Sparse linear algebra solvers in SciPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the sparse linear solver from scipy.

SciPy

from scipy.sparse.linalg import [1]

Drag options to blanks, or click blank then click option'

Asolve

Bsolve_sparse

Csparse_solve

Dspsolve

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'solve' which is for dense matrices.

Trying to import a non-existent function like 'solve_sparse'.

The function spsolve is the correct sparse linear algebra solver in scipy.

2fill in blank

medium

Complete the code to create a sparse matrix in CSR format.

SciPy

from scipy.sparse import [1]

matrix = [1]((data, (row_indices, col_indices)), shape=(4, 4))

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Acsr_matrix

Bcsc_matrix

Ccoo_matrix

Ddia_matrix

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'csc_matrix' which is Compressed Sparse Column format.

Using 'coo_matrix' which is coordinate format.

The csr_matrix is the Compressed Sparse Row format, commonly used for efficient arithmetic and matrix vector operations.

3fill in blank

hard

Fix the error in solving a sparse linear system using spsolve.

SciPy

from scipy.sparse.linalg import spsolve
from scipy.sparse import csr_matrix

A = csr_matrix([[3, 0], [0, 4]])
b = [6, 8]
x = spsolve(A, [1])

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Ax

BA

Cb

D[6, 8]

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the matrix A instead of vector b.

Passing the solution variable x before it is defined.

The right-hand side vector b must be passed to spsolve to solve Ax = b.

4fill in blank

hard

Fill both blanks to create a sparse diagonal matrix and solve the system.

SciPy

from scipy.sparse import [1]
from scipy.sparse.linalg import [2]

D = [1]([1, 2, 3, 4])
b = [1, 4, 9, 16]
x = [2](D, b)

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Adiags

Bspsolve

Ccsr_matrix

Dsolve

Attempts:

3 left

💡 Hint

Common Mistakes

Using csr_matrix to create diagonal matrix without specifying diagonals.

Using solve which is for dense matrices.

diags creates a sparse diagonal matrix. spsolve solves the sparse linear system.

5fill in blank

hard

Fill all three blanks to create a sparse matrix, convert it to CSR format, and solve the system.

SciPy

from scipy.sparse import [1]
from scipy.sparse.linalg import [2]

coo = [1]((data, (rows, cols)), shape=(3, 3))
csr = coo.[3]()
b = [1, 2, 3]
x = [2](csr, b)

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Acoo_matrix

Bspsolve

Ctocsr

Dcsr_matrix

Attempts:

3 left

💡 Hint

Common Mistakes

Trying to solve directly with COO format without conversion.

Using csr_matrix constructor instead of conversion method.

coo_matrix creates a sparse matrix in coordinate format. tocsr() converts it to CSR format. spsolve solves the system.