But the world has changed. Fortran and C have given way to Python as the lingua franca of scientific computing. So where does that leave Numerical Recipes today?
These libraries are:
| NR Classic Topic | Modern Python Solution | |----------------|------------------------| | Linear algebra | numpy.linalg / scipy.linalg | | FFTs | numpy.fft | | ODE integrators | scipy.integrate (e.g., solve_ivp ) | | Random numbers | numpy.random (PCG64, MT19937) | | Optimization | scipy.optimize | | Interpolation | scipy.interpolate | | Special functions | scipy.special | numerical recipes python
Implementing "numerical recipes" involves using algorithms to solve mathematical problems that are too complex for analytical solutions. The main areas of interest typically include: Numerical Recipes In C++ - sciphilconf.berkeley.edu But the world has changed