I have a multivariable function that I have defined the cost function and its gradient...

Gaaljh

Gaaljh

Answered

2022-06-25

I have a multivariable function that I have defined the cost function and its gradient with respect to the variable vector. Let's call the cost function f ( x ), and variable vector x . I have been using nonlinear conjugate gradient descenet method for minimization. Algorithm is as follows:
k = 0 x = 0 g 0 = x f ( x 0 ) Δ x 0 = g 0
x k + 1 x k + t Δ x k g k + 1 f ( x k + 1 ) Δ x k + 1 g k + 1 + γ Δ x k
I know that I need to update formula so the solution "ascends" instead of descension. How can I update the algorithm? Also I wonder how the wolfe condition changes for maximization problems. Thank you and have a nice day.

Answer & Explanation

Myla Pierce

Myla Pierce

Expert

2022-06-26Added 20 answers

Convert your function from maximization problem to minimization by setting f n e w ( x ) = f ( x ) and run default conjugate gradient for minimization.

Since you had specifically asked how the formulas and wolfe conditions update you can plug in f ( x ) for function values and f ( x ) for gradients and see for yourself.

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