An intelligent block matrix library for numpy, PyTorch, and beyond. Crafted by Brandon Amos with significant contributions by Eric Wong.

Let's try to construct the KKT matrix from Mattingley and Boyd's CVXGEN paper in numpy and PyTorch:

Without block, there is no way to infer the appropriate sizes of the zero and identity matrix blocks. It is an inconvenience to think about what size these matrices should be.

Block acts a lot like np.bmat and replaces:

• Any constant with an appropriately shaped block matrix filled with that constant.
• The string 'I' with an appropriately shaped identity matrix.
• The string '-I' with an appropriately shaped negated identity matrix.
• [Request more features.]

Yes, block is meant to be a quick prototyping tool and there's probably a more efficient way to solve your system if it has a lot of zeros or identity elements.

I wrote the logic to handle matrix sizing to be agnostic of the matrix library being used. numpy and PyTorch are just backends. More backends can easily be added for your favorite Python matrix library.

class Backend(metaclass=ABCMeta):
    @abstractmethod
    def extract_shape(self, x): pass
    @abstractmethod
    def build_eye(self, n): pass
    @abstractmethod
    def build_full(self, shape, fill_val): pass
    @abstractmethod
    def build(self, rows): pass
    @abstractmethod
    def is_complete(self, rows): pass

• Install: pip install block
• Usage: from block import block
• Run tests in test.py: nosetests test.py

I'd be happy to hear from you about any issues or features you add, please file an issue or send in a PR.

This repository is Apache-licensed.