NOA: Nonlinear Optimisation Algorithms

We aim to make it easier to integrate optimisation with on-line learning, bayesian computation and large simulation frameworks through dedicated differentiable algorithms. Our solution is suitable for both research and applications in performance demanding systems such as encountered in streaming analytics, game development and high frequency trading.

Installation

Currently, we support only GNU, and CUDA for GPU (check WSL for Windows). A toolchain fully supporting C++17 is required. NOA is a header-only library, so you can directly drop the include/noa folder into your project.

CMake project

The core of the library depends on LibTorch Pre-cxx11 ABI (which is also distributed via pip and conda) tested with version 1.9.0. For additional configuration needed by some applications please refer to the documentation below.

We encourage you to work with conda. If your system supports CUDA, the environment env.yml contains all the required libraries:

$ conda env create -f env.yml
$ conda activate noa

For a CPU only installation please use env-cpu.yml instead.

Build tests & install the library (to turn testing off add -DBUILD_NOA_TESTS=OFF):

$ mkdir -p build && cd build
$ cmake .. -GNinja -DCMAKE_INSTALL_PREFIX=$CONDA_PREFIX
$ cmake --build . --target install
$ ctest -V

To build benchmarks specify -DBUILD_NOA_BENCHMARKS=ON. To enable parallel execution for some algorithms you should link against OpenMP. To build CUDA tests add -DBUILD_NOA_CUDA=ON and -DCMAKE_CUDA_ARCHITECTURES=75 (or the GPU architecture of your choice).

Finally, once NOA is installed, you can link against it in your own CMakeLists.txt file. Make sure to add LibTorch as well:

cmake_minimum_required(VERSION 3.12)
set(CMAKE_CXX_STANDARD 17)
find_package(Torch REQUIRED)
find_package(NOA CONFIG REQUIRED)
target_link_libraries(your_target torch NOA::NOA)
target_compile_options(your_target PRIVATE -Wall -Wextra -Wpedantic -O3)

KMath wrapper

NOA is exposed within the kotlin library KMath as a dedicated module kmath-noa. To build the JNI wrapper you need to add -DBUILD_JNOA=ON. This will produce the shared library jnoa to which you should point the java.library.path for the JVM to load it.

Applications

NOA offers several advanced applications for optimisation. Please refer to the documentation and usage examples for each component to find out more:

• GHMC focus on Bayesian optimisation with the Geometric HMC algorithms dedicated to sampling from higher-dimensional probability distributions.
• PMS provides a framework for solving inverse problems in the passage of particles through matter simulations.

Acknowledgments

We welcome contributions to the project and would love to hear about any feature requests.

The JNI wrapper is being developed in collaboration with KMath contributors.

For commercial support or consultancy services contact GrinisRIT.

(c) 2021 Roland Grinis, GrinisRIT ltd.