Performance Tips

Best practices for sparse matrix performance.

1. Convert Once

from scipy import sparse

# Bad: repeated conversion
for i in range(100):
    csr = coo.tocsr()
    result = csr @ x

# Good: convert once
csr = coo.tocsr()
for i in range(100):
    result = csr @ x

2. Use Right Format

Operation	Best Format
A @ x	CSR
A[:, j]	CSC
A[i, :]	CSR
Build	LIL, COO

3. Avoid Element Access

# Bad: element-by-element
for i in range(n):
    for j in range(n):
        A[i, j] = func(i, j)

# Good: build with COO
rows, cols, data = [], [], []
for i in range(n):
    for j in range(n):
        if (val := func(i, j)) != 0:
            rows.append(i)
            cols.append(j)
            data.append(val)
A = sparse.coo_matrix((data, (rows, cols)))

4. Preallocate When Possible

Use lil_matrix for incremental builds, then convert.

5. Monitor Fill-in

Matrix products can dramatically increase density.