# Intel® Math Kernel Library
Cookbook

- Matrix recipes using Intel MKL PARDISO, BLAS, Sparse BLAS, and LAPACK routines
- Finding an approximate solution to a nonlinear equation demonstrates a method of finding a solution to a nonlinear equation using Intel MKL PARDISO, BLAS, and Sparse BLAS routines.
- Factoring a block tridiagonal matrix uses Intel MKL implementations of BLAS and LAPACK routines.
- Solving a system of linear equations with an LU-factored block tridiagonal coefficient matrix extends the factoring recipe to solving a system of equations.
- Factoring block tridiagonal symmetric positive definite matrices using BLAS and LAPACK routines demonstrates Cholesky factorization of a symmetric positive definite block tridiagonal matrix using BLAS and LAPACK routines.
- Solving a system of linear equations with block tridiagonal symmetric positive definite coefficient matrix extends the factoring recipe to solving a system of equations using BLAS and LAPACK routines.
- Computing principal angles between two subspaces uses LAPACK SVD to calculate the principal angles.
- Computing principal angles between invariant subspaces of block triangular matrices extends the use of LAPACK SVD to the case where the subspaces are invariant subspaces of a block triangular matrix and are complementary to each other.

- Fast Fourier Transform recipes
- Evaluating a Fourier Integral uses Intel MKL Fast Fourier Transform (FFT) interface to evaluate a continuous Fourier transform integral.
- Using Fast Fourier Transforms for computer tomography image reconstruction uses Intel MKL FFT interface to reconstruct an image from computer tomography data.

- Numerics recipes
- Noise filtering in financial market data streams uses Intel MKL summary statistics routines for computing a correlation matrix for streaming data.
- Using the Monte Carlo method for simulating European options pricing computes call and put European option prices with an Intel MKL basic random number generator (BRNG).
- Using the Black-Scholes formula for European options pricing speeds up Black-Scholes computation of European options pricing with Intel MKL vector math functions.
- Multiple simple random sampling without replacement generates
simple random length-Ksamples without replacement from a population of sizeMfor a largeN.K - Using a histospline technique to scale images uses Intel MKL data fitting functions for image scaling and spline interpolation for histospline computation.

- Recipes for using Intel MKL in different programming environments
- Speeding up Python* scientific computations demonstrates a performance boost of Python code by building NumPy* and SciPy* sources with Intel MKL and enabling Intel MKL Automatic Offload.