Why linear algebra is MATLAB's core - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a 2x2 identity matrix.

MATLAB

I = eye([1]);

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A3

B1

C4

D2

Attempts:

3 left

💡 Hint

Common Mistakes

Using a size other than 2 will create a matrix of wrong dimensions.

Forgetting that the identity matrix is square.

The eye function creates an identity matrix of the specified size. Here, eye(2) creates a 2x2 identity matrix.

2fill in blank

medium

Complete the code to multiply two matrices A and B.

MATLAB

C = A [1] B;

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A+

B.*

C*

D-

Attempts:

3 left

💡 Hint

Common Mistakes

Using .* instead of * for matrix multiplication.

Using addition or subtraction operators by mistake.

Matrix multiplication in MATLAB uses the * operator. The .* operator does element-wise multiplication, which is different.

3fill in blank

hard

Fix the error in the code to compute the inverse of matrix M.

MATLAB

invM = [1](M);

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Ainv

Bdet

Ctranspose

Drank

Attempts:

3 left

💡 Hint

Common Mistakes

Using transpose instead of inv.

Confusing determinant with inverse.

The inv function computes the inverse of a matrix. Other functions like transpose, det, and rank do different operations.

4fill in blank

hard

Fill both blanks to create a vector v with elements from 1 to 5 and compute its norm.

MATLAB

v = [1];
norm_v = [2](v);

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A1:5

Bnorm

Csum

Dlinspace

Attempts:

3 left

💡 Hint

Common Mistakes

Using linspace without correct arguments.

Using sum instead of norm.

1:5 creates a vector with elements 1 to 5. The norm function computes the length (magnitude) of the vector.

5fill in blank

hard

Fill all three blanks to create a matrix A, compute its transpose, and then multiply A by its transpose.

MATLAB

A = [1];
At = [2](A);
result = A [3] At;

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A[1 2 3]

Btranspose

C*

Dsum

Attempts:

3 left

💡 Hint

Common Mistakes

Using sum instead of multiplication.

Using element-wise multiplication .* instead of *.

[1 2 3] creates a row vector with elements 1 to 3. transpose flips rows and columns. Multiplying A by its transpose uses * for matrix multiplication.