Sparse Matrix Lab
Encode sparse matrices with classic formats and compare compression trade-offs.
Many real-world matrices are mostly zeros. Storing every zero wastes memory and slows computation.
Sparse formats store only the important entries and their positions. This can reduce storage and speed up linear algebra.
COO: explicit (row, col, value) triples.
CSR / CSC: compact row-wise or column-wise indexing for fast traversal.
RLE: run-length encoding for rows with long stretches of zeros.
Dictionary / Bitmap: lightweight encodings for fast lookup or compact bit patterns.
- Generate or edit a matrix.
- Select an encoding format and click Encode.
- Reconstruct to verify correctness and compare sizes.
Matrix Generator
Current: 10%
Encoding
Summary
Dimensions: -
Non-zero count: -
Raw size (dense): - bytes
Compressed size (est.): - bytes
Compression ratio: -
Reconstruction: -
Original Matrix (click to edit)
Click a cell to edit. Empty values become zero.
Encoded Result
Matrix JSON
Notes
- COO is simple but not cache-friendly for large matrices.
- CSR is fast for row slicing; CSC is fast for column slicing.
- RLE is best when long runs of zeros appear in rows.
- Bitmap works well for structural sparsity, but values still need storage.