Steepest Descent Method with Matrices

In summary, the Steepest Descent Method with Matrices is an optimization algorithm that uses the gradient vector and a matrix of second derivatives to find the minimum value of a function. It works by iteratively moving in the direction of steepest descent and has advantages such as simplicity and handling non-differentiable functions. However, it has limitations such as slow convergence rate and being prone to local minima. It differs from other optimization algorithms by using both the gradient vector and matrix of second derivatives, making it more accurate but less efficient.
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Homework Statement
Perform the steepest descent method with exact line search for the function f(x)=(1/2)(x^T)Qx+(q^T)x-B.
Relevant Equations
f(x)=(1/2)(x^T)Qx+(q^T)x-B
We are given f(x)=(1/2)(xT)Qx+qTx-B where xk+1=xkksk, the search direction is sk=-∇f(xk). Q is a 2x2 matrix and q is 2x1 matrix and B=6. My issue is I'm confused what -∇f(xk) is, is ∇f(xk)=Q(xk)-q? Just like how it is in Conjugate Gradient/Fletcher Reeve's method? Or is it Q(xk)+q?

Thank you
 
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[itex]\nabla f = \frac12(Q^T + Q)x + q[/itex], which is [itex]Qx + q[/itex] if [itex]Q[/itex] is symmetric.
 

FAQ: Steepest Descent Method with Matrices

What is the Steepest Descent Method with Matrices?

The Steepest Descent Method with Matrices is a numerical algorithm used to find the minimum value of a multivariable function. It involves using matrices to iteratively search for the minimum value by moving in the direction of steepest descent.

How does the Steepest Descent Method with Matrices work?

The method starts with an initial guess for the minimum value and then calculates the gradient of the function at that point. This gradient is then multiplied by a step size and subtracted from the initial guess to get a new point. This process is repeated until the minimum value is found.

What are the advantages of using the Steepest Descent Method with Matrices?

The method is relatively simple to implement and does not require any special knowledge of the function being minimized. It also works well for functions with multiple local minima, as it can find the global minimum. Additionally, it can handle functions with a large number of variables.

What are the limitations of the Steepest Descent Method with Matrices?

One limitation is that it can be slow to converge, especially for functions with a large number of variables. It also requires the function to be differentiable, which may not always be the case. Additionally, the step size can greatly affect the accuracy of the results.

How is the Steepest Descent Method with Matrices used in real-world applications?

The method is commonly used in optimization problems, such as in engineering and machine learning. It can be used to find the optimal values for parameters in a model or to minimize error in a system. It has also been used in image processing, signal processing, and other fields where optimization is necessary.

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