numericalalgorithmsgroup/NAGPythonLibraryTraining

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numericalalgorithmsgroup/NAGPythonLibraryTraining

numericalalgorithmsgroup

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Hands on exercises and solutions for training in the NAG Library for Python

pybobyqa

Python-based Derivative-Free Optimization with Bound Constraints

NAGPythonExamples

Examples and demos showing how to call functions from the NAG Library for Python

Jupyter Notebook

LAPACK_Examples

Example programs showing how to call LAPACK driver and computational routines (Fortran double precision)

dfols

Python-based Derivative-Free Optimizer for Least-Squares

dfogn

DFO-GN: Derivative-Free Optimization using Gauss-Newton

nagcpp

C++ Interfaces for the NAG Library

2decomp_fft

pypop

Python Tools for the POP Metrics

Jupyter Notebook

Ski-LLS

dfbgn

Python solver for large-scale nonlinear least-squares minimization without derivatives

HighPerformancePythonTraining

Jupyter Notebook

AlgorithmicDifferentiation

NAGJavaExamples

Examples demonstrating the NAG Numerical Library for Java

robinhood-reports

A set of reports for presenting data collected by the Robinhood Policy Engine

AzureML_Best_Practice

Reference Implementations of AzureML Workloads with Cost/Performance Best Practice

Jupyter Notebook

dco_cpp

MLFirstSteps_Azure

Tutorial demonstrating how to get started porting an existing Machine Learning application to MS Azure

bayesian-uq

NAGPython_PyQ

Using the NAG Library for Python with Kdb+ and PyQ

The-Role-of-Matrix-Functions

A Jupyter Notebook about using matrix functions with the NAG Library for Python

Jupyter Notebook

kdb_ffi

performance_profiling_examples

Accompanying resources for the NAG webinar and blog series