#OccamyPy, an object-oriented optimization framework for large-scale inverse problems
We present a python library that can be employed to solve small- and large-scale problems based on the concept of vectors and operators.
Based on NumPy, CuPy and PyTorch, it includes different iterative optimization algorithms that can be used in combination with architecture-independent vectors and operators, thus running on CPU, GPUs and HPC clusters with a unique codebase.
Inspired by PyLops, we demonstrate its flexibility and scalability on multiple inverse problems, where convex and non-convex objective functions are optimized with different iterative algorithms.
- Francesco Picetti - Image and Sound Processing Lab, Politecnico di Milano
- Ettore Biondi - SeismoLab, California Institute of Technology
#What you’ll need
- Slack channel: #t22-tue-occamypy
- Pre-filled notebooks and data on Dropbox (simply download the whole folder)
- A python virtual environment defined here