Sparse Matrices

December 30, 2025 ยท View on GitHub

Learn how to work with sparse matrices efficiently in VSL.

What You'll Learn

  • Understanding sparse matrices
  • When to use sparse storage
  • Sparse matrix operations
  • Storage formats

Prerequisites

Theory

Sparse matrices have mostly zero entries. Storing only non-zero values saves memory and computation.

Storage Formats

Coordinate Format (COO)

Store (row, column, value) triplets.

Compressed Sparse Row (CSR)

Efficient row access.

Compressed Sparse Column (CSC)

Efficient column access.

When to Use Sparse

  • Matrix is >90% zeros
  • Large matrices (>1000x1000)
  • Memory is limited
  • Many zero operations can be skipped

Operations

Sparse matrix operations are optimized to skip zero entries, making them faster for sparse data.

Next Steps