized maps and output. The process used to annotate maps is demystified and described in clear, easy-to-follow steps. You will produce data-driven, updatable maps in GIF format for use in Web-based presentations and other applications. Also presented are details on creating more complicated choropleth maps. These include maps that combine geographic areas with internal boundaries removed, maps that display multiple geographic areas, and clipped maps. Enhance your data presentations with this well-organized guide.
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“The book’s focus on imaging problems is very unique among the competing books on inverse and ill-posed problems. …It gives a nice introduction into the MATLAB world of images and deblurring problems.”
— Martin Hanke, Professor, Institut für Mathematik, Johannes-Gutenberg-Universität. When we use a camera, we want the recorded image to be a faithful representation of the scene that we see, but every image is more or less blurry. In image deblurring, the goal is to recover the original, sharp image by using a mathematical model of the blurring process. The key issue is that some information on the lost details is indeed present in the blurred image, but this “hidden” information can be recovered only if we know the details of the blurring process. Deblurring Images: Matrices, Spectra, and Filtering describes the deblurring algorithms and techniques collectively known as spectral filtering methods, in which the singular value decomposition—or a similar decomposition with spectral properties—is used to introduce the necessary regularization or filtering in the reconstructed image. The concise MATLAB® implementations described in the book provide a template of techniques that can be used to restore blurred images from many applications. This book’s treatment of image deblurring is unique in two ways: it includes algorithmic and implementation details; and by keeping the formulations in terms of matrices, vectors, and matrix computations, it makes the material accessible to a wide range of readers. Students and researchers in engineering will gain an understanding of the linear algebra behind filtering methods, while readers in applied mathematics, numerical analysis, and computational science will be exposed to modern techniques to solve realistic large-scale problems in image processing. With a focus on practical and efficient algorithms, Deblurring Images: Matrices, Spectra, and Filtering includes many examples, sample image data, and MATLAB codes that allow readers to experiment with the algorithms. It also incorporates introductory material, such as how to manipulate images within the MATLAB environment, making it a stand-alone text. Pointers to the literature are given for techniques not covered in the book. Audience
This book is intended for beginners in the field of image restoration and regularization. Readers should be familiar with basic concepts of linear algebra and matrix computations, including the singular value decomposition and orthogonal transformations. A background in signal processing and a familiarity with regularization methods or with ill-posed problems are not needed. For readers who already have this knowledge, this book gives a new and practical perspective on the use of regularization methods to solve real problems. Preface; How to Get the Software; List of Symbols; Chapter 1: The Image Deblurring Problem; Chapter 2: Manipulating Images in MATLAB; Chapter 3: The Blurring Function; Chapter 4: Structured Matrix Computations; Chapter 5: SVD and Spectral Analysis; Chapter 6: Regularization by Spectral Filtering; Chapter 7: Color Images, Smoothing Norms, and Other Topics; Appendix: MATLAB Functions; Bibliography; Index.
Algorithms in Bioinformatics: 5th International Workshop, WABI 2005, Mallorca, Spain, October 3-6, 2005, Proceedings (Lecture Notes in Computer Science / Lecture Notes in Bioinformatics)
This book constitutes the refereed proceedings of the 5th International Workshop on Algorithms in Bioinformatics, WABI 2005, held in Mallorca, Spain, in September 2005 as part of the ALGO 2005 conference meetings. The 34 revised full papers presented were carefully reviewed and selected from 95 submissions. All current issues of algorithms in bioinformatics are addressed with special focus on statistical and probabilistic algorithms in the field of molecular and structural biology. The papers are organized in topical sections on expression (hybrid methods and time patterns), phylogeny (quartets, tree reconciliation, clades and haplotypes), networks, genome rearrangements (transposition model and other models), sequences (strings, multi-alignment and clustering, clustering and representation), and structure (threading and folding).