Intel® oneAPI Math Kernel Library Cookbook

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Date 6/24/2024
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Document Table of Contents x
Intel® oneAPI Math Kernel Library Cookbook
Intel® oneAPI Math Kernel Library Cookbook x
Notices and Disclaimers Getting Technical Support What's New Notational Conventions Related Information Finding an approximate solution to a stationary nonlinear heat equation Factoring general block tridiagonal matrices Solving a system of linear equations with an LU-factored block tridiagonal coefficient matrix Factoring block tridiagonal symmetric positive definite matrices Solving a system of linear equations with a block tridiagonal symmetric positive definite coefficient matrix Computing principal angles between two subspaces Computing principal angles between invariant subspaces of block triangular matrices Evaluating a Fourier integral Using Fast Fourier Transforms for computer tomography image reconstruction Noise filtering in financial market data streams Using the Monte Carlo method for simulating European options pricing Using the Black-Scholes formula for European options pricing Multiple simple random sampling without replacement Using a histospline technique to scale images Speeding up Python* scientific computations Bibliography
Intel® oneAPI Math Kernel Library Cookbook

Bibliography

Sparse Solvers

[Amos10]

Ron Amos. Lecture 3: Solving Equations Using Fixed Point Iterations, University of Wisconsin CS412: Introduction to Numerical Analysis, 2010 (available at http://pages.cs.wisc.edu/~holzer/cs412/lecture03.pdf).

[Smith86]

G.D. Smith. Numerical Solution of Partial Differential Equations: Finite Difference Methods, Oxford Applied Mathematics & Computing Science Series, Oxford University Press, USA, 1986.

Numerics

[Black73]

Fischer Black and Myron S. Scholes. The Pricing of Options and Corporate Liabilities. Journal of Political Economy, v81 issue 3, 637-654, 1973.

[Kargupta02]

Hillol Kargupta, Krishnamoorthy Sivakumar, and Samiran Ghost. A Random Matrix-Based Approach for Dependency Detection from Data Streams, Proceedings of Principles of Data Mining and Knowledge Discovery, PKDD 2002: 250-262. Springer, New York, 2002.

[KnuthV2]

D. Knuth. The Art of Computer Programming, Volume 2: Seminumerical Algorithms, section 3.4.2: Random Sampling and Shuffling. Algorithm P, 3rd Edition, Addison-Wesley Professional, 2014.

[SRSWOR]

Using Intel® C++ Composer XE for Multiple Simple Random Sampling without Replacement, available at https://community.intel.com/t5/Intel-oneAPI-Math-Kernel-Library/Using-Intel-C-Composer-XE-for-Multiple-Simple-Random-Sampling/m-p/1030226.

[Zhang12]

Zhang Zhang, Andrey Nikolaev, and Victoriya Kardakova. Optimizing Correlation Analysis of Financial Market Data Streams Using Intel® Math Kernel Library, Intel Parallel Universe Magazine, 12: 42-48, 2012.

[Bosner14]

Tina Bosner, Bojan Crnković, and Jerko Škifić. Tension splines with application on image resampling, Mathematical Communications 19 (3), 2014, 517-529.

[Zavyalov80]

Yu. S. Zavyalov, B.I. Kvasov, and V. L. Miroshnichenko. Methods of Spline Functions, Nauka (in Russian), 1980.

Using Intel® oneAPI Math Kernel Library (oneMKL) in different programming environments

[AO]

Intel MKL Automatic Offload enabled functions for Intel® Xeon Phi™ coprocessors.

Level Two Title