A linear transformation is a function that preserves linear operations, such as addition and scalar multiplication. In this case, f(x) = 2x + 1 is a linear transformation because f(x + y) = 2(x + y) + 1 = 2x + 2y + 1 = f(x) + f(y) and f(cx) = 2(cx) + 1 = c(2x + 1) = cf(x) for any scalars c and vectors x and y.

The matrix representation of a linear transformation is the matrix that performs the same transformation when multiplied by a column vector. In this case, the matrix representation of f(x) = 2x - 1 is [[2, -1], [0, 1]] because [2, -1][x] = 2x - 1 and [0, 1][y] = y for any scalars x and y.

An eigenvalue of a linear transformation is a scalar λ such that there exists a nonzero vector x such that f(x) = λx. In this case, f(3) = 3(3) - 2 = 7 and 3 is a nonzero scalar, so 3 is an eigenvalue of f(x) = 3x - 2.

The kernel of a linear transformation is the set of all vectors that are mapped to the zero vector. In this case, the kernel of f(x) = Ax is the set of all vectors x such that Ax = 0.

Linear transformations preserve linear operations, such as addition and scalar multiplication. This means that f(x + y) = f(x) + f(y) and f(cx) = cf(x) for any scalars c and vectors x and y.

The range of a linear transformation is the set of all vectors that can be obtained by applying the transformation to some vector in the domain. In this case, the range of f(x) = [x, 2x, 3x] is the set of all vectors of the form [x, 2x, 3x], which is a one-dimensional subspace of R^3. Therefore, the dimension of the range is 1.

A linear transformation is invertible if there exists a linear transformation g(x) such that f(g(x)) = g(f(x)) = x for all vectors x in the domain. In this case, f(x) = x^2 is not invertible because there is no linear transformation g(x) such that f(g(x)) = g(f(x)) = x for all vectors x in the domain.

The matrix representation of a linear transformation is the matrix that performs the same transformation when multiplied by a column vector. In this case, the matrix representation of f(x) = [x1, x2, x3] is [[1, 0, 0], [0, 1, 0], [0, 0, 1]] because [[1, 0, 0], [0, 1, 0], [0, 0, 1]][x1, x2, x3]^T = [x1, x2, x3]^T for any vector [x1, x2, x3]^T.

A linear transformation is diagonalizable if there exists a basis of eigenvectors for the transformation. In this case, f(x) = 2x + 1 is diagonalizable because the eigenvectors of f(x) are the vectors [1, 0] and [0, 1], which form a basis for R^2.

The determinant of the matrix representation of a linear transformation is equal to the determinant of the matrix that performs the same transformation when multiplied by a column vector. In this case, the matrix representation of f(x) = [x1 + x2, x2 + x3, x3 + x1] is [[1, 1, 0], [0, 1, 1], [1, 0, 1]], and the determinant of this matrix is 2.

A linear transformation is surjective if for every vector y in the codomain, there exists a vector x in the domain such that f(x) = y. In this case, f(x) = x^2 is not surjective because there is no vector x in the domain such that f(x) = -1.

The nullity of a linear transformation is the dimension of the kernel of the transformation. In this case, the kernel of f(x) = [x1, x2, x3] is the set of all vectors [x1, x2, x3] such that x1 = x2 = x3 = 0, which is a zero-dimensional subspace of R^3. Therefore, the nullity of f(x) is 0.

A linear transformation is one-to-one if for every two distinct vectors x and y in the domain, f(x) ≠ f(y). In this case, f(x) = x^2 is not one-to-one because f(-1) = f(1) = 1.

The rank of a linear transformation is the dimension of the range of the transformation. In this case, the range of f(x) = [x1 + x2, x2 + x3, x3 + x1] is the set of all vectors [x1 + x2, x2 + x3, x3 + x1], which is a two-dimensional subspace of R^3. Therefore, the rank of f(x) is 2.