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1. |
Record Nr. |
UNINA9910817966003321 |
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Titolo |
Domain decomposition methods in scientific and engineering computing : proceedings of the Seventh International Conference on Domain Decomposition, October 27-30, 1993, the Pennsylvania State University / / David E. Keyes, Jinchao Xu, editors |
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Pubbl/distr/stampa |
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Providence, Rhode Island : , : American Mathematical Society, , [1994] |
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©1994 |
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ISBN |
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0-8218-7771-2 |
0-8218-5171-3 |
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Descrizione fisica |
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1 online resource (578 p.) |
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Collana |
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Contemporary mathematics, , 0271-4132 ; ; 180 |
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Disciplina |
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Soggetti |
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Decomposition method |
Differential equations, Partial |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references. |
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Nota di contenuto |
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""Contents""; ""Preface""; ""List of participants""; ""Part I. Theory""; ""Interpolation spaces and optimal multilevel preconditioners""; ""Two-level additive Schwarz preconditioners for nonconforming finite elements""; ""Two proofs of convergence for the combination technique for the efficient solution of sparse grid problems""; ""Domain decomposition rnethods for monotone nonlinear elliptic problems""; ""Cascadic conjugate gradient methods for elliptic partial differential equations: Algorithm and numerical results"" |
""Multilevel methods for elliptic problems with discontinuous coefficients in three dimensions""""Multilevel methods for elliptic problems on domains not resolved by the coarse grid""; ""Three-dimensional domain decomposition methods with nonmatching grids and unstructured coarse solvers""; ""Domain decomposition for elliptic problems with large condition numbers""; ""Stable subspace splittings for Sobolev spaces and domain decomposition algorithms""; ""A wire basket based method for spectral elements in three dimensions"" |
""An analysis of spectral graph partitioning via quadratic assignment problems""""Error estimators based on stable splittings""; ""Multilevel methods for PI nonconforming finite elements and discontinuous |
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coefficients in three dimensions""; ""On generalized Schwarz coupling applied to advection-dominated problems""; ""Exotic coarse spaces for Schwarz methods for lower order and spectral finite elements""; ""Part II. Algorithms""; ""Preconditioning via asymptotically-defined domain decomposition""; ""A spectral Stokes solver in domain decomposition methods"" |
""Preconditioned iterative methods in a subspace for linear algebraic equations with large jumps in the coefficients""""The hierarchical basis multigrid method and incomplete LU decomposition""; ""Domain decomposition and multigrid algorithms for elliptic problems on unstructured meshes""; ""Two-grid methods for mixed finite element approximations of nonlinear parabolic equations""; ""Domain decomposition algorithms for PDE problems with large scale variations""; ""A one shot domain decomposition/fictitious domain method for the Navier-Stokes equations"" |
""Domain-oriented multilevel methods""""Multigrid and domain decomposition methods for electrostatics problems""; ""A parallel subspace decomposition method for hyperbolic equations""; ""Numerical treatments for the Helmholtz problem by domain decomposition techniques""; ""Schwarz methods for obstacle problems with convection-diffusion operators""; ""On domain decomposition and shooting methods for two-point boundary value problems""; ""Robust methods for highly nonsymmetric problems""; ""Domain decomposition via the Sinc-Galerkin method for second order differential equations"" |
""A bisection method to find all solutions of a system of nonlinear equations"" |
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2. |
Record Nr. |
UNINA9910826444103321 |
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Autore |
Idris Ivan |
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Titolo |
Python data analysis : learn how to apply powerful data analysis techniques with popular open source Python modules / / Ivan Idris |
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Pubbl/distr/stampa |
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Birmingham : , : Packt Publishing, , 2014 |
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ISBN |
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Edizione |
[1st edition] |
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Descrizione fisica |
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1 online resource (348 p.) |
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Collana |
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Community experience distilled |
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Disciplina |
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Soggetti |
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Python (Computer program language) |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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Cover; Copyright; Credits; About the Author; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Getting Started with Python Libraries; Software used in this book; Installing software and setup; On Windows; On Linux; On Mac OS X; Building NumPY, SciPy, matplotlib, and IPython from source; Installing with setuptools; NumPy arrays; Simple application; Using IPython as a shell; Reading manual pages; IPython notebooks; Where to find help and references; Summary; Chapter 2: NumPy Arrays; The NumPy array object; The advantages of NumPy arrays |
Creating a multidimensional arraySelecting NumPy array elements; NumPy numerical types; Data type objects; Character codes; The dtype constructors; The dtype attributes; One-dimensional slicing and indexing; Manipulating array shapes; Stacking arrays; Splitting NumPy arrays; NumPy array attributes; Converting arrays; Creating array views and copies; Fancy indexing; Indexing with a list of locations; Indexing NumPy arrays with Booleans; Broadcasting NumPy arrays; Summary; Chapter 3: Statistics and Linear Algebra; NumPy and SciPy modules; Basic descriptive statistics with NumPy |
Linear algebra with NumPyInverting matrices with NumPy; Solving linear systems with NumPy; Finding eigenvalues and eigenvectors with NumPy; NumPy random numbers; Gambling with the binomial distribution; Sampling the normal distribution; Performing a normality test with SciPy; Creating a NumPy-masked array; Disregarding negative and extreme values; Summary; Chapter 4: pandas Primer; Installing and |
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exploring pandas; pandas DataFrames; pandas Series; Querying data in pandas; Statistics with pandas DataFrames; Data aggregation with pandas DataFrames; Concatenating and appending DataFrames |
Joining DataFramesHandling missing values; Dealing with dates; Pivot tables; Remote data access; Summary; Chapter 5: Retrieving, Processing, and Storing Data; Writing CSV files with NumPy and pandas; Comparing the NumPy .npy binary format and pickling pandas DataFrames; Storing data with PyTables; Reading and writing pandas DataFrames to HDF5 stores; Reading and writing to Excel with pandas; Using REST web services and JSON; Reading and writing JSON with pandas; Parsing RSS and Atom feeds; Parsing HTML with BeautifulSoup; Summary; Chapter 6: Data Visualization; matplotlib subpackages |
Basic matplotlib plotsLogarithmic plots; Scatter plots; Legends and annotations; Three-dimensional plots; Plotting in pandas; Lag plots; Autocorrelation plots; Plot.ly; Summary; Chapter 7: Signal Processing and Time Series; statsmodels subpackages; Moving averages; Window functions; Defining cointegration; Autocorrelation; Autoregressive models; ARMA models; Generating periodic signals; Fourier analysis; Spectral analysis; Filtering; Summary; Chapter 8: Working with Databases; Lightweight access with sqlite3; Accessing databases from pandas; SQLAlchemy; Installing and setting up SQLAlchemy |
Populating a database with SQLAlchemy |
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Sommario/riassunto |
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This book is for programmers, scientists, and engineers who have knowledge of the Python language and know the basics of data science. It is for those who wish to learn different data analysis methods using Python and its libraries. This book contains all the basic ingredients you need to become an expert data analyst. |
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