Efficient MATLAB Computations with Sparse and Factored Tensors

Hardware Acceleration for Finite-Element

Hardware Acceleration for Finite-Element Electromagnetics: Efficient Sparse Matrix Floating-Point Computations With FPGAs

✍ El-Kurdi, Y.; Giannacopoulos, D.; Gross, W.J. 📂 Article 📅 2007 🏛 IEEE 🌐 English ⚖ 209 KB

Mathematics for Business, Science, and T

Mathematics for Business, Science, and Technology: With MATLAB and Excel Computations

✍ Steven Karris 📂 Library 📅 2007 🏛 Orchard Publications 🌐 English ⚖ 6 MB

This text includes the following chapters and appendices: Numbers and Arithmetic Operations, Elementary Algebra, Intermediate Algebra, Fundamentals of Geometry, Fundamentals of Plane Trigonometry, Fundamentals of Calculus, Mathematics of Finance and Economics, Depreciation, Impairment, and Depletion

Scientific Computing with MATLAB and Oct

Scientific Computing with MATLAB and Octave

✍ Alfio Quarteroni, Fausto Saleri, Paola Gervasio (auth.) 📂 Library 📅 2010 🏛 Springer 🌐 English ⚖ 6 MB

This textbook is an introduction to Scientific Computing, in which several numerical methods for the computer-based solution of certain classes of mathematical problems are illustrated. The authors show how to compute the zeros or the integrals of continuous functions, solve linear systems, approxim

Scientific Computing with MATLAB and Oct

Scientific Computing with MATLAB and Octave

✍ Alfio Quarteroni, Fausto Saleri, Paola Gervasio (auth.) 📂 Library 📅 2010 🏛 Springer 🌐 English ⚖ 7 MB

This textbook is an introduction to Scientific Computing, in which several numerical methods for the computer-based solution of certain classes of mathematical problems are illustrated. The authors show how to compute the zeros or the integrals of continuous functions, solve linear systems, approxim

Linear combination of Lanczos vectors: A

Linear combination of Lanczos vectors: A storage-efficient algorithm for sparse matrix eigenvector computations

✍ T. Koslowski; W. Von Niessen 📂 Article 📅 1993 🏛 John Wiley and Sons 🌐 English ⚖ 634 KB

We present a storage-efficient and robust algorithm for the computation of eigenvectors of large sparse symmetrical matrices using a Lanczos scheme. The algorithm is based upon a linear combination of Lanczos vectors (LCLV) with a variable iteration depth. A simple method is given to determine the i

Efficient computation of minimum-varianc

Efficient computation of minimum-variance wave-front reconstructors with sparse matrix techniques

✍ Ellerbroek, Brent L. 📂 Article 📅 2002 🏛 Optical Society of America 🌐 English ⚖ 239 KB