ndsparse
Structures to store and retrieve N-dimensional sparse data. Well, not any N ∈ ℕ
but any natural number that fits into the pointer size of the machine that you are using. E.g., an 8-bit microcontroller can manipulate any sparse structure with up to 255 dimensions.
For those that might be wondering about why this crate should be used, it generally comes down to space-efficiency, ergometrics and retrieving speed. The following snippet shows some use-cases for potential replacement with _cube_of_vecs
being the most inefficient of all.
let _vec_of_options: = Default default;
let _matrix_of_options: = Default default;
let _cube_of_vecs: = Default default;
// The list worsens exponentially for higher dimensions
See this blog post for more information.
Example
use ;
Supported structures
- Compressed Sparse Line (CSL)
- Coordinate format (COO)
Optional features
alloc
- Constant generics
- Bindings (Py03, wasm-bindgen)
- Deserialization/Serialization (serde)
- Dynamic arrays (ArrayVec, SmallVec and StaticVec)
- Parallel iterators (rayon)
- Random instances (rand)
Nightly compiler
If dimensions or array storages with more than 32 elements are needed, then it is necessary to include the const_generics
feature that is only available when using a nightly Rustc compiler.
Future
Although CSR and COO are general sparse structures, they aren't good enough for certain situations, threfore, the existence of DIA, JDS, ELL, LIL, DOK and many others.
If there are enough interest, the mentioned sparse storages might be added at some point in the future.
Algebra library
This project isn't and will never be a sparse algebra library because of its own self-contained responsability and complexity. Futhermore, a good implementation of such library would require a titanic amout of work and research for different algorithms, operations, decompositions, solvers and hardwares.
Alternatives
One of these libraries might suit you better: