mathtoolbox

A library of mathematical tools (regression, interpolation, dimensionality reduction, clustering, etc.) written in C++11 and Eigen.

Algorithms

Scattered Data Interpolation and Function Approximation

• rbf-interpolation: Radial basis function (RBF) network
• gaussian-process-regression: Gaussian process regression (GPR)

Dimensionality Reduction and Low-Dimensional Embedding

• classical-mds: Classical multi-dimensional scaling (MDS)

Numerical Optimization

• backtracking-line-search: Backtracking line search
• bfgs: BFGS method
• l-bfgs: Limited-memory BFGS method
• strong-wolfe-conditions-line-search: Strong Wolfe conditions line search

Linear Algebra

• matrix-inversion: Matrix inversion techniques

Utilities

• acquisition-functions: Acquisition functions
• constants: Constants
• kernel-functions: Kernel functions
• probability-distributions: Probability distributions

Dependencies

Main Library

• Eigen http://eigen.tuxfamily.org/ (brew install eigen)

Python Bindings

• pybind11 https://github.com/pybind/pybind11 (included as gitsubmodule)

Examples

• optimization-test-function https://github.com/yuki-koyama/optimization-test-functions (included as gitsubmodule)

Build and Installation

mathtoolbox uses CMake https://cmake.org/ for building source codes. This library can be built, for example, by

git clone https://github.com/yuki-koyama/mathtoolbox.git --recursive
cd mathtoolbox
mkdir build
cd build
cmake ../
make

and optionally it can be installed to the system by

make install

When the CMake parameter MATHTOOLBOX_BUILD_EXAMPLES is set ON, the example applications are also built. (The default setting is OFF.) This is done by

cmake ../ -DMATHTOOLBOX_BUILD_EXAMPLES=ON
make

When the CMake parameter MATHTOOLBOX_PYTHON_BINDINGS is set ON, the example applications are also built. (The default setting is OFF.) This is done by

cmake ../ -DMATHTOOLBOX_PYTHON_BINDINGS=ON
make

Prerequisites

macOS:

brew install eigen

Ubuntu:

sudo apt install libeigen3-dev

Projects Using mathtoolbox

• SelPh https://github.com/yuki-koyama/selph (for classical-mds)
• Sequential Line Search https://github.com/yuki-koyama/sequential-line-search (for acquisition-functions, kernel-functions, and probability-distributions)

Licensing

The MIT License.