Steepest Descent Method with Matrices

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The discussion revolves around the steepest descent method for the function f(x) defined with a quadratic form involving a matrix Q and vector q. The user seeks clarification on the gradient, specifically whether -∇f(xk) is equal to Q(xk) - q or Q(xk) + q. It is confirmed that the gradient ∇f(x) is given by (1/2)(Q^T + Q)x + q, simplifying to Qx + q if Q is symmetric. This clarification resolves the user's confusion regarding the correct expression for the search direction in the steepest descent method. Understanding the gradient's formulation is crucial for applying the method correctly.
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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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\nabla f = \frac12(Q^T + Q)x + q, which is Qx + q if Q is symmetric.
 
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