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Interactive 3D/Sparse Matrix Methods
Matrix Size
6×6 = 36 cells
4×416×16
Sparsity
70% zerosnnz=0
Animate Matvec
Memory
nnz / total:0 / 36
Sparsity:100.0%
Dense bytes:144
CSR bytes:28
Memory ratio:5.1×
Dense vs CSR storage
DenseCSR
CSR format: 3 arrays - values (nonzeros), column indices, row pointers. Matvec only touches nnz entries. At 90% sparsity, CSR uses 10× less memory than dense.

Sparse Matrix Methods - Interactive Visualization

Most matrices in large-scale ML are sparse - adjacency matrices, graph Laplacians, NLP document-term matrices. Storing them in dense format wastes memory; sparse formats (CSR/CSC) store only non-zero values. This visualization shows the dense matrix and its CSR representation simultaneously, animating a matrix-vector multiply to show which entries are accessed.

  • Adjust sparsity slider to see entries zero out
  • See CSR storage: values, column indices, row pointers arrays
  • Watch matrix-vector multiply animate, skipping zero entries
  • Compare memory for dense (n²) vs CSR (2·nnz + n) storage
  • Foundation for sparse neural networks, graph convolutions, and large-scale ML

Part of the EngineersOfAI Interactive 3D - free interactive visualizations covering every major concept in machine learning and AI engineering. Hover any element for a plain-English explanation. No code required.