☰

Computers & Technology

Direct Methods for Sparse Linear Systems (Fundamentals of Algorithms, Series Number 2)

R 4,861

or 4 x payments of R1,215.25 with Payflex

Payflex

Availability: Currently in Stock

Delivery: 10-20 working days

Direct Methods for Sparse Linear Systems (Fundamentals of Algorithms, Series Number 2)
- New
- Mint Condition
- Dispatch same day for order received before 12 noon
- Guaranteed packaging
- No quibbles returns
Computational scientists often encounter problems requiring the solution of sparse systems of linear equations. Attacking these problems efficiently requires an in-depth knowledge of the underlying theory, algorithms, and data structures found in sparse matrix software libraries. Here, Davis presents the fundamentals of sparse matrix algorithms to provide the requisite background. The book includes CSparse, a concise downloadable sparse matrix package that illustrates the algorithms and theorems presented in the book and equips readers with the tools necessary to understand larger and more complex software packages. With a strong emphasis on MATLABÂ® and the C programming language, Direct Methods for Sparse Linear Systems equips readers with the working knowledge required to use sparse solver packages and write code to interface applications to those packages. The book also explains how MATLAB performs its sparse matrix computations.
Country USA
Manufacturer Society for Industrial and Applied Mathematics
Binding Paperback
UnitCount 1
Format International Edition
EANs 9780898716139
ReleaseDate 0000-00-00

You might also like...

Iterative Methods for Sparse Linear Systems

Iterative Methods for Sparse Linear Systems

Direct Methods for Sparse Matrices (Numerical Mathematics and Scientific Computation)

Direct Methods for Sparse Matrices (Numerical Mathematics and Scientific Computation)

The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)

The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)

Matrix Computations (Johns Hopkins Studies in the Mathematical Sciences)

Matrix Computations (Johns Hopkins Studies in the Mathematical Sciences)

Convex Optimization

Convex Optimization

Applied Numerical Linear Algebra

Applied Numerical Linear Algebra

Numerical Optimization (Springer Series in Operations Research and Financial Engineering)

Numerical Optimization (Springer Series in Operations Research and Financial Engineering)

Using OpenMP: Portable Shared Memory Parallel Programming (Scientific and Engineering Computation)

Using OpenMP: Portable Shared Memory Parallel Programming (Scientific and Engineering Computation)