dy/dx - = 1 - y,y(0) = 0 is an example of

[zaasmi]

@zaasmi said in MTH603 Mid Term Past and Current Solved Paper Discussion:

The determinant of a diagonal matrix is the product of the diagonal elements.
True
False

True

Explanation:

• For a diagonal matrix, all off-diagonal elements are zero. The determinant of a diagonal matrix is calculated as the product of its diagonal elements.

For example, if (D) is a diagonal matrix with diagonal elements (d_1, d_2, \ldots, d_n), then:

[ \text{det}(D) = d_1 \cdot d_2 \cdot \ldots \cdot d_n ]

So, the determinant of a diagonal matrix is indeed the product of its diagonal elements.

Therefore, the statement is:

True

[zaasmi]

The Gauss-Seidel method is applicable to strictly diagonally dominant or symmetric positive definite
matrices A.
True
False

[zaasmi]

@zaasmi said in MTH603 Mid Term Past and Current Solved Paper Discussion:

The Gauss-Seidel method is applicable to strictly diagonally dominant or symmetric positive definite
matrices A.
True
False

True

Explanation:

• Gauss-Seidel Method: This iterative method for solving linear systems converges under certain conditions.

• Strictly Diagonally Dominant Matrices: If a matrix is strictly diagonally dominant (i.e., for each row of the matrix, the magnitude of the diagonal element is greater than the sum of the magnitudes of the other elements in that row), the Gauss-Seidel method will converge.

• Symmetric Positive Definite Matrices: For symmetric positive definite matrices, the Gauss-Seidel method is guaranteed to converge.

Therefore, the Gauss-Seidel method is indeed applicable and convergent for strictly diagonally dominant or symmetric positive definite matrices.

So, the correct statement is:

True

[zaasmi]

The determinant of a _______ matrix is the product of the diagonal elements.

1. Upper triangular
2. Lower triangular
3. Scalar
4. Diagonal

[zaasmi]

@zaasmi said in MTH603 Mid Term Past and Current Solved Paper Discussion:

The determinant of a _______ matrix is the product of the diagonal elements.

1. Upper triangular
2. Lower triangular
3. Scalar
4. Diagonal

The determinant of a matrix is the product of the diagonal elements if the matrix is:

Diagonal, Upper Triangular, or Lower Triangular

Explanation:

• Diagonal Matrix: All off-diagonal elements are zero. The determinant is the product of the diagonal elements.

• Upper Triangular Matrix: All elements below the main diagonal are zero. The determinant is the product of the diagonal elements.

• Lower Triangular Matrix: All elements above the main diagonal are zero. The determinant is the product of the diagonal elements.

• Scalar Matrix: A diagonal matrix where all diagonal elements are the same scalar value. The determinant is still the product of the diagonal elements.

Therefore, the correct options are:

Diagonal, Upper Triangular, Lower Triangular

[zaasmi]

For differences methods we require the set of values.
True
False

[zaasmi]

@zaasmi said in MTH603 Mid Term Past and Current Solved Paper Discussion:

For differences methods we require the set of values.
True
False

True

Explanation:

• Difference Methods: These methods, such as finite difference methods, require a set of discrete values to approximate derivatives or solve differential equations.

• For example, in numerical differentiation, you need discrete data points (or values) to estimate the derivatives using forward, backward, or central differences. Similarly, in finite difference methods for solving partial differential equations, a grid of discrete values is used to approximate solutions.

Thus, the statement that difference methods require a set of values is indeed correct.

So, the correct answer is:

True

[zaasmi]

If x is an eigen value corresponding to eigen value of V of a matrix A. If a is any constant, then x – a is an eigen value corresponding to eigen vector V is an of the matrix A - a I.
True
False

[zaasmi]

@zaasmi said in MTH603 Mid Term Past and Current Solved Paper Discussion:

If x is an eigen value corresponding to eigen value of V of a matrix A. If a is any constant, then x – a is an eigen value corresponding to eigen vector V is an of the matrix A - a I.
True
False

True

Explanation:

• If ( x ) is an eigenvalue of a matrix ( A ) corresponding to an eigenvector ( V ), this means:
  [ A V = x V ]

• If ( a ) is any constant, then ( x - a ) will be an eigenvalue of the matrix ( A - aI ), where ( I ) is the identity matrix.

• To see why, consider:
  [ (A - aI) V = A V - a I V ]
  [ = x V - a V ]
  [ = (x - a) V ]

• This shows that ( V ) is still an eigenvector of ( A - aI ), but now with the eigenvalue ( x - a ).

So, the statement is:

True

[zaasmi]

Central difference method seems to be giving a better approximation, however it requires more computations.

True
False

[zaasmi]

@zaasmi said in MTH603 Mid Term Past and Current Solved Paper Discussion:

Central difference method seems to be giving a better approximation, however it requires more computations.

True
False

True

Explanation:

• Central Difference Method: This method approximates derivatives by averaging the forward and backward differences:
  [ f’(x) \approx \frac{f(x + h) - f(x - h)}{2h} ]

• Accuracy: The central difference method is often more accurate than the forward or backward difference methods because it uses information from both sides of the point where the derivative is being approximated. It has a smaller truncation error and provides a better approximation to the derivative.

• Computations: While it is more accurate, the central difference method requires evaluating the function at two points (both ( x + h ) and ( x - h )), as opposed to just one point for forward or backward differences. This requires more function evaluations and, therefore, more computational effort.

So, the statement that the central difference method gives a better approximation but requires more computations is:

True

[zaasmi]

Iterative algorithms can be more rapid than direct methods.
True
False

[zaasmi]

@zaasmi said in MTH603 Mid Term Past and Current Solved Paper Discussion:

Iterative algorithms can be more rapid than direct methods.
True
False

True

Explanation:

• Iterative Algorithms: These methods, such as Jacobi, Gauss-Seidel, and Conjugate Gradient, are often used for solving large systems of linear equations, especially when the matrix is sparse. They can be more efficient in terms of memory and computation time for very large problems.

• Direct Methods: These methods, such as Gaussian elimination or LU decomposition, provide exact solutions (within rounding errors) but can be computationally intensive and require significant memory, especially for large systems.

• Efficiency: For large-scale problems, iterative methods can converge more quickly to an approximate solution and are often preferred due to their lower computational complexity and memory requirements compared to direct methods.

Therefore, iterative algorithms can indeed be more rapid than direct methods in certain cases.

So, the statement is:

True

[zaasmi]

Central Difference method is the finite difference method.
True
False

[zaasmi]

@zaasmi said in MTH603 Mid Term Past and Current Solved Paper Discussion:

Central Difference method is the finite difference method.
True
False

True

Explanation:

• Central Difference Method: This method is a type of finite difference method used for approximating derivatives. It estimates the derivative by considering the average of the forward and backward differences:
  [ f’(x) \approx \frac{f(x + h) - f(x - h)}{2h} ]

• Finite Difference Methods: These methods are used to approximate derivatives and solve differential equations by using discrete points. The central difference method is one of these approaches, specifically designed to improve the accuracy of derivative approximations.

Thus, the central difference method is indeed a finite difference method.

So, the statement is:

True

[zaasmi]

Back substitution procedure is used in …
Select correct option:
Gaussian Elimination Method
Jacobi’s method
Gauss-Seidel method
None of the given choices